Title: | Analysis of Ecological Data: Exploratory and Euclidean Methods in Environmental Sciences |
---|---|
Description: | Tools for multivariate data analysis. Several methods are provided for the analysis (i.e., ordination) of one-table (e.g., principal component analysis, correspondence analysis), two-table (e.g., coinertia analysis, redundancy analysis), three-table (e.g., RLQ analysis) and K-table (e.g., STATIS, multiple coinertia analysis). The philosophy of the package is described in Dray and Dufour (2007) <doi:10.18637/jss.v022.i04>. |
Authors: | Stéphane Dray [aut] |
Maintainer: | Aurélie Siberchicot <[email protected]> |
License: | GPL (>= 2) |
Version: | 1.7-23 |
Built: | 2024-09-06 14:16:27 UTC |
Source: | https://github.com/sdray/ade4 |
This package is developed in the Biometry and Evolutionary Biology Lab (UMR CNRS 5558) - University Lyon 1.
It contains Data Analysis functions to analyse Ecological and Environmental data in the framework of Euclidean Exploratory methods, hence the name ade4.
ade4 is characterized by (1) the implementation of graphical and statistical functions, (2) the availability of numerical data, (3) the redaction of technical and thematic documentation and (4) the inclusion of bibliographic references.
To cite ade4, please use citation("ade4")
.
Stéphane Dray, Anne-Béatrice Dufour, and Jean Thioulouse. Contributions from Daniel Borcard, Stéphanie Bougeard, Thibaut Jombart, Pierre Legendre, Jean R. Lobry, Sébastien Ollier, Sandrine Pavoine and Aurélie Siberchicot. Based on earlier work by Daniel Chessel.
Dray S and Dufour A (2007). “The ade4 Package: Implementing the Duality Diagram for Ecologists.” _Journal of Statistical Software_, *22*(4), pp. 1-20. doi: 10.18637/jss.v022.i04 (URL: http://doi.org/10.18637/jss.v022.i04).
See ade4 website: http://pbil.univ-lyon1.fr/ADE-4/
ade4TkGUI
, adegenet
, adehabitat
, adegraphics
This data set gathers three phylogenies with three sets of traits as reported by Abouheif (1999).
data(abouheif.eg)
data(abouheif.eg)
abouheif.eg
is a list containing the 6 following objects :
is a character string giving the first phylogenetic tree made up of 8 leaves.
is a numeric vector with 8 values.
is a character string giving the second phylogenetic tree made up of 7 leaves.
is a numeric vector with 7 values.
is a character string giving the third phylogenetic tree made up of 15 leaves.
is a numeric vector with 15 values.
Data taken from the phylogenetic independence program developed by Ehab Abouheif
Abouheif, E. (1999) A method for testing the assumption of phylogenetic independence in comparative data. Evolutionary Ecology Research, 1, 895–909.
data(abouheif.eg) par(mfrow=c(2,2)) symbols.phylog(newick2phylog(abouheif.eg$tre1), abouheif.eg$vec1, sub = "Body Mass (kg)", csi = 2, csub = 2) symbols.phylog(newick2phylog(abouheif.eg$tre2), abouheif.eg$vec2, sub = "Body Mass (kg)", csi = 2, csub = 2) dotchart.phylog(newick2phylog(abouheif.eg$tre1), abouheif.eg$vec1, sub = "Body Mass (kg)", cdot = 2, cnod = 1, possub = "topleft", csub = 2, ceti = 1.5) dotchart.phylog(newick2phylog(abouheif.eg$tre2), abouheif.eg$vec2, sub = "Body Mass (kg)", cdot = 2, cnod = 1, possub = "topleft", csub = 2, ceti = 1.5) par(mfrow = c(1,1)) w.phy=newick2phylog(abouheif.eg$tre3) dotchart.phylog(w.phy,abouheif.eg$vec3, clabel.n = 1)
data(abouheif.eg) par(mfrow=c(2,2)) symbols.phylog(newick2phylog(abouheif.eg$tre1), abouheif.eg$vec1, sub = "Body Mass (kg)", csi = 2, csub = 2) symbols.phylog(newick2phylog(abouheif.eg$tre2), abouheif.eg$vec2, sub = "Body Mass (kg)", csi = 2, csub = 2) dotchart.phylog(newick2phylog(abouheif.eg$tre1), abouheif.eg$vec1, sub = "Body Mass (kg)", cdot = 2, cnod = 1, possub = "topleft", csub = 2, ceti = 1.5) dotchart.phylog(newick2phylog(abouheif.eg$tre2), abouheif.eg$vec2, sub = "Body Mass (kg)", cdot = 2, cnod = 1, possub = "topleft", csub = 2, ceti = 1.5) par(mfrow = c(1,1)) w.phy=newick2phylog(abouheif.eg$tre3) dotchart.phylog(w.phy,abouheif.eg$vec3, clabel.n = 1)
Counts of individuals of Acacia ehrenbergiana from five parallel transects of 32 quadrats.
data(acacia)
data(acacia)
acacia
is a data frame with 15 variables :
se.T1, se.T2, se.T3, se.T4, se.T5 are five numeric vectors containing quadrats counts of
seedlings from transects 1 to 5 respectively;
sm.T1, sm.T2, sm.T3, sm.T4, sm.T5 are five numeric vectors containing quadrats counts of
small trees (crown < 1 in canopy) of transects 1 to 5 respectively;
la.T1, la.T2, la.T3, la.T4, la.T5 are five numeric vectors containing quadrats counts of
trees with large crown (crown > 1 in canopy) of transects 1 to 5 respectively.
Greig-Smith, P. and Chadwick, M.J. (1965) Data on pattern within plant communities. III. Acacia-Capparis semi-desert scrub in the Sudan. Journal of Ecology, 53, 465–474.
Hill, M.O. (1973) The intensity of spatial pattern in plant communities. Journal of Ecology, 61, 225–235.
data(acacia) if(adegraphicsLoaded()) { gg <- s1d.barchart(acacia, p1d.horizontal = FALSE, psub.position = "topleft", plabels.cex = 0, ylim = c(0,20)) } else { par(mfcol = c(5, 3)) par(mar = c(2, 2, 2, 2)) for(k in 1:15) { barplot(acacia[, k], ylim = c(0, 20), col = grey(0.8)) ade4:::scatterutil.sub(names(acacia)[k], 1.5, "topleft") } par(mfcol = c(1, 1)) }
data(acacia) if(adegraphicsLoaded()) { gg <- s1d.barchart(acacia, p1d.horizontal = FALSE, psub.position = "topleft", plabels.cex = 0, ylim = c(0,20)) } else { par(mfcol = c(5, 3)) par(mar = c(2, 2, 2, 2)) for(k in 1:15) { barplot(acacia[, k], ylim = c(0, 20), col = grey(0.8)) ade4:::scatterutil.sub(names(acacia)[k], 1.5, "topleft") } par(mfcol = c(1, 1)) }
add.scatter
is a function which defines a new plot area within an existing plot and displays an additional graphic inside this area. The additional graphic is determined by a function which is the first argument taken by add.scatter
. It can be used in various ways, for instance to add a screeplot to an ordination scatterplot (add.scatter.eig
).
The function add.scatter.eig
uses the following colors: black (represented axes), grey(axes retained in the analysis) and white (others).
add.scatter(func,posi = c("bottomleft","bottomright","topleft","topright"), ratio = 0.2, inset = 0.01, bg.col = 'white') add.scatter.eig(w, nf = NULL, xax, yax, posi = "bottomleft", ratio = .25, inset = 0.01, sub = "Eigenvalues", csub = 2 * ratio)
add.scatter(func,posi = c("bottomleft","bottomright","topleft","topright"), ratio = 0.2, inset = 0.01, bg.col = 'white') add.scatter.eig(w, nf = NULL, xax, yax, posi = "bottomleft", ratio = .25, inset = 0.01, sub = "Eigenvalues", csub = 2 * ratio)
func |
an - evaluated - function producing a graphic |
posi |
a character vector (only its first element being considered) giving the position of the added graph. Possible values are "bottomleft" (="bottom"),"bottomright","topleft" (="top"),"topright", and "none" (no plot). |
ratio |
the size of the added graph in proportion of the current plot region |
inset |
the inset from which the graph is drawn, in proportion of the whole plot region. Can be a vector of length 2, giving the inset in x and y. If atomic, same inset is used in x and y |
bg.col |
the color of the background of the added graph |
w |
numeric vector of eigenvalues |
nf |
the number of retained factors, NULL if not provided |
xax |
first represented axis |
yax |
second represented axis |
sub |
title of the screeplot |
csub |
size of the screeplot title |
add.scatter
uses par("plt")
to redefine the new plot region.
As stated in par
documentation, this produces to (sometimes
surprising) interactions with other parameters such as "mar".
In particular, such interactions are likely to reset the plot region
by default which would cause the additional graphic to take the whole
plot region. To avoid such inconvenient, add par([other
options], plt=par("plt"))
when using par
in your graphical
function (argument func
).
The matched call (invisible).
Thibaut Jombart [email protected]
data(microsatt) w <- dudi.coa(data.frame(t(microsatt$tab)), scann = FALSE, nf = 3) if(adegraphicsLoaded()) { a1 <- rnorm(100) b1 <- s1d.barchart(sort(a1), p1d.horizontal = FALSE, plot = FALSE) h1 <- s1d.hist(a1, pgrid.draw = FALSE, porigin.draw = FALSE, pbackground.col = "grey", plot = FALSE, ppoly.col = "white", ppoly.alpha = 1) g1 <- insert(h1, b1, posi = "topleft", plot = FALSE) a2 <- rnorm(100) b2 <- s1d.barchart(sort(a2), p1d.horizontal = FALSE, plot = FALSE) h2 <- s1d.hist(a2, pgrid.draw = FALSE, porigin.draw = FALSE, pbackground.col = "grey", plot = FALSE, ppoly.col = "white", ppoly.alpha = 1) g2 <- insert(h2, b2, posi = "topleft", inset = c(0.25, 0.01), plot = FALSE) a3 <- rnorm(100) b3 <- s1d.barchart(sort(a3), p1d.horizontal = FALSE, plot = FALSE) h3 <- s1d.hist(a3, pgrid.draw = FALSE, porigin.draw = FALSE, pbackground.col = "grey", plot = FALSE, ppoly.col = "white", ppoly.alpha = 1) g3 <- insert(h3, b3, posi = "bottomleft", inset = 0.4, ratio = 0.2, plot = FALSE) a4 <- rnorm(100) b4 <- s1d.barchart(sort(a4), p1d.horizontal = FALSE, plot = FALSE) h4 <- s1d.hist(a4, pgrid.draw = FALSE, porigin.draw = FALSE, pbackground.col = "grey", plot = FALSE, ppoly.col = "white", ppoly.alpha = 1) g4 <- insert(h3, b3, posi = "bottomright", ratio = 0.3, plot = FALSE) G1 <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2), plot = TRUE) g5 <- s.label(w$co, plot = FALSE) g6 <- plotEig(w$eig, w$nf, psub = list(text = "Eigenvalues"), pbackground = list(box = TRUE), plot = FALSE) G2 <- insert(g6, g5, posi = "bottomright", ratio = 0.25) } else { par(mfrow=c(2,2)) f1 <- function(a){ opar=par("mar","xaxt","yaxt","plt") on.exit(par(opar)) par(mar=rep(.1,4),xaxt="n",yaxt="n",plt=par("plt")) hist(a,xlab="",ylab="",main="",col="white",proba=TRUE) lines(seq(-4,4,le=50),dnorm(seq(-4,4,le=50)),col="red") } a <- rnorm(100) barplot(sort(a)) add.scatter(f1(a),posi="topleft",bg.col="grey") a <- rnorm(100) barplot(sort(a)) add.scatter(f1(a),posi="topleft",bg.col="grey",inset=c(.25,.01)) a <- rnorm(100) barplot(sort(a)) add.scatter(f1(a),posi="topleft",bg.col="grey",inset=.25,ratio=.1) a <- rnorm(100) barplot(sort(a)) add.scatter(f1(a),posi="bottomright",bg.col="grey",ratio=.3) par(mfrow=c(1,1)) s.label(w$co) add.scatter.eig(w$eig,w$nf,posi="bottomright",1,2) }
data(microsatt) w <- dudi.coa(data.frame(t(microsatt$tab)), scann = FALSE, nf = 3) if(adegraphicsLoaded()) { a1 <- rnorm(100) b1 <- s1d.barchart(sort(a1), p1d.horizontal = FALSE, plot = FALSE) h1 <- s1d.hist(a1, pgrid.draw = FALSE, porigin.draw = FALSE, pbackground.col = "grey", plot = FALSE, ppoly.col = "white", ppoly.alpha = 1) g1 <- insert(h1, b1, posi = "topleft", plot = FALSE) a2 <- rnorm(100) b2 <- s1d.barchart(sort(a2), p1d.horizontal = FALSE, plot = FALSE) h2 <- s1d.hist(a2, pgrid.draw = FALSE, porigin.draw = FALSE, pbackground.col = "grey", plot = FALSE, ppoly.col = "white", ppoly.alpha = 1) g2 <- insert(h2, b2, posi = "topleft", inset = c(0.25, 0.01), plot = FALSE) a3 <- rnorm(100) b3 <- s1d.barchart(sort(a3), p1d.horizontal = FALSE, plot = FALSE) h3 <- s1d.hist(a3, pgrid.draw = FALSE, porigin.draw = FALSE, pbackground.col = "grey", plot = FALSE, ppoly.col = "white", ppoly.alpha = 1) g3 <- insert(h3, b3, posi = "bottomleft", inset = 0.4, ratio = 0.2, plot = FALSE) a4 <- rnorm(100) b4 <- s1d.barchart(sort(a4), p1d.horizontal = FALSE, plot = FALSE) h4 <- s1d.hist(a4, pgrid.draw = FALSE, porigin.draw = FALSE, pbackground.col = "grey", plot = FALSE, ppoly.col = "white", ppoly.alpha = 1) g4 <- insert(h3, b3, posi = "bottomright", ratio = 0.3, plot = FALSE) G1 <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2), plot = TRUE) g5 <- s.label(w$co, plot = FALSE) g6 <- plotEig(w$eig, w$nf, psub = list(text = "Eigenvalues"), pbackground = list(box = TRUE), plot = FALSE) G2 <- insert(g6, g5, posi = "bottomright", ratio = 0.25) } else { par(mfrow=c(2,2)) f1 <- function(a){ opar=par("mar","xaxt","yaxt","plt") on.exit(par(opar)) par(mar=rep(.1,4),xaxt="n",yaxt="n",plt=par("plt")) hist(a,xlab="",ylab="",main="",col="white",proba=TRUE) lines(seq(-4,4,le=50),dnorm(seq(-4,4,le=50)),col="red") } a <- rnorm(100) barplot(sort(a)) add.scatter(f1(a),posi="topleft",bg.col="grey") a <- rnorm(100) barplot(sort(a)) add.scatter(f1(a),posi="topleft",bg.col="grey",inset=c(.25,.01)) a <- rnorm(100) barplot(sort(a)) add.scatter(f1(a),posi="topleft",bg.col="grey",inset=.25,ratio=.1) a <- rnorm(100) barplot(sort(a)) add.scatter(f1(a),posi="bottomright",bg.col="grey",ratio=.3) par(mfrow=c(1,1)) s.label(w$co) add.scatter.eig(w$eig,w$nf,posi="bottomright",1,2) }
aminoacyl
is a list containing the codon counts of 36 genes encoding yeast aminoacyl-tRNA-synthetase(S.Cerevisiae).
data(aminoacyl)
data(aminoacyl)
aminoacyl
is a list containing the 5 following objects:
is a vector giving the gene names.
is a vector giving the cellular localisation of the proteins (M = mitochondrial, C = cytoplasmic, I = indetermined, CI = cyto and mito).
is a vector containing the 64 triplets.
is a factor giving the amino acid names for each codon.
is a dataframe containing the codon counts for each gene.
Data prepared by D. Charif [email protected]
Chiapello H., Olivier E., Landes-Devauchelle C., Nitschké P. and Risler J.L (1999) Codon usage as a tool to predict the cellular localisation of eukariotic ribosomal proteins and aminoacyl-tRNA synthetases. Nucleic Acids Res., 27, 14, 2848–2851.
data(aminoacyl) aminoacyl$genes aminoacyl$usage.codon dudi.coa(aminoacyl$usage.codon, scannf = FALSE)
data(aminoacyl) aminoacyl$genes aminoacyl$usage.codon dudi.coa(aminoacyl$usage.codon, scannf = FALSE)
The analysis of molecular variance tests the differences among population and/or groups of populations in a way similar to ANOVA. It includes evolutionary distances among alleles.
amova(samples, distances, structures) ## S3 method for class 'amova' print(x, full = FALSE, ...)
amova(samples, distances, structures) ## S3 method for class 'amova' print(x, full = FALSE, ...)
samples |
a data frame with haplotypes (or genotypes) as rows, populations as columns and abundance as entries |
distances |
an object of class |
structures |
a data frame containing, in the jth row and the kth column, the name of the group of level k to which the jth population belongs |
x |
an object of class |
full |
a logical value indicating whether the original data ('distances', 'samples', 'structures') should be printed |
... |
further arguments passed to or from other methods |
Returns a list of class amova
call |
call |
results |
a data frame with the degrees of freedom, the sums of squares, and the mean squares. Rows represent levels of variability. |
componentsofcovariance |
a data frame containing the components of covariance and their contribution to the total covariance |
statphi |
a data frame containing the phi-statistics |
Sandrine Pavoine [email protected]
Excoffier, L., Smouse, P.E. and Quattro, J.M. (1992) Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics, 131, 479–491.
data(humDNAm) amovahum <- amova(humDNAm$samples, sqrt(humDNAm$distances), humDNAm$structures) amovahum
data(humDNAm) amovahum <- amova(humDNAm$samples, sqrt(humDNAm$distances), humDNAm$structures) amovahum
This data set gives the occurences for the allelic form on 8 loci in 10 populations of honeybees.
data(apis108)
data(apis108)
A data frame containing 180 rows (allelic forms on 8 loci) and 10 columns (populations of honeybees : El.Hermel, Al.Hoceima, Nimba, Celinda, Pretoria, Chalkidiki, Forli, Valenciennes, Umea and Seville).
Franck P., Garnery L., Solignac M. and Cornuet J.M. (2000) Molecular confirmation of a fourth lineage in honeybees from the Near-East. Apidologie, 31, 167–180.
data(apis108) str(apis108) names(apis108)
data(apis108) str(apis108) names(apis108)
The hierarchical apportionment of quadratic entropy defined by Rao (1982).
apqe(samples, dis = NULL, structures) ## S3 method for class 'apqe' print(x, full = FALSE, ...)
apqe(samples, dis = NULL, structures) ## S3 method for class 'apqe' print(x, full = FALSE, ...)
samples |
a data frame with haplotypes (or genotypes) as rows, populations as columns and abundance or presence-absence as entries |
dis |
an object of class |
structures |
a data frame that contains, in the jth row and the kth column, the name of the group of level k to which the jth population belongs |
x |
an object of class |
full |
a logical value that indicates whether the original data ('distances', 'samples', 'structures') should be printed |
... |
|
Returns a list of class apqe
call |
call |
results |
a data frame that contains the components of diversity. |
Sandrine Pavoine [email protected]
Rao, C.R. (1982) Diversity: its measurement, decomposition, apportionment and analysis. Sankhya: The Indian Journal of Statistics, A44, 1–22.
Pavoine S. and Dolédec S. (2005) The apportionment of quadratic entropy: a useful alternative for partitioning diversity in ecological data. Environmental and Ecological Statistics, 12, 125–138.
data(ecomor) ecomor.phylog <- taxo2phylog(ecomor$taxo) apqe(ecomor$habitat, ecomor.phylog$Wdist)
data(ecomor) ecomor.phylog <- taxo2phylog(ecomor$taxo) apqe(ecomor$habitat, ecomor.phylog$Wdist)
This dataset describe the distribution of 82 species of Alpine plants in 75 sites. Species traits and environmental variables are also measured.
data(aravo)
data(aravo)
aravo
is a list containing the following objects :
is a data.frame with the abundance values of 82 species (columns) in 75 sites (rows).
is a data.frame with the measurements of 6 environmental variables for the sites.
is data.frame with the measurements of 8 traits for the species.
is a vector with full species names.
The environmental variables are:
Aspect | Relative south aspect (opposite of the sine of aspect with flat coded 0) |
Slope | Slope inclination (degrees) |
Form | Microtopographic landform index: 1 (convexity); 2 (convex slope); 3 (right slope); 4 (concave slope); 5 (concavity) |
Snow | Mean snowmelt date (Julian day) averaged over 1997-1999 |
PhysD | Physical disturbance, i.e., percentage of unvegetated soil due to physical processes |
ZoogD | Zoogenic disturbance, i.e., quantity of unvegetated soil due to marmot activity: no; some; high |
The species traits for the plants are:
Height | Vegetative height (cm) |
Spread | Maximum lateral spread of clonal plants (cm) |
Angle | Leaf elevation angle estimated at the middle of the lamina |
Area | Area of a single leaf |
Thick | Maximum thickness of a leaf cross section (avoiding the midrib) |
SLA | Specific leaf area |
Nmass | Mass-based leaf nitrogen content |
Seed | Seed mass |
Choler, P. (2005) Consistent shifts in Alpine plant traits along a mesotopographical gradient. Arctic, Antarctic, and Alpine Research, 37,444–453.
data(aravo) coa1 <- dudi.coa(aravo$spe, scannf = FALSE, nf = 2) dudienv <- dudi.hillsmith(aravo$env, scannf = FALSE, nf = 2, row.w = coa1$lw) duditrait <- dudi.pca(aravo$traits, scannf = FALSE, nf = 2, row.w = coa1$cw) rlq1 <- rlq(dudienv, coa1, duditrait, scannf = FALSE, nf = 2) plot(rlq1)
data(aravo) coa1 <- dudi.coa(aravo$spe, scannf = FALSE, nf = 2) dudienv <- dudi.hillsmith(aravo$env, scannf = FALSE, nf = 2, row.w = coa1$lw) duditrait <- dudi.pca(aravo$traits, scannf = FALSE, nf = 2, row.w = coa1$cw) rlq1 <- rlq(dudienv, coa1, duditrait, scannf = FALSE, nf = 2) plot(rlq1)
This data set gives information about species of benthic macroinvertebrates in different sites and dates.
data(ardeche)
data(ardeche)
ardeche
is a list with 6 components.
is a data frame containing fauna table with 43 species (rows) and 35 samples (columns).
is a vector containing the repartition of samples for the 6 dates : july 1982, august 1982, november 1982, february 1983, april 1983 and july 1983.
is a vector containing the repartition of species in the 4 groups defining the species order.
is a date factor for samples (6 dates).
is a site factor for samples (6 sites).
is a species order factor (Ephemeroptera, Plecoptera, Coleoptera, Trichoptera).
The columns of the data frame ardeche$tab
define the samples by a number between 1 and 6 (the date)
and a letter between A and F (the site).
Cazes, P., Chessel, D., and Dolédec, S. (1988) L'analyse des correspondances internes d'un tableau partitionné : son usage en hydrobiologie. Revue de Statistique Appliquée, 36, 39–54.
data(ardeche) dudi1 <- dudi.coa(ardeche$tab, scan = FALSE) s.class(dudi1$co, ardeche$dat.fac) if(adegraphicsLoaded()) { s.label(dudi1$co, plab.cex = 0.5, add = TRUE) } else { s.label(dudi1$co, clab = 0.5, add.p = TRUE) }
data(ardeche) dudi1 <- dudi.coa(ardeche$tab, scan = FALSE) s.class(dudi1$co, ardeche$dat.fac) if(adegraphicsLoaded()) { s.label(dudi1$co, plab.cex = 0.5, add = TRUE) } else { s.label(dudi1$co, clab = 0.5, add.p = TRUE) }
'area' is a data frame with three variables.
The first variable is a factor defining the polygons.
The second and third variables are the xy coordinates of the
polygon vertices in the order where they are found.
area.plot : grey levels areas mapping
poly2area takes an object of class 'polylist' (maptools package) and returns a data frame of type area.
area2poly takes an object of type 'area' and returns a list of class 'polylist'
area2link takes an object of type 'area' and returns a proximity matrix which terms are given by
the length of the frontier between two polygons.
area.util.contour,area.util.xy and area.util.class are three utility functions.
area.plot(x, center = NULL, values = NULL, graph = NULL, lwdgraph = 2, nclasslegend = 8, clegend = 0.75, sub = "", csub = 1, possub = "topleft", cpoint = 0, label = NULL, clabel = 0, ...) area2poly(area) poly2area(polys) area2link(area) area.util.contour(area) area.util.xy(area)
area.plot(x, center = NULL, values = NULL, graph = NULL, lwdgraph = 2, nclasslegend = 8, clegend = 0.75, sub = "", csub = 1, possub = "topleft", cpoint = 0, label = NULL, clabel = 0, ...) area2poly(area) poly2area(polys) area2link(area) area.util.contour(area) area.util.xy(area)
x |
a data frame with three variables |
center |
a matrix with the same row number as x and two columns, the coordinates
of polygone centers. If NULL, it is computed with |
values |
if not NULL, a vector which values will be mapped to grey levels.
The values must be in the same order as the values in |
graph |
if not NULL, |
lwdgraph |
a line width to draw the neighbouring graph |
nclasslegend |
if |
clegend |
if not NULL, a character size for the legend, used with |
sub |
a string of characters to be inserted as sub-title |
csub |
a character size for the sub-titles, used with |
possub |
a string of characters indicating the sub-titles position ("topleft", "topright", "bottomleft", "bottomright") |
cpoint |
if positive, a character size for drawing the polygons vertices (check up),
used with |
label |
if not NULL, by default the levels of the factor that define the polygons
are used as labels. To change this value, use label. These labels must be in the same order than
|
clabel |
if not NULL, a character size for the polygon labels, |
polys |
a list belonging to the 'polylist' class in the spdep package |
area |
a data frame of class 'area' |
... |
further arguments passed to or from other methods |
poly2area returns a data frame 'factor,x,y'.
area2poly returns a list of class polylist
.
Daniel Chessel
data(elec88) par(mfrow = c(2, 2)) area.plot(elec88$area, cpoint = 1) area.plot(elec88$area, lab = elec88$lab$dep, clab = 0.75) area.plot(elec88$area, clab = 0.75) area.plot(elec88$area, graph = neig(area = elec88$area), sub = "Neighbourhood graph", possub = "topright") par(mfrow = c(1, 1)) ## Not run: par(mfrow = c(3, 3)) for(i in 1:9) { x <- elec88$tab[,i] area.plot(elec88$area, val = x, sub = names(elec88$tab)[i], csub = 3, cleg = 1.5) } par(mfrow = c(1, 1)) if(adegraphicsLoaded()) { if(requireNamespace("sp", quietly = TRUE)) { s.value(elec88$xy, elec88$tab, Sp = elec88$Spatial, method = "color", psub.text = names(elec88$tab), psub.cex = 3, pSp.col = "white", pgrid.draw = FALSE, porigin.include = FALSE) } } else { par(mfrow = c(3, 3)) for(i in 1:9) { x <- elec88$tab[, i] s.value(elec88$xy, elec88$tab[, i], contour = elec88$contour, meth = "greylevel", sub = names(elec88$tab)[i], csub = 3, cleg = 1.5, incl = FALSE) } par(mfrow = c(1, 1)) } if(!adegraphicsLoaded()) { data(irishdata) par(mfrow = c(2, 2)) w <- ade4:::area.util.contour(irishdata$area) xy <- ade4:::area.util.xy(irishdata$area) area.plot(irishdata$area, cpoint = 1) apply(w, 1, function(x) segments(x[1], x[2], x[3], x[4], lwd = 3)) area.plot(irishdata$area, clabel = 1) s.label(xy, area = irishdata$area, incl = FALSE, clab = 0, cpoi = 3, addax = FALSE, contour = w) s.label(xy, area = irishdata$area, incl = FALSE, addax = FALSE, contour = w) par(mfrow = c(1, 1)) } ## End(Not run) data(irishdata) w <- irishdata$area[c(42:53, 18:25), ] w w$poly <- as.factor(as.character(w$poly)) area.plot(w, clab = 2) points(68, 59, pch = 20, col = "red", cex = 3) points(68, 35, pch = 20, col = "red", cex = 3) points(45, 12, pch = 20, col = "red", cex = 3) sqrt((59 - 35) ^ 2) + sqrt((68 - 45) ^ 2 + (35 - 12) ^ 2) area2link(w)
data(elec88) par(mfrow = c(2, 2)) area.plot(elec88$area, cpoint = 1) area.plot(elec88$area, lab = elec88$lab$dep, clab = 0.75) area.plot(elec88$area, clab = 0.75) area.plot(elec88$area, graph = neig(area = elec88$area), sub = "Neighbourhood graph", possub = "topright") par(mfrow = c(1, 1)) ## Not run: par(mfrow = c(3, 3)) for(i in 1:9) { x <- elec88$tab[,i] area.plot(elec88$area, val = x, sub = names(elec88$tab)[i], csub = 3, cleg = 1.5) } par(mfrow = c(1, 1)) if(adegraphicsLoaded()) { if(requireNamespace("sp", quietly = TRUE)) { s.value(elec88$xy, elec88$tab, Sp = elec88$Spatial, method = "color", psub.text = names(elec88$tab), psub.cex = 3, pSp.col = "white", pgrid.draw = FALSE, porigin.include = FALSE) } } else { par(mfrow = c(3, 3)) for(i in 1:9) { x <- elec88$tab[, i] s.value(elec88$xy, elec88$tab[, i], contour = elec88$contour, meth = "greylevel", sub = names(elec88$tab)[i], csub = 3, cleg = 1.5, incl = FALSE) } par(mfrow = c(1, 1)) } if(!adegraphicsLoaded()) { data(irishdata) par(mfrow = c(2, 2)) w <- ade4:::area.util.contour(irishdata$area) xy <- ade4:::area.util.xy(irishdata$area) area.plot(irishdata$area, cpoint = 1) apply(w, 1, function(x) segments(x[1], x[2], x[3], x[4], lwd = 3)) area.plot(irishdata$area, clabel = 1) s.label(xy, area = irishdata$area, incl = FALSE, clab = 0, cpoi = 3, addax = FALSE, contour = w) s.label(xy, area = irishdata$area, incl = FALSE, addax = FALSE, contour = w) par(mfrow = c(1, 1)) } ## End(Not run) data(irishdata) w <- irishdata$area[c(42:53, 18:25), ] w w$poly <- as.factor(as.character(w$poly)) area.plot(w, clab = 2) points(68, 59, pch = 20, col = "red", cex = 3) points(68, 35, pch = 20, col = "red", cex = 3) points(45, 12, pch = 20, col = "red", cex = 3) sqrt((59 - 35) ^ 2) + sqrt((68 - 45) ^ 2 + (35 - 12) ^ 2) area2link(w)
This data set gives arrival times of 254 patients at an intensive care unit during one day.
data(arrival)
data(arrival)
arrival
is a list containing the 2 following objects :
is a vector giving the arrival times in the form HH:MM
is a vector giving the number of arrivals per hour for the day considered
Data taken from the Oriana software developed by Warren L. Kovach [email protected] starting from https://www.kovcomp.co.uk/oriana/index.html.
Fisher, N. I. (1993) Statistical Analysis of Circular Data. Cambridge University Press.
data(arrival) dotcircle(arrival$hours, pi/2 + pi/12)
data(arrival) dotcircle(arrival$hours, pi/2 + pi/12)
The function as.taxo
creates an object of class taxo
that is a sub-class of data.frame
.
Each column of the data frame must be a factor corresponding to a level j of the taxonomy (genus, family, ...).
The levels of factor j define some classes that must be completly included in classes of factor j+1.
A factor with exactly one level is not allowed. A factor with exactly one individual in each level is not allowed.
The function dist.taxo
compute taxonomic distances.
as.taxo(df) dist.taxo(taxo)
as.taxo(df) dist.taxo(taxo)
df |
a data frame |
taxo |
a data frame of class |
as.taxo
returns a data frame of class taxo
.
dist.taxo
returns a numeric of class dist
.
Daniel Chessel
Sébastien Ollier [email protected]
taxo2phylog
to transform an object of class taxo
into an object of class phylog
data(taxo.eg) tax <- as.taxo(taxo.eg[[1]]) tax.phy <- taxo2phylog(as.taxo(taxo.eg[[1]]),add.tools=TRUE) par(mfrow = c(1,2)) plot(tax.phy, clabel.l = 1.25, clabel.n = 1.25, f = 0.75) plot(taxo2phylog(as.taxo(taxo.eg[[1]][sample(15),])), clabel.l = 1.25, clabel.n = 1.25, f = 0.75) par(mfrow = c(1,1)) all(dist.taxo(tax)==tax.phy$Wdist)
data(taxo.eg) tax <- as.taxo(taxo.eg[[1]]) tax.phy <- taxo2phylog(as.taxo(taxo.eg[[1]]),add.tools=TRUE) par(mfrow = c(1,2)) plot(tax.phy, clabel.l = 1.25, clabel.n = 1.25, f = 0.75) plot(taxo2phylog(as.taxo(taxo.eg[[1]][sample(15),])), clabel.l = 1.25, clabel.n = 1.25, f = 0.75) par(mfrow = c(1,1)) all(dist.taxo(tax)==tax.phy$Wdist)
atlas
is a list containing three kinds of information about 23 regions (The French Alps) :
geographical coordinates, meteorology and bird presences.
data(atlas)
data(atlas)
atlas
is a list of 9 components:
is a convex hull of 23 geographical regions.
are the coordinates of the region centers and altitude (in meters).
is a vector of region names.
is a data frame with 7 variables: min and max temperature in january; min and max temperature in july; january, july and total rainfalls.
is a data frame with 15 variables (species).
is a data frame with 4 variables (x1, y1, x2, y2) for the contour display of The French Alps.
is a data frame with 3 variables altitude in percentage [0,800], ]800,1500] and ]1500,5000].
is the map of the 23 regions of The French Alps (an object of the class SpatialPolygons
of sp
).
is the contour of the map of the 23 regions of the French Alps (an object of the class SpatialPolygons
of sp
).
Extract from:
Lebreton, Ph. (1977) Les oiseaux nicheurs rhonalpins. Atlas ornithologique Rhone-Alpes.
Centre Ornithologique Rhone-Alpes, Universite Lyon 1, 69621 Villeurbanne.
Direction de la Protection de la Nature, Ministere de la Qualite de la Vie. 1–354.
data(atlas) if(adegraphicsLoaded()) { if(requireNamespace("sp", quietly = TRUE)) { g11 <- s.Spatial(atlas$Spatial, pSp.col = "white", plot = FALSE) g12 <- s.label(atlas$area[, 2:3], plabels.cex = 0, plot = FALSE) g1 <- superpose(g11, g12, plot = FALSE) g2 <- s.label(atlas$xy, lab = atlas$names.district, Sp = atlas$Spatial, pgrid.dra = FALSE, pSp.col = "white", plot = FALSE) obj3 <- sp::SpatialPolygonsDataFrame(Sr = atlas$Spatial, data = atlas$meteo) g3 <- s.Spatial(obj3[, 1], nclass = 12, psub = list(position = "topleft", text = "Temp Mini January", cex = 2), plot = FALSE) g4 <- s.corcircle((dudi.pca(atlas$meteo, scann = FALSE)$co), plabels.cex = 1, plot = FALSE) G1 <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) obj5 <- sp::SpatialPolygonsDataFrame(Sr = atlas$Spatial, data = dudi.pca(atlas$meteo, scann = FALSE)$li) g5 <- s.Spatial(obj5[, 1], nclass = 12, psub = list(position = "topleft", text = "Principal Component Analysis analysis", cex = 1.5), plot = FALSE) coa1 <- dudi.coa(atlas$birds, scann = FALSE, nf = 1) obj6 <- sp::SpatialPolygonsDataFrame(Sr = atlas$Spatial, data = coa1$li) g6 <- s.Spatial(obj6[, 1], nclass = 12, psub = list(position = "topleft", text = "Correspondence analysis", cex = 1.5), plot = FALSE) g7 <- s.value(atlas$xy, coa1$li$Axis1, Sp = atlas$Spatial.contour, ppoints.cex = 2, porigin.include = FALSE, paxes.draw = FALSE, pSp.col = "white", plot = FALSE) g8 <- triangle.label(atlas$alti, plabels.cex = 0, plot = FALSE) G2 <- ADEgS(list(g5, g6, g7, g8), layout = c(2, 2)) } } else { op <- par(no.readonly = TRUE) par(mfrow = c(2, 2)) area.plot(atlas$area, cpoin = 1.5) area.plot(atlas$area, lab = atlas$names.district, clab = 1) x <- atlas$meteo$mini.jan names(x) <- row.names(atlas$meteo) area.plot(atlas$area, val = x, ncl = 12, sub = "Temp Mini January", csub = 2, cleg = 1) s.corcircle((dudi.pca(atlas$meteo, scann = FALSE)$co), clab = 1) area.plot(atlas$area, val = dudi.pca(atlas$meteo,scann=FALSE)$li[, 1], ncl = 12, sub = "Principal Component Analysis analysis", csub = 1.5, cleg = 1) birds.coa <- dudi.coa(atlas$birds, sca = FALSE, nf = 1) x <- birds.coa$li$Axis1 area.plot(atlas$area, val = x, ncl = 12, sub = "Correspondence analysis", csub = 1.5, cleg = 1) s.value(atlas$xy, x, contour = atlas$contour, csi = 2, incl = FALSE, addax = FALSE) triangle.plot(atlas$alti) par(op) par(mfrow = c(1, 1))}
data(atlas) if(adegraphicsLoaded()) { if(requireNamespace("sp", quietly = TRUE)) { g11 <- s.Spatial(atlas$Spatial, pSp.col = "white", plot = FALSE) g12 <- s.label(atlas$area[, 2:3], plabels.cex = 0, plot = FALSE) g1 <- superpose(g11, g12, plot = FALSE) g2 <- s.label(atlas$xy, lab = atlas$names.district, Sp = atlas$Spatial, pgrid.dra = FALSE, pSp.col = "white", plot = FALSE) obj3 <- sp::SpatialPolygonsDataFrame(Sr = atlas$Spatial, data = atlas$meteo) g3 <- s.Spatial(obj3[, 1], nclass = 12, psub = list(position = "topleft", text = "Temp Mini January", cex = 2), plot = FALSE) g4 <- s.corcircle((dudi.pca(atlas$meteo, scann = FALSE)$co), plabels.cex = 1, plot = FALSE) G1 <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) obj5 <- sp::SpatialPolygonsDataFrame(Sr = atlas$Spatial, data = dudi.pca(atlas$meteo, scann = FALSE)$li) g5 <- s.Spatial(obj5[, 1], nclass = 12, psub = list(position = "topleft", text = "Principal Component Analysis analysis", cex = 1.5), plot = FALSE) coa1 <- dudi.coa(atlas$birds, scann = FALSE, nf = 1) obj6 <- sp::SpatialPolygonsDataFrame(Sr = atlas$Spatial, data = coa1$li) g6 <- s.Spatial(obj6[, 1], nclass = 12, psub = list(position = "topleft", text = "Correspondence analysis", cex = 1.5), plot = FALSE) g7 <- s.value(atlas$xy, coa1$li$Axis1, Sp = atlas$Spatial.contour, ppoints.cex = 2, porigin.include = FALSE, paxes.draw = FALSE, pSp.col = "white", plot = FALSE) g8 <- triangle.label(atlas$alti, plabels.cex = 0, plot = FALSE) G2 <- ADEgS(list(g5, g6, g7, g8), layout = c(2, 2)) } } else { op <- par(no.readonly = TRUE) par(mfrow = c(2, 2)) area.plot(atlas$area, cpoin = 1.5) area.plot(atlas$area, lab = atlas$names.district, clab = 1) x <- atlas$meteo$mini.jan names(x) <- row.names(atlas$meteo) area.plot(atlas$area, val = x, ncl = 12, sub = "Temp Mini January", csub = 2, cleg = 1) s.corcircle((dudi.pca(atlas$meteo, scann = FALSE)$co), clab = 1) area.plot(atlas$area, val = dudi.pca(atlas$meteo,scann=FALSE)$li[, 1], ncl = 12, sub = "Principal Component Analysis analysis", csub = 1.5, cleg = 1) birds.coa <- dudi.coa(atlas$birds, sca = FALSE, nf = 1) x <- birds.coa$li$Axis1 area.plot(atlas$area, val = x, ncl = 12, sub = "Correspondence analysis", csub = 1.5, cleg = 1) s.value(atlas$xy, x, contour = atlas$contour, csi = 2, incl = FALSE, addax = FALSE) triangle.plot(atlas$alti) par(op) par(mfrow = c(1, 1))}
This data set contains information about genetic variability of Atya innocous and Atya scabra in Guadeloupe (France).
data(atya)
data(atya)
atya
is a list with the following components:
a data frame with the coordinates of the 31 sites
a data frame with 22 variables collected on 31 sites
a neighborhood object (class nb
defined in package spdep
)
Fievet, E., Eppe, F. and Dolédec, S. (2001) Etude de la variabilité morphométrique et génétique des populations de Cacadors (Atya innocous et Atya scabra) de l'île de Basse-Terre. Direction Régionale de L'Environnement Guadeloupe, Laboratoire des hydrosystèmes fluviaux, Université Lyon 1.
## Not run: data(atya) if(requireNamespace("pixmap", quietly = TRUE)) { atya.digi <- pixmap::read.pnm(system.file("pictures/atyadigi.pnm", package = "ade4")) atya.carto <- pixmap::read.pnm(system.file("pictures/atyacarto.pnm", package = "ade4")) par(mfrow = c(1, 2)) pixmap:::plot(atya.digi) pixmap:::plot(atya.carto) points(atya$xy, pch = 20, cex = 2) } if(requireNamespace("spdep", quietly = TRUE)) { plot(atya$nb, atya$xy, col = "red", add = TRUE, lwd = 2) par(mfrow = c(1,1)) } ## End(Not run)
## Not run: data(atya) if(requireNamespace("pixmap", quietly = TRUE)) { atya.digi <- pixmap::read.pnm(system.file("pictures/atyadigi.pnm", package = "ade4")) atya.carto <- pixmap::read.pnm(system.file("pictures/atyacarto.pnm", package = "ade4")) par(mfrow = c(1, 2)) pixmap:::plot(atya.digi) pixmap:::plot(atya.carto) points(atya$xy, pch = 20, cex = 2) } if(requireNamespace("spdep", quietly = TRUE)) { plot(atya$nb, atya$xy, col = "red", add = TRUE, lwd = 2) par(mfrow = c(1,1)) } ## End(Not run)
This data set contains information about spatial distribution of bird species in a zone surrounding the river Rhône near Lyon (France).
data(avijons)
data(avijons)
avijons
is a list with the following components:
a data frame with the coordinates of the sites
an object of class area
a data frame with the abundance of 64 bird species in 91 sites
a vector of strings of character with the species names in french
an object of the class SpatialPolygons
of sp
,
containing the map
Bournaud, M., Amoros, C., Chessel, D., Coulet, M., Doledec, S., Michelot, J.L., Pautou, G., Rostan, J.C., Tachet, H. and Thioulouse, J. (1990). Peuplements d'oiseaux et propriétés des écocomplexes de la plaine du Rhône : descripteurs de fonctionnement global et gestion des berges. Rapport programme S.R.E.T.I.E., Ministère de l'Environnement CORA et URA CNRS 367, Univ. Lyon I.
Thioulouse, J., Chessel, D. and Champely, S. (1995) Multivariate analysis of spatial patterns: a unified approach to local and global structures. Environmental and Ecological Statistics, 2, 1–14.
See a data description at http://pbil.univ-lyon1.fr/R/pdf/pps051.pdf (in French).
data(avijons) w1 <- dudi.coa(avijons$fau, scannf = FALSE)$li area.plot(avijons$area, center = avijons$xy, val = w1[, 1], clab = 0.75, sub = "CA Axis 1", csub = 3) ## Not run: data(avijons) if(!adegraphicsLoaded()) { if(requireNamespace("pixmap", quietly = TRUE)) { pnm.eau <- pixmap::read.pnm(system.file("pictures/avijonseau.pnm", package = "ade4")) pnm.rou <- pixmap::read.pnm(system.file("pictures/avijonsrou.pnm", package = "ade4")) pnm.veg <- pixmap::read.pnm(system.file("pictures/avijonsveg.pnm", package = "ade4")) pnm.vil <- pixmap::read.pnm(system.file("pictures/avijonsvil.pnm", package = "ade4")) jons.coa <- dudi.coa(avijons$fau, scan = FALSE, nf = 4) par(mfcol = c(3, 2)) s.value(avijons$xy, jons.coa$li[, 1], pixmap = pnm.rou, inclu = FALSE, grid = FALSE, addax = FALSE, cleg = 0, sub = "F1+ROADS", csub = 3) s.value(avijons$xy, jons.coa$li[, 1], pixmap = pnm.veg, inclu = FALSE, grid = FALSE, addax = FALSE, cleg = 0, sub = "F1+TREES", csub = 3) s.value(avijons$xy, jons.coa$li[, 1], pixmap = pnm.eau, inclu = FALSE, grid = FALSE, addax = FALSE, cleg = 0, sub = "F1+WATER", csub = 3) s.value(avijons$xy, jons.coa$li[, 2], pixmap = pnm.rou, inclu = FALSE, grid = FALSE, addax = FALSE, cleg = 0, sub = "F2+ROADS", csub = 3) s.value(avijons$xy, jons.coa$li[, 2], pixmap = pnm.veg, inclu = FALSE, grid = FALSE, addax = FALSE, cleg = 0, sub = "F2+TREES", csub = 3) s.value(avijons$xy, jons.coa$li[, 2], pixmap = pnm.eau, inclu = FALSE, grid = FALSE, addax = FALSE, cleg = 0, sub = "F2+WATER", csub = 3) par(mfrow = c(1, 1)) } if(requireNamespace("spdep", quietly = TRUE) & requireNamespace("pixmap", quietly = TRUE) & requireNamespace("adespatial", quietly = TRUE)) { link1 <- area2link(avijons$area) lw1 <- apply(link1, 1, function(x) x[x > 0]) neig1 <- neig(mat01 = 1*(link1 > 0)) nb1 <- neig2nb(neig1) listw1 <- spdep::nb2listw(nb1,lw1) jons.ms <- adespatial::multispati(jons.coa, listw1, scan = FALSE, nfp = 3, nfn = 2) summary(jons.ms) par(mfrow = c(2, 2)) barplot(jons.coa$eig) barplot(jons.ms$eig) s.corcircle(jons.ms$as) plot(jons.coa$li[, 1], jons.ms$li[, 1]) par(mfrow = c(1, 1)) par(mfcol = c(3, 2)) s.value(avijons$xy, jons.ms$li[, 1], pixmap = pnm.rou, inclu = FALSE, grid = FALSE, addax = FALSE, cleg = 0, sub = "F1+ROADS", csub = 3) s.value(avijons$xy, jons.ms$li[, 1], pixmap = pnm.veg, inclu = FALSE, grid = FALSE, addax = FALSE, cleg = 0, sub = "F1+TREES", csub = 3) s.value(avijons$xy, jons.ms$li[, 1], pixmap = pnm.eau, inclu = FALSE, grid = FALSE, addax = FALSE, cleg = 0, sub = "F1+WATER", csub = 3) s.value(avijons$xy, jons.ms$li[, 2], pixmap = pnm.rou, inclu = FALSE, grid = FALSE, addax = FALSE, cleg = 0, sub = "F2+ROADS", csub = 3) s.value(avijons$xy, jons.ms$li[, 2], pixmap = pnm.veg, inclu = FALSE, grid = FALSE, addax = FALSE, cleg = 0, sub = "F2+TREES", csub = 3) s.value(avijons$xy, jons.ms$li[, 2], pixmap = pnm.eau, inclu = FALSE, grid = FALSE, addax = FALSE, cleg = 0, sub = "F2+WATER", csub = 3) par(mfrow = c(1, 1)) }} ## End(Not run)
data(avijons) w1 <- dudi.coa(avijons$fau, scannf = FALSE)$li area.plot(avijons$area, center = avijons$xy, val = w1[, 1], clab = 0.75, sub = "CA Axis 1", csub = 3) ## Not run: data(avijons) if(!adegraphicsLoaded()) { if(requireNamespace("pixmap", quietly = TRUE)) { pnm.eau <- pixmap::read.pnm(system.file("pictures/avijonseau.pnm", package = "ade4")) pnm.rou <- pixmap::read.pnm(system.file("pictures/avijonsrou.pnm", package = "ade4")) pnm.veg <- pixmap::read.pnm(system.file("pictures/avijonsveg.pnm", package = "ade4")) pnm.vil <- pixmap::read.pnm(system.file("pictures/avijonsvil.pnm", package = "ade4")) jons.coa <- dudi.coa(avijons$fau, scan = FALSE, nf = 4) par(mfcol = c(3, 2)) s.value(avijons$xy, jons.coa$li[, 1], pixmap = pnm.rou, inclu = FALSE, grid = FALSE, addax = FALSE, cleg = 0, sub = "F1+ROADS", csub = 3) s.value(avijons$xy, jons.coa$li[, 1], pixmap = pnm.veg, inclu = FALSE, grid = FALSE, addax = FALSE, cleg = 0, sub = "F1+TREES", csub = 3) s.value(avijons$xy, jons.coa$li[, 1], pixmap = pnm.eau, inclu = FALSE, grid = FALSE, addax = FALSE, cleg = 0, sub = "F1+WATER", csub = 3) s.value(avijons$xy, jons.coa$li[, 2], pixmap = pnm.rou, inclu = FALSE, grid = FALSE, addax = FALSE, cleg = 0, sub = "F2+ROADS", csub = 3) s.value(avijons$xy, jons.coa$li[, 2], pixmap = pnm.veg, inclu = FALSE, grid = FALSE, addax = FALSE, cleg = 0, sub = "F2+TREES", csub = 3) s.value(avijons$xy, jons.coa$li[, 2], pixmap = pnm.eau, inclu = FALSE, grid = FALSE, addax = FALSE, cleg = 0, sub = "F2+WATER", csub = 3) par(mfrow = c(1, 1)) } if(requireNamespace("spdep", quietly = TRUE) & requireNamespace("pixmap", quietly = TRUE) & requireNamespace("adespatial", quietly = TRUE)) { link1 <- area2link(avijons$area) lw1 <- apply(link1, 1, function(x) x[x > 0]) neig1 <- neig(mat01 = 1*(link1 > 0)) nb1 <- neig2nb(neig1) listw1 <- spdep::nb2listw(nb1,lw1) jons.ms <- adespatial::multispati(jons.coa, listw1, scan = FALSE, nfp = 3, nfn = 2) summary(jons.ms) par(mfrow = c(2, 2)) barplot(jons.coa$eig) barplot(jons.ms$eig) s.corcircle(jons.ms$as) plot(jons.coa$li[, 1], jons.ms$li[, 1]) par(mfrow = c(1, 1)) par(mfcol = c(3, 2)) s.value(avijons$xy, jons.ms$li[, 1], pixmap = pnm.rou, inclu = FALSE, grid = FALSE, addax = FALSE, cleg = 0, sub = "F1+ROADS", csub = 3) s.value(avijons$xy, jons.ms$li[, 1], pixmap = pnm.veg, inclu = FALSE, grid = FALSE, addax = FALSE, cleg = 0, sub = "F1+TREES", csub = 3) s.value(avijons$xy, jons.ms$li[, 1], pixmap = pnm.eau, inclu = FALSE, grid = FALSE, addax = FALSE, cleg = 0, sub = "F1+WATER", csub = 3) s.value(avijons$xy, jons.ms$li[, 2], pixmap = pnm.rou, inclu = FALSE, grid = FALSE, addax = FALSE, cleg = 0, sub = "F2+ROADS", csub = 3) s.value(avijons$xy, jons.ms$li[, 2], pixmap = pnm.veg, inclu = FALSE, grid = FALSE, addax = FALSE, cleg = 0, sub = "F2+TREES", csub = 3) s.value(avijons$xy, jons.ms$li[, 2], pixmap = pnm.eau, inclu = FALSE, grid = FALSE, addax = FALSE, cleg = 0, sub = "F2+WATER", csub = 3) par(mfrow = c(1, 1)) }} ## End(Not run)
avimedi
is a list containing the information about 302 sites :
frequencies of 51 bird species ; two factors (habitats and Mediterranean origin).
data(avimedi)
data(avimedi)
This list contains the following objects:
is a data frame 302 sites - 51 bird species.
is a data frame 302 sites - 2 factors : reg
with two levels Provence (Pr
,
South of France) and Corsica (Co
) ;
str
with six levels describing the vegetation from a very low matorral (1) up to a mature forest of holm oaks (6).
is a vector 51 latin names.
Blondel, J., Chessel, D., & Frochot, B. (1988) Bird species impoverishment, niche expansion, and density inflation in mediterranean island habitats. Ecology, 69, 1899–1917.
## Not run: data(avimedi) coa1 <- dudi.coa(avimedi$fau, scan = FALSE, nf = 3) bet1 <- bca(coa1, avimedi$plan$str, scan = FALSE) wit1 <- wca(coa1, avimedi$plan$reg, scan=FALSE) pcaiv1 <- pcaiv(coa1, avimedi$plan, scan = FALSE) if(adegraphicsLoaded()) { g1 <- s.class(coa1$li, avimedi$plan$str:avimedi$plan$reg, psub.text = "Correspondences Analysis", plot = FALSE) g2 <- s.class(bet1$ls, avimedi$plan$str, psub.text = "Between Analysis", plot = FALSE) g3 <- s.class(wit1$li, avimedi$plan$str, psub.text = "Within Analysis", plot = FALSE) g41 <- s.match(pcaiv1$li, pcaiv1$ls, plabels.cex = 0, psub.text = "Canonical Correspondences Analysis", plot = FALSE) g42 <- s.class(pcaiv1$li, avimedi$plan$str:avimedi$plan$reg, plot = FALSE) g4 <- superpose(g41, g42, plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2,2)) s.class(coa1$li,avimedi$plan$str:avimedi$plan$reg, sub = "Correspondences Analysis") s.class(bet1$ls, avimedi$plan$str, sub = "Between Analysis") s.class(wit1$li, avimedi$plan$str, sub = "Within Analysis") s.match(pcaiv1$li, pcaiv1$ls, clab = 0, sub = "Canonical Correspondences Analysis") s.class(pcaiv1$li, avimedi$plan$str:avimedi$plan$reg, add.plot = TRUE) par(mfrow=c(1,1)) } ## End(Not run)
## Not run: data(avimedi) coa1 <- dudi.coa(avimedi$fau, scan = FALSE, nf = 3) bet1 <- bca(coa1, avimedi$plan$str, scan = FALSE) wit1 <- wca(coa1, avimedi$plan$reg, scan=FALSE) pcaiv1 <- pcaiv(coa1, avimedi$plan, scan = FALSE) if(adegraphicsLoaded()) { g1 <- s.class(coa1$li, avimedi$plan$str:avimedi$plan$reg, psub.text = "Correspondences Analysis", plot = FALSE) g2 <- s.class(bet1$ls, avimedi$plan$str, psub.text = "Between Analysis", plot = FALSE) g3 <- s.class(wit1$li, avimedi$plan$str, psub.text = "Within Analysis", plot = FALSE) g41 <- s.match(pcaiv1$li, pcaiv1$ls, plabels.cex = 0, psub.text = "Canonical Correspondences Analysis", plot = FALSE) g42 <- s.class(pcaiv1$li, avimedi$plan$str:avimedi$plan$reg, plot = FALSE) g4 <- superpose(g41, g42, plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2,2)) s.class(coa1$li,avimedi$plan$str:avimedi$plan$reg, sub = "Correspondences Analysis") s.class(bet1$ls, avimedi$plan$str, sub = "Between Analysis") s.class(wit1$li, avimedi$plan$str, sub = "Within Analysis") s.match(pcaiv1$li, pcaiv1$ls, clab = 0, sub = "Canonical Correspondences Analysis") s.class(pcaiv1$li, avimedi$plan$str:avimedi$plan$reg, add.plot = TRUE) par(mfrow=c(1,1)) } ## End(Not run)
This data set is a list of information about 51 sites : bird species and environmental variables.
A data frame contains biological traits for each species.
data(aviurba)
data(aviurba)
This list contains the following objects:
is a data frame 51 sites 40 bird species.
is a data frame 51 sites 11 environmental variables (see details).
is a data frame 40 species 4 biological traits (see details).
is a vector of the species names in french.
is a vector of the species names in latin.
is a factor : the species families.
aviurba$mil
contains for each site, 11 habitat attributes describing the degree of urbanization.
The presence or absence of farms or villages, small buildings, high buildings, industry, fields, grassland, scrubby areas,
deciduous woods, coniferous woods, noisy area are noticed. At least, the vegetation cover (variable 11) is a factor with 8 levels
from a minimum cover (R5) up to a maximum (R100).
aviurba$traits
contains four factors : feeding habit (insectivor, granivore, omnivore), feeding stratum (ground, aerial, foliage and scrub),
breeding stratum (ground, building, scrub, foliage) and migration strategy (resident, migrant).
Dolédec, S., Chessel, D., Ter Braak,C. J. F. and Champely S. (1996) Matching species traits to environmental variables: a new three-table ordination method. Environmental and Ecological Statistics, 3, 143–166.
data(aviurba) a1 <- dudi.coa(aviurba$fau, scan = FALSE, nf=4) a2 <- dudi.acm(aviurba$mil, row.w = a1$lw, scan = FALSE, nf = 4) plot(coinertia(a1, a2, scan = FALSE))
data(aviurba) a1 <- dudi.coa(aviurba$fau, scan = FALSE, nf=4) a2 <- dudi.acm(aviurba$mil, row.w = a1$lw, scan = FALSE, nf = 4) plot(coinertia(a1, a2, scan = FALSE))
bacteria
is a list containing 43 species and genomic informations : codons, amino acid and bases.
data(bacteria)
data(bacteria)
This list contains the following objects:
is a factor with the amino acid names for each codon.
is a data frame 43 species 64 codons.
is a data frame 43 species 21 amino acid.
is a data frame 43 species 4 bases.
Data prepared by J. Lobry [email protected] starting from https://www.jcvi.org/.
data(bacteria) names(bacteria$espcodon) names(bacteria$espaa) names(bacteria$espbase) sum(bacteria$espcodon) # 22,619,749 codons if(adegraphicsLoaded()) { g <- scatter(dudi.coa(bacteria$espcodon, scann = FALSE), posi = "bottomleft") } else { scatter(dudi.coa(bacteria$espcodon, scann = FALSE), posi = "bottomleft") }
data(bacteria) names(bacteria$espcodon) names(bacteria$espaa) names(bacteria$espbase) sum(bacteria$espcodon) # 22,619,749 codons if(adegraphicsLoaded()) { g <- scatter(dudi.coa(bacteria$espcodon, scann = FALSE), posi = "bottomleft") } else { scatter(dudi.coa(bacteria$espcodon, scann = FALSE), posi = "bottomleft") }
banque
gives the results of a bank survey onto 810 customers.
data(banque)
data(banque)
This data frame contains the following columns:
csp: "Socio-professional categories" a factor with levels
agric
Farmers
artis
Craftsmen, Shopkeepers, Company directors
cadsu
Executives and higher intellectual professions
inter
Intermediate professions
emplo
Other white-collar workers
ouvri
Manual workers
retra
Pensionners
inact
Non working population
etudi
Students
duree: "Time relations with the customer" a factor with levels
dm2
<2 years
d24
[2 years, 4 years[
d48
[4 years, 8 years[
d812
[8 years, 12 years[
dp12
>= 12 years
oppo: "Stopped a check?" a factor with levels
non
no
oui
yes
age: "Customer's age" a factor with levels
ai25
[18 years, 25 years[
ai35
[25 years, 35 years[
ai45
[35 years, 45 years[
ai55
[45 years, 55 years[
ai75
[55 years, 75 years[
sexe: "Customer's gender" a factor with levels
hom
Male
fem
Female
interdit: "No checkbook allowed" a factor with levels
non
no
oui
yes
cableue: "Possess a bank card?" a factor with levels
non
no
oui
yes
assurvi: "Contrat of life insurance?" a factor with levels
non
no
oui
yes
soldevu: "Balance of the current accounts" a factor with levels
p4
credit balance > 20000
p3
credit balance 12000-20000
p2
credit balance 4000-12000
p1
credit balance >0-4000
n1
debit balance 0-4000
n2
debit balance >4000
eparlog: "Savings and loan association account amount" a factor with levels
for
> 20000
fai
>0 and <20000
nul
nulle
eparliv: "Savings bank amount" a factor with levels
for
> 20000
fai
>0 and <20000
nul
nulle
credhab: "Home loan owner" a factor with levels
non
no
oui
yes
credcon: "Consumer credit amount" a factor with levels
nul
none
fai
>0 and <20000
for
> 20000
versesp: "Check deposits" a factor with levels
oui
yes
non
no
retresp: "Cash withdrawals" a factor with levels
fai
< 2000
moy
2000-5000
for
> 5000
remiche: "Endorsed checks amount" a factor with levels
for
>10000
moy
10000-5000
fai
1-5000
nul
none
preltre: "Treasury Department tax deductions" a factor with levels
nul
none
fai
<1000
moy
>1000
prelfin: "Financial institution deductions" a factor with levels
nul
none
fai
<1000
moy
>1000
viredeb: "Debit transfer amount" a factor with levels
nul
none
fai
<2500
moy
2500-5000
for
>5000
virecre: "Credit transfer amount" a factor with levels
for
>10000
moy
10000-5000
fai
<5000
nul
aucun
porttit: "Securities portfolio estimations" a factor with levels
nul
none
fai
< 20000
moy
20000-100000
for
>100000
anonymous
data(banque) banque.acm <- dudi.acm(banque, scannf = FALSE, nf = 3) apply(banque.acm$cr, 2, mean) banque.acm$eig[1:banque.acm$nf] # the same thing if(adegraphicsLoaded()) { g <- s.arrow(banque.acm$c1, plabels.cex = 0.75) } else { s.arrow(banque.acm$c1, clab = 0.75) }
data(banque) banque.acm <- dudi.acm(banque, scannf = FALSE, nf = 3) apply(banque.acm$cr, 2, mean) banque.acm$eig[1:banque.acm$nf] # the same thing if(adegraphicsLoaded()) { g <- s.arrow(banque.acm$c1, plabels.cex = 0.75) } else { s.arrow(banque.acm$c1, clab = 0.75) }
This data set is a list containing relations between sites and fish species linked to dates.
data(baran95)
data(baran95)
This list contains the following objects:
is a data frame 95 seinings and 33 fish species.
is a data frame 2 factors : date and site. The date
has 6 levels (april 1993, june 1993,
august 1993, october 1993, december 1993 and february 1994) and the sites
are defined by 4 distances to the
Atlantic Ocean (km03, km17, km33 and km46).
is a vector of species latin names.
Baran, E. (1995) Dynamique spatio-temporelle des peuplements de Poissons estuariens en Guinée (Afrique de l'Ouest). Thèse de Doctorat, Université de Bretagne Occidentale. Data collected by net fishing sampling in the Fatala river estuary.
See a data description at http://pbil.univ-lyon1.fr/R/pdf/pps027.pdf (in French).
data(baran95) w <- dudi.pca(log(baran95$fau + 1), scal = FALSE, scann = FALSE, nf = 3) w1 <- wca(w, baran95$plan$date, scann = FALSE) fatala <- ktab.within(w1) stat1 <- statis(fatala, scan = FALSE, nf = 3) mfa1 <- mfa(fatala, scan = FALSE, nf = 3) if(adegraphicsLoaded()) { g1 <- s.class(stat1$C.Co, baran95$plan$site, facets = baran95$plan$date, pellipses.axes.draw = FALSE, ppoints.cex = 0.5, plot = FALSE) n1 <- length(g1@ADEglist) g2 <- ADEgS(lapply(1:n1, function(i) s.label(stat1$C.Co, plabels.cex = 0, ppoints.cex = 0.5, plot = FALSE)), positions = g1@positions, plot = FALSE) G1 <- superpose(g2, g1, plot = TRUE) G2 <- kplot(stat1, arrow = FALSE, traject = FALSE, class = baran95$plan$site, col.plabels.cex = 0, ppoints.cex = 0.5) g3 <- s.class(mfa1$co, baran95$plan$site, facets = baran95$plan$date, pellipses.axes.draw = FALSE, ppoints.cex = 0.5, plot = FALSE) n2 <- length(g3@ADEglist) g4 <- ADEgS(lapply(1:n2, function(i) s.label(mfa1$co, plabels.cex = 0, ppoints.cex = 0.5, plot = FALSE)), positions = g3@positions, plot = FALSE) G3 <- superpose(g4, g3, plot = TRUE) } else { par(mfrow = c(3, 2)) w2 <- split(stat1$C.Co, baran95$plan$date) w3 <- split(baran95$plan$site, baran95$plan$date) for (j in 1:6) { s.label(stat1$C.Co[,1:2], clab = 0, sub = tab.names(fatala)[j], csub = 3) s.class(w2[[j]][, 1:2], w3[[j]], clab = 2, axese = FALSE, add.plot = TRUE) } par(mfrow = c(1, 1)) kplot(stat1, arrow = FALSE, traj = FALSE, clab = 2, uni = TRUE, class = baran95$plan$site) #simpler par(mfrow = c(3, 2)) w4 <- split(mfa1$co, baran95$plan$date) for (j in 1:6) { s.label(mfa1$co[, 1:2], clab = 0, sub = tab.names(fatala)[j], csub = 3) s.class(w4[[j]][, 1:2], w3[[j]], clab = 2, axese = FALSE, add.plot = TRUE) } par(mfrow = c(1, 1)) }
data(baran95) w <- dudi.pca(log(baran95$fau + 1), scal = FALSE, scann = FALSE, nf = 3) w1 <- wca(w, baran95$plan$date, scann = FALSE) fatala <- ktab.within(w1) stat1 <- statis(fatala, scan = FALSE, nf = 3) mfa1 <- mfa(fatala, scan = FALSE, nf = 3) if(adegraphicsLoaded()) { g1 <- s.class(stat1$C.Co, baran95$plan$site, facets = baran95$plan$date, pellipses.axes.draw = FALSE, ppoints.cex = 0.5, plot = FALSE) n1 <- length(g1@ADEglist) g2 <- ADEgS(lapply(1:n1, function(i) s.label(stat1$C.Co, plabels.cex = 0, ppoints.cex = 0.5, plot = FALSE)), positions = g1@positions, plot = FALSE) G1 <- superpose(g2, g1, plot = TRUE) G2 <- kplot(stat1, arrow = FALSE, traject = FALSE, class = baran95$plan$site, col.plabels.cex = 0, ppoints.cex = 0.5) g3 <- s.class(mfa1$co, baran95$plan$site, facets = baran95$plan$date, pellipses.axes.draw = FALSE, ppoints.cex = 0.5, plot = FALSE) n2 <- length(g3@ADEglist) g4 <- ADEgS(lapply(1:n2, function(i) s.label(mfa1$co, plabels.cex = 0, ppoints.cex = 0.5, plot = FALSE)), positions = g3@positions, plot = FALSE) G3 <- superpose(g4, g3, plot = TRUE) } else { par(mfrow = c(3, 2)) w2 <- split(stat1$C.Co, baran95$plan$date) w3 <- split(baran95$plan$site, baran95$plan$date) for (j in 1:6) { s.label(stat1$C.Co[,1:2], clab = 0, sub = tab.names(fatala)[j], csub = 3) s.class(w2[[j]][, 1:2], w3[[j]], clab = 2, axese = FALSE, add.plot = TRUE) } par(mfrow = c(1, 1)) kplot(stat1, arrow = FALSE, traj = FALSE, clab = 2, uni = TRUE, class = baran95$plan$site) #simpler par(mfrow = c(3, 2)) w4 <- split(mfa1$co, baran95$plan$date) for (j in 1:6) { s.label(mfa1$co[, 1:2], clab = 0, sub = tab.names(fatala)[j], csub = 3) s.class(w4[[j]][, 1:2], w3[[j]], clab = 2, axese = FALSE, add.plot = TRUE) } par(mfrow = c(1, 1)) }
Performs a particular case of a Principal Component Analysis with respect to Instrumental Variables (pcaiv), in which there is only a single factor as explanatory variable.
## S3 method for class 'dudi' bca(x, fac, scannf = TRUE, nf = 2, ...)
## S3 method for class 'dudi' bca(x, fac, scannf = TRUE, nf = 2, ...)
x |
a duality diagram, object of class |
fac |
a factor partitioning the rows of |
scannf |
a logical value indicating whether the eigenvalues barplot should be displayed |
nf |
if scannf FALSE, a numeric value indicating the number of kept axes |
... |
further arguments passed to or from other methods |
Returns a list of class dudi
, subclass 'between' containing
tab |
a data frame class-variables containing the means per class for each variable |
cw |
a numeric vector of the column weigths |
lw |
a numeric vector of the class weigths |
eig |
a numeric vector with all the eigenvalues |
rank |
the rank of the analysis |
nf |
an integer value indicating the number of kept axes |
c1 |
a data frame with the column normed scores |
l1 |
a data frame with the class normed scores |
co |
a data frame with the column coordinates |
li |
a data frame with the class coordinates |
call |
the matching call |
ratio |
the bewteen-class inertia percentage |
ls |
a data frame with the row coordinates |
as |
a data frame containing the projection of inertia axes onto between axes |
To avoid conflict names with the base:::within
function, the
function within
is now deprecated and removed. To be
consistent, the between
function is also deprecated and
is replaced by the method bca.dudi
of the new generic bca
function.
Daniel Chessel
Anne-Béatrice Dufour [email protected]
Dolédec, S. and Chessel, D. (1987) Rythmes saisonniers et composantes stationnelles en milieu aquatique I- Description d'un plan d'observations complet par projection de variables. Acta Oecologica, Oecologia Generalis, 8, 3, 403–426.
data(meaudret) pca1 <- dudi.pca(meaudret$env, scan = FALSE, nf = 4) pca2 <- dudi.pca(meaudret$spe, scal = FALSE, scan = FALSE, nf = 4) bet1 <- bca(pca1, meaudret$design$site, scan = FALSE, nf = 2) bet2 <- bca(pca2, meaudret$design$site, scan = FALSE, nf = 2) if(adegraphicsLoaded()) { g1 <- s.class(pca1$li, meaudret$design$site, psub.text = "Principal Component Analysis (env)", plot = FALSE) g2 <- s.class(pca2$li, meaudret$design$site, psub.text = "Principal Component Analysis (spe)", plot = FALSE) g3 <- s.class(bet1$ls, meaudret$design$site, psub.text = "Between sites PCA (env)", plot = FALSE) g4 <- s.class(bet2$ls, meaudret$design$site, psub.text = "Between sites PCA (spe)", plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) s.class(pca1$li, meaudret$design$site, sub = "Principal Component Analysis (env)", csub = 1.75) s.class(pca2$li, meaudret$design$site, sub = "Principal Component Analysis (spe)", csub = 1.75) s.class(bet1$ls, meaudret$design$site, sub = "Between sites PCA (env)", csub = 1.75) s.class(bet2$ls, meaudret$design$site, sub = "Between sites PCA (spe)", csub = 1.75) par(mfrow = c(1, 1)) } coib <- coinertia(bet1, bet2, scann = FALSE) plot(coib)
data(meaudret) pca1 <- dudi.pca(meaudret$env, scan = FALSE, nf = 4) pca2 <- dudi.pca(meaudret$spe, scal = FALSE, scan = FALSE, nf = 4) bet1 <- bca(pca1, meaudret$design$site, scan = FALSE, nf = 2) bet2 <- bca(pca2, meaudret$design$site, scan = FALSE, nf = 2) if(adegraphicsLoaded()) { g1 <- s.class(pca1$li, meaudret$design$site, psub.text = "Principal Component Analysis (env)", plot = FALSE) g2 <- s.class(pca2$li, meaudret$design$site, psub.text = "Principal Component Analysis (spe)", plot = FALSE) g3 <- s.class(bet1$ls, meaudret$design$site, psub.text = "Between sites PCA (env)", plot = FALSE) g4 <- s.class(bet2$ls, meaudret$design$site, psub.text = "Between sites PCA (spe)", plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) s.class(pca1$li, meaudret$design$site, sub = "Principal Component Analysis (env)", csub = 1.75) s.class(pca2$li, meaudret$design$site, sub = "Principal Component Analysis (spe)", csub = 1.75) s.class(bet1$ls, meaudret$design$site, sub = "Between sites PCA (env)", csub = 1.75) s.class(bet2$ls, meaudret$design$site, sub = "Between sites PCA (spe)", csub = 1.75) par(mfrow = c(1, 1)) } coib <- coinertia(bet1, bet2, scann = FALSE) plot(coib)
Performs a between-class analysis after a coinertia analysis
## S3 method for class 'coinertia' bca(x, fac, scannf = TRUE, nf = 2, ...)
## S3 method for class 'coinertia' bca(x, fac, scannf = TRUE, nf = 2, ...)
x |
a coinertia analysis (object of class coinertia) obtained by the function coinertia |
fac |
a factor partitioning the rows in classes |
scannf |
a logical value indicating whether the eigenvalues barplot should be displayed |
nf |
if scannf FALSE, an integer indicating the number of kept axes |
... |
further arguments passed to or from other methods |
This analysis is equivalent to do a between-class analysis on each initial dudi, and a coinertia analysis on the two between analyses. This function returns additional outputs for the interpretation.
An object of the class betcoi
. Outputs are described by the
print
function
To avoid conflict names with the base:::within
function, the
function within
is now deprecated and removed. To be
consistent, the betweencoinertia
function is also deprecated and
is replaced by the method bca.coinertia
of the new generic bca
function.
Stéphane Dray [email protected] and Jean Thioulouse [email protected]
Franquet E., Doledec S., and Chessel D. (1995) Using multivariate analyses for separating spatial and temporal effects within species-environment relationships. Hydrobiologia, 300, 425–431.
data(meaudret) pca1 <- dudi.pca(meaudret$env, scan = FALSE, nf = 4) pca2 <- dudi.pca(meaudret$spe, scal = FALSE, scan = FALSE, nf = 4) bet1 <- bca(pca1, meaudret$design$site, scan = FALSE, nf = 2) bet2 <- bca(pca2, meaudret$design$site, scan = FALSE, nf = 2) coib <- coinertia(bet1, bet2, scannf = FALSE) coi <- coinertia(pca1, pca2, scannf = FALSE, nf = 3) coi.b <- bca(coi,meaudret$design$site, scannf = FALSE) ## coib and coi.b are equivalent plot(coi.b)
data(meaudret) pca1 <- dudi.pca(meaudret$env, scan = FALSE, nf = 4) pca2 <- dudi.pca(meaudret$spe, scal = FALSE, scan = FALSE, nf = 4) bet1 <- bca(pca1, meaudret$design$site, scan = FALSE, nf = 2) bet2 <- bca(pca2, meaudret$design$site, scan = FALSE, nf = 2) coib <- coinertia(bet1, bet2, scannf = FALSE) coi <- coinertia(pca1, pca2, scannf = FALSE, nf = 3) coi.b <- bca(coi,meaudret$design$site, scannf = FALSE) ## coib and coi.b are equivalent plot(coi.b)
Performs a particular RLQ analysis where a partition of sites (rows of R) is taken into account. The between-class RLQ analysis search for linear combinations of traits and environmental variables maximizing the covariances between the traits and the average environmental conditions of classes.
## S3 method for class 'rlq' bca(x, fac, scannf = TRUE, nf = 2, ...) ## S3 method for class 'betrlq' plot(x, xax = 1, yax = 2, ...) ## S3 method for class 'betrlq' print(x, ...)
## S3 method for class 'rlq' bca(x, fac, scannf = TRUE, nf = 2, ...) ## S3 method for class 'betrlq' plot(x, xax = 1, yax = 2, ...) ## S3 method for class 'betrlq' print(x, ...)
x |
an object of class rlq (created by the |
fac |
a factor partitioning the rows of R |
scannf |
a logical value indicating whether the eigenvalues bar plot should be displayed |
nf |
if scannf FALSE, an integer indicating the number of kept axes |
xax |
the column number for the x-axis |
yax |
the column number for the y-axis |
... |
further arguments passed to or from other methods |
The bca.rlq
function returns an object of class 'betrlq'
(sub-class of 'dudi'). See the outputs of the print
function
for more details.
Stéphane Dray [email protected]
Wesuls, D., Oldeland, J. and Dray, S. (2012) Disentangling plant trait responses to livestock grazing from spatio-temporal variation: the partial RLQ approach. Journal of Vegetation Science, 23, 98–113.
data(piosphere) afcL <- dudi.coa(log(piosphere$veg + 1), scannf = FALSE) acpR <- dudi.pca(piosphere$env, scannf = FALSE, row.w = afcL$lw) acpQ <- dudi.hillsmith(piosphere$traits, scannf = FALSE, row.w = afcL$cw) rlq1 <- rlq(acpR, afcL, acpQ, scannf = FALSE) brlq1 <- bca(rlq1, fac = piosphere$habitat, scannf = FALSE) brlq1 plot(brlq1)
data(piosphere) afcL <- dudi.coa(log(piosphere$veg + 1), scannf = FALSE) acpR <- dudi.pca(piosphere$env, scannf = FALSE, row.w = afcL$lw) acpQ <- dudi.hillsmith(piosphere$traits, scannf = FALSE, row.w = afcL$cw) rlq1 <- rlq(acpR, afcL, acpQ, scannf = FALSE) brlq1 <- bca(rlq1, fac = piosphere$habitat, scannf = FALSE) brlq1 plot(brlq1)
Outputs and graphical representations of the results of a between-class analysis.
## S3 method for class 'between' plot(x, xax = 1, yax = 2, ...) ## S3 method for class 'between' print(x, ...) ## S3 method for class 'betcoi' plot(x, xax = 1, yax = 2, ...) ## S3 method for class 'betcoi' print(x, ...) ## S3 method for class 'between' summary(object, ...)
## S3 method for class 'between' plot(x, xax = 1, yax = 2, ...) ## S3 method for class 'between' print(x, ...) ## S3 method for class 'betcoi' plot(x, xax = 1, yax = 2, ...) ## S3 method for class 'betcoi' print(x, ...) ## S3 method for class 'between' summary(object, ...)
x , object
|
an object of class |
xax , yax
|
the column index of the x-axis and the y-axis |
... |
further arguments passed to or from other methods |
Daniel Chessel
Anne-Béatrice Dufour [email protected]
Stéphane Dray [email protected]
Dolédec, S. and Chessel, D. (1987) Rythmes saisonniers et composantes stationnelles en milieu aquatique I- Description d'un plan d'observations complet par projection de variables. Acta Oecologica, Oecologia Generalis, 8, 3, 403–426.
data(meaudret) pca1 <- dudi.pca(meaudret$env, scan = FALSE, nf = 4) pca2 <- dudi.pca(meaudret$spe, scal = FALSE, scan = FALSE, nf = 4) bet1 <- bca(pca1, meaudret$design$site, scan = FALSE, nf = 2) bet2 <- bca(pca2, meaudret$design$site, scan = FALSE, nf = 2) if(adegraphicsLoaded()) { g1 <- s.class(pca1$li, meaudret$design$site, psub.text = "Principal Component Analysis (env)", plot = FALSE) g2 <- s.class(pca2$li, meaudret$design$site, psub.text = "Principal Component Analysis (spe)", plot = FALSE) g3 <- s.class(bet1$ls, meaudret$design$site, psub.text = "Between sites PCA (env)", plot = FALSE) g4 <- s.class(bet2$ls, meaudret$design$site, psub.text = "Between sites PCA (spe)", plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) s.class(pca1$li, meaudret$design$site, sub = "Principal Component Analysis (env)", csub = 1.75) s.class(pca2$li, meaudret$design$site, sub = "Principal Component Analysis (spe)", csub = 1.75) s.class(bet1$ls, meaudret$design$site, sub = "Between sites PCA (env)", csub = 1.75) s.class(bet2$ls, meaudret$design$site, sub = "Between sites PCA (spe)", csub = 1.75) par(mfrow = c(1,1)) } coib <- coinertia(bet1, bet2, scann = FALSE) plot(coib)
data(meaudret) pca1 <- dudi.pca(meaudret$env, scan = FALSE, nf = 4) pca2 <- dudi.pca(meaudret$spe, scal = FALSE, scan = FALSE, nf = 4) bet1 <- bca(pca1, meaudret$design$site, scan = FALSE, nf = 2) bet2 <- bca(pca2, meaudret$design$site, scan = FALSE, nf = 2) if(adegraphicsLoaded()) { g1 <- s.class(pca1$li, meaudret$design$site, psub.text = "Principal Component Analysis (env)", plot = FALSE) g2 <- s.class(pca2$li, meaudret$design$site, psub.text = "Principal Component Analysis (spe)", plot = FALSE) g3 <- s.class(bet1$ls, meaudret$design$site, psub.text = "Between sites PCA (env)", plot = FALSE) g4 <- s.class(bet2$ls, meaudret$design$site, psub.text = "Between sites PCA (spe)", plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) s.class(pca1$li, meaudret$design$site, sub = "Principal Component Analysis (env)", csub = 1.75) s.class(pca2$li, meaudret$design$site, sub = "Principal Component Analysis (spe)", csub = 1.75) s.class(bet1$ls, meaudret$design$site, sub = "Between sites PCA (env)", csub = 1.75) s.class(bet2$ls, meaudret$design$site, sub = "Between sites PCA (spe)", csub = 1.75) par(mfrow = c(1,1)) } coib <- coinertia(bet1, bet2, scann = FALSE) plot(coib)
bf88
is a list of 6 data frames corresponding to 6 stages of vegetation.
Each data frame gives some bird species informations for 4 counties.
data(bf88)
data(bf88)
A list of six data frames with 79 rows (bird species) and 4 columns (counties).
The 6 arrays (S1 to S6) are the 6 stages of vegetation.
The attribut 'nomesp' of this list is a vector of species French names.
Blondel, J. and Farre, H. (1988) The convergent trajectories of bird communities along ecological successions in european forests. Oecologia (Berlin), 75, 83–93.
data(bf88) fou1 <- foucart(bf88, scann = FALSE, nf = 3) fou1 if(adegraphicsLoaded()) { g1 <- scatter(fou1, plot = FALSE) g2 <- s.traject(fou1$Tco, fou1$TC[, 1], plines.lty = 1:length(levels(fou1$TC[, 1])), plot = FALSE) g3 <- s.traject(fou1$Tco, fou1$TC[, 2], plines.lty = 1:length(levels(fou1$TC[, 2])), plot = FALSE) g41 <- s.label(fou1$Tco, plot = FALSE) g42 <- s.label(fou1$co, plab.cex = 2, plot = FALSE) g4 <- superpose(g41, g42, plot = FALSE) G1 <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) G2 <- kplot(fou1, row.plab.cex = 0, psub.cex = 2) } else { par(mfrow = c(2,2)) scatter(fou1) s.traject(fou1$Tco, fou1$TC[, 1]) s.traject(fou1$Tco, fou1$TC[, 2]) s.label(fou1$Tco) s.label(fou1$co, add.p = TRUE, clab = 2) par(mfrow = c(1, 1)) kplot(fou1, clab.c = 2, clab.r = 0, csub = 3) }
data(bf88) fou1 <- foucart(bf88, scann = FALSE, nf = 3) fou1 if(adegraphicsLoaded()) { g1 <- scatter(fou1, plot = FALSE) g2 <- s.traject(fou1$Tco, fou1$TC[, 1], plines.lty = 1:length(levels(fou1$TC[, 1])), plot = FALSE) g3 <- s.traject(fou1$Tco, fou1$TC[, 2], plines.lty = 1:length(levels(fou1$TC[, 2])), plot = FALSE) g41 <- s.label(fou1$Tco, plot = FALSE) g42 <- s.label(fou1$co, plab.cex = 2, plot = FALSE) g4 <- superpose(g41, g42, plot = FALSE) G1 <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) G2 <- kplot(fou1, row.plab.cex = 0, psub.cex = 2) } else { par(mfrow = c(2,2)) scatter(fou1) s.traject(fou1$Tco, fou1$TC[, 1]) s.traject(fou1$Tco, fou1$TC[, 2]) s.label(fou1$Tco) s.label(fou1$co, add.p = TRUE, clab = 2) par(mfrow = c(1, 1)) kplot(fou1, clab.c = 2, clab.r = 0, csub = 3) }
This function creates a doubly centred matrix.
bicenter.wt(X, row.wt = rep(1, nrow(X)), col.wt = rep(1, ncol(X)))
bicenter.wt(X, row.wt = rep(1, nrow(X)), col.wt = rep(1, ncol(X)))
X |
a matrix with n rows and p columns |
row.wt |
a vector of positive or null weights of length n |
col.wt |
a vector of positive or null weights of length p |
returns a doubly centred matrix
Daniel Chessel
w <- matrix(1:6, 3, 2) bicenter.wt(w, c(0.2,0.6,0.2), c(0.3,0.7)) w <- matrix(1:20, 5, 4) sum(bicenter.wt(w, runif(5), runif(4))^2)
w <- matrix(1:6, 3, 2) bicenter.wt(w, c(0.2,0.6,0.2), c(0.3,0.7)) w <- matrix(1:20, 5, 4) sum(bicenter.wt(w, runif(5), runif(4))^2)
The bordeaux
data frame gives the opinions of 200 judges in a blind tasting of five different types of claret
(red wine from the Bordeaux area in the south western parts of France).
data(bordeaux)
data(bordeaux)
This data frame has 5 rows (the wines) and 4 columns (the judgements) divided in excellent, good, mediocre and boring.
van Rijckevorsel, J. (1987) The application of fuzzy coding and horseshoes in multiple correspondence analysis. DSWO Press, Leiden (p. 32)
data(bordeaux) bordeaux score(dudi.coa(bordeaux, scan = FALSE))
data(bordeaux) bordeaux score(dudi.coa(bordeaux, scan = FALSE))
This data set gives ecological and biological characteristics of 131 species of aquatic insects.
data(bsetal97)
data(bsetal97)
bsetal97
is a list of 8 components.
is a vector of the names of aquatic insects.
is a data frame containing the taxonomy of species: genus, family and order.
is a data frame containing 10 biological traits for a total of 41 modalities.
is a vector of the numbers of items for each biological trait.
is a vector of the names of the biological traits.
is a data frame with 7 ecological traits for a total of 34 modalities.
is a vector of the numbers of items for each ecological trait.
is a vector of the names of the ecological traits.
The 10 variables of the data frame bsetal97$biol
are called in bsetal97$biol.blo.names
and the number of modalities per variable given in bsetal97$biol.blo
. The variables are:
female size - the body length from the front of the head to the end of the abdomen (7 length modalities),
egg length - the egg size (6 modalities), egg number - count of eggs actually oviposited,
generations per year (3 modalities: , 2, > 2),
oviposition period - the length of time during which oviposition occurred (3 modalities:
months,
between 2 and 5 months, > 5 months), incubation time - the time between oviposition and hatching of the larvae
(3 modalities:
weeks, between 4 and 12 weeks, > 12 weeks), egg shape (1-spherical, 2-oval, 3-cylindrical),
egg attachment - physiological feature of the egg and of the female (4 modalities), clutch structure (1-single eggs, 2-grouped eggs,
3-egg masses), clutch number (3 modalities : 1, 2, > 2).
The 7 variables of the data frame bsetal97$ecol
are called in bsetal97$ecol.blo.names
and the number of modalities per variable given in bsetal97$ecol.blo
. The variables are:
oviposition site - position relative to the water (7 modalities), substratum type for eggs - the substratum to which
the eggs are definitely attached (6 modalities), egg deposition - the position of the eggs during the oviposition process (4 modalities),
gross habitat - the general habitat use of the species such as temporary waters or estuaries (8 modalities), saturation variance -
the exposure of eggs to the risk of dessication (2 modalities), time of day (1-morning, 2-day, 3-evening, 4-night),
season - time of the year (1-Spring, 2-Summer, 3-Automn).
Statzner, B., Hoppenhaus, K., Arens, M.-F. and Richoux, P. (1997) Reproductive traits, habitat use and templet theory: a synthesis of world-wide data on aquatic insects. Freshwater Biology, 38, 109–135.
See a data description at http://pbil.univ-lyon1.fr/R/pdf/pps029.pdf (in French).
data(bsetal97) X <- prep.fuzzy.var(bsetal97$biol, bsetal97$biol.blo) Y <- prep.fuzzy.var(bsetal97$ecol, bsetal97$ecol.blo) plot(coinertia(dudi.fca(X, scan = FALSE), dudi.fca(Y, scan = FALSE), scan = FALSE))
data(bsetal97) X <- prep.fuzzy.var(bsetal97$biol, bsetal97$biol.blo) Y <- prep.fuzzy.var(bsetal97$ecol, bsetal97$ecol.blo) plot(coinertia(dudi.fca(X, scan = FALSE), dudi.fca(Y, scan = FALSE), scan = FALSE))
This data set contains informations about Buech basin characteristics.
data(buech)
data(buech)
buech
is a list with the following components:
a data frame with 10 environmental variables collected on 31 sites in Juin (1984)
a data frame with 10 environmental variables collected on 31 sites in September (1984)
a data frame with the coordinates of the sites
a data frame for background map
the neighbouring graph between sites, object of the class nb
an object of the class SpatialPolygons
of sp
,
containing the map
Variables of buech$tab1
and buech$tab2
are the following ones:
pH ;
Conductivity ( S/cm) ;
Carbonate (water hardness (mg/l CaCO3)) ;
hardness (total water hardness (mg/l CaCO3)) ;
Bicarbonate (alcalinity (mg/l HCO3-)) ;
Chloride (alcalinity (mg/l Cl-)) ;
Suspens (particles in suspension (mg/l)) ;
Organic (organic particles (mg/l)) ;
Nitrate (nitrate rate (mg/l NO3-)) ;
Ammonia (amoniac rate (mg/l NH4-))
Vespini, F. (1985) Contribution à l'étude hydrobiologique du Buech, rivière non aménagée de Haute-Provence. Thèse de troisième cycle, Université de Provence.
Vespini, F., Légier, P. and Champeau, A. (1987) Ecologie d'une rivière non aménagée des Alpes du Sud : Le Buëch (France) I. Evolution longitudinale des descripteurs physiques et chimiques. Annales de Limnologie, 23, 151–164.
data(buech) if(adegraphicsLoaded()) { if(requireNamespace("sp", quietly = TRUE)) { g1 <- s.label(buech$xy, Sp = buech$Spatial, nb = buech$nb, pSp.col = "transparent", plot = FALSE) g2 <- s.value(buech$xy, buech$tab2$Suspens - buech$tab1$Suspens, Sp = buech$Spatial, nb = buech$nb, pSp.col = "transparent", plot = FALSE) G <- cbindADEg(g1, g2, plot = TRUE) } }
data(buech) if(adegraphicsLoaded()) { if(requireNamespace("sp", quietly = TRUE)) { g1 <- s.label(buech$xy, Sp = buech$Spatial, nb = buech$nb, pSp.col = "transparent", plot = FALSE) g2 <- s.value(buech$xy, buech$tab2$Suspens - buech$tab1$Suspens, Sp = buech$Spatial, nb = buech$nb, pSp.col = "transparent", plot = FALSE) G <- cbindADEg(g1, g2, plot = TRUE) } }
This data set contains environmental and genetics informations about 16 Euphydryas editha butterfly colonies studied in California and Oregon.
data(butterfly)
data(butterfly)
butterfly
is a list with the following components:
a data frame with the two coordinates of the 16 Euphydryas editha butterfly colonies
a environmental data frame of 16 sites - 4 variables
a genetics data frame of 16 sites - 6 allele frequencies
a data frame for background map (California map)
an object of the class SpatialPolygons
of sp
,
containing the map
McKechnie, S.W., Ehrlich, P.R. and White, R.R. (1975). Population genetics of Euphydryas butterflies. I. Genetic variation and the neutrality hypothesis. Genetics, 81, 571–594.
Manly, B.F. (1994) Multivariate Statistical Methods. A primer. Second edition. Chapman & Hall, London. 1–215.
data(butterfly) if(adegraphicsLoaded()) { if(requireNamespace("sp", quietly = TRUE)) { g1 <- s.label(butterfly$xy, Sp = butterfly$Spatial, pSp.col = "white", porigin.include = FALSE, plot = FALSE) g2 <- table.value(dist(butterfly$xy), plot = FALSE) g3 <- s.value(butterfly$xy, dudi.pca(butterfly$envir, scan = FALSE)$li[, 1], Sp = butterfly$Spatial, pori.inc = FALSE, pSp.col = "transparent", ppoints.cex = 2, plot = FALSE) ## mt <- mantel.randtest(dist(butterfly$xy), dist(butterfly$gen), 99) G <- ADEgS(list(g1, g2, g3), layout = c(2, 2), plot = TRUE) } } else { par(mfrow = c(2, 2)) s.label(butterfly$xy, contour = butterfly$contour, inc = FALSE) table.dist(dist(butterfly$xy), labels = row.names(butterfly$xy)) # depends of mva s.value(butterfly$xy, dudi.pca(butterfly$envir, scan = FALSE)$li[,1], contour = butterfly$contour, inc = FALSE, csi = 3) plot(mantel.randtest(dist(butterfly$xy), dist(butterfly$gen), 99), main = "genetic/spatial") par(mfrow = c(1,1)) }
data(butterfly) if(adegraphicsLoaded()) { if(requireNamespace("sp", quietly = TRUE)) { g1 <- s.label(butterfly$xy, Sp = butterfly$Spatial, pSp.col = "white", porigin.include = FALSE, plot = FALSE) g2 <- table.value(dist(butterfly$xy), plot = FALSE) g3 <- s.value(butterfly$xy, dudi.pca(butterfly$envir, scan = FALSE)$li[, 1], Sp = butterfly$Spatial, pori.inc = FALSE, pSp.col = "transparent", ppoints.cex = 2, plot = FALSE) ## mt <- mantel.randtest(dist(butterfly$xy), dist(butterfly$gen), 99) G <- ADEgS(list(g1, g2, g3), layout = c(2, 2), plot = TRUE) } } else { par(mfrow = c(2, 2)) s.label(butterfly$xy, contour = butterfly$contour, inc = FALSE) table.dist(dist(butterfly$xy), labels = row.names(butterfly$xy)) # depends of mva s.value(butterfly$xy, dudi.pca(butterfly$envir, scan = FALSE)$li[,1], contour = butterfly$contour, inc = FALSE, csi = 3) plot(mantel.randtest(dist(butterfly$xy), dist(butterfly$gen), 99), main = "genetic/spatial") par(mfrow = c(1,1)) }
These functions allow to study the variations in diversity among communities (as in dpcoa) taking into account a partition in classes
bwca.dpcoa(x, fac, cofac, scannf = TRUE, nf = 2, ...) ## S3 method for class 'dpcoa' bca(x, fac, scannf = TRUE, nf = 2, ...) ## S3 method for class 'dpcoa' wca(x, fac, scannf = TRUE, nf = 2, ...) ## S3 method for class 'betwit' randtest(xtest, nrepet = 999, ...) ## S3 method for class 'betwit' summary(object, ...) ## S3 method for class 'witdpcoa' print(x, ...) ## S3 method for class 'betdpcoa' print(x, ...)
bwca.dpcoa(x, fac, cofac, scannf = TRUE, nf = 2, ...) ## S3 method for class 'dpcoa' bca(x, fac, scannf = TRUE, nf = 2, ...) ## S3 method for class 'dpcoa' wca(x, fac, scannf = TRUE, nf = 2, ...) ## S3 method for class 'betwit' randtest(xtest, nrepet = 999, ...) ## S3 method for class 'betwit' summary(object, ...) ## S3 method for class 'witdpcoa' print(x, ...) ## S3 method for class 'betdpcoa' print(x, ...)
x |
an object of class |
fac |
a factor partitioning the collections in classes |
scannf |
a logical value indicating whether the eigenvalues barplot should be displayed |
nf |
if scannf FALSE, a numeric value indicating the number of kept axes |
... |
further arguments passed to or from other methods |
cofac |
a cofactor partitioning the collections in classes used as a covariable |
nrepet |
the number of permutations |
xtest , object
|
an object of class |
Objects of class betdpcoa
, witdpcoa
or betwit
Stéphane Dray [email protected]
Dray, S., Pavoine, S. and Aguirre de Carcer, D. (2015) Considering external information to improve the phylogenetic comparison of microbial communities: a new approach based on constrained Double Principal Coordinates Analysis (cDPCoA). Molecular Ecology Resources, 15, 242–249. doi:10.1111/1755-0998.12300
## Not run: ## First example of Dray et al (2015) paper con <- url("https://pbil.univ-lyon1.fr/datasets/dray/MER2014/soilmicrob.rda") load(con) close(con) ## Partial CCA coa <- dudi.coa(soilmicrob$OTU, scannf = FALSE) wcoa <- wca(coa, soilmicrob$env$pH, scannf = FALSE) wbcoa <- bca(wcoa,soilmicrob$env$VegType, scannf = FALSE) ## Classical DPCoA dp <- dpcoa(soilmicrob$OTU, soilmicrob$dphy, RaoDecomp = FALSE, scannf = FALSE) ## Between DPCoA (focus on the effect of vegetation type) bdp <- bca(dp, fac = soilmicrob$env$VegType , scannf = FALSE) bdp$ratio ## 0.2148972 randtest(bdp) ## p = 0.001 ## Within DPCoA (remove the effect of pH) wdp <- wca(dp, fac = soilmicrob$env$pH, scannf = FALSE) wdp$ratio ## 0.5684348 ## Between Within-DPCoA (remove the effect of pH and focus on vegetation type) wbdp <- bwca.dpcoa(dp, fac = soilmicrob$env$VegType, cofac = soilmicrob$env$pH, scannf = FALSE) wbdp$ratio ## 0.05452813 randtest(wbdp) ## p = 0.001 ## End(Not run)
## Not run: ## First example of Dray et al (2015) paper con <- url("https://pbil.univ-lyon1.fr/datasets/dray/MER2014/soilmicrob.rda") load(con) close(con) ## Partial CCA coa <- dudi.coa(soilmicrob$OTU, scannf = FALSE) wcoa <- wca(coa, soilmicrob$env$pH, scannf = FALSE) wbcoa <- bca(wcoa,soilmicrob$env$VegType, scannf = FALSE) ## Classical DPCoA dp <- dpcoa(soilmicrob$OTU, soilmicrob$dphy, RaoDecomp = FALSE, scannf = FALSE) ## Between DPCoA (focus on the effect of vegetation type) bdp <- bca(dp, fac = soilmicrob$env$VegType , scannf = FALSE) bdp$ratio ## 0.2148972 randtest(bdp) ## p = 0.001 ## Within DPCoA (remove the effect of pH) wdp <- wca(dp, fac = soilmicrob$env$pH, scannf = FALSE) wdp$ratio ## 0.5684348 ## Between Within-DPCoA (remove the effect of pH and focus on vegetation type) wbdp <- bwca.dpcoa(dp, fac = soilmicrob$env$VegType, cofac = soilmicrob$env$pH, scannf = FALSE) wbdp$ratio ## 0.05452813 randtest(wbdp) ## p = 0.001 ## End(Not run)
This function computes the smallest positive constant that makes Euclidean a distance matrix and applies it.
cailliez(distmat, print = FALSE, tol = 1e-07, cor.zero = TRUE)
cailliez(distmat, print = FALSE, tol = 1e-07, cor.zero = TRUE)
distmat |
an object of class |
print |
if TRUE, prints the eigenvalues of the matrix |
tol |
a tolerance threshold for zero |
cor.zero |
if TRUE, zero distances are not modified |
an object of class dist
containing a Euclidean distance matrix.
Daniel Chessel
Stéphane Dray [email protected]
Cailliez, F. (1983) The analytical solution of the additive constant problem. Psychometrika, 48, 305–310.
Legendre, P. and Anderson, M.J. (1999) Distance-based redundancy analysis: testing multispecies responses in multifactorial ecological experiments. Ecological Monographs, 69, 1–24.
Legendre, P., and Legendre, L. (1998) Numerical ecology, 2nd English edition edition. Elsevier Science BV, Amsterdam.
data(capitales) d0 <- capitales$dist is.euclid(d0) # FALSE d1 <- cailliez(d0, TRUE) # Cailliez constant = 2429.87867 is.euclid(d1) # TRUE plot(d0, d1) abline(lm(unclass(d1)~unclass(d0))) print(coefficients(lm(unclass(d1)~unclass(d0))), dig = 8) # d1 = d + Cte is.euclid(d0 + 2428) # FALSE is.euclid(d0 + 2430) # TRUE the smallest constant
data(capitales) d0 <- capitales$dist is.euclid(d0) # FALSE d1 <- cailliez(d0, TRUE) # Cailliez constant = 2429.87867 is.euclid(d1) # TRUE plot(d0, d1) abline(lm(unclass(d1)~unclass(d0))) print(coefficients(lm(unclass(d1)~unclass(d0))), dig = 8) # d1 = d + Cte is.euclid(d0 + 2428) # FALSE is.euclid(d0 + 2430) # TRUE the smallest constant
This data set gives the road distances between 15 European capitals and their coordinates.
data(capitales)
data(capitales)
capitales
is a list with the following components:
a data frame containing the coordinates of capitals
a data frame containing three variables, designed to be used in area.plot function
a list of pixmap objects, each one symbolizing a capital
an object of the class SpatialPolygons
of sp
,
containing the map
a dist object the road distances between 15 European capitals
data(capitales) attr(capitales$dist, "Labels") index <- pmatch(tolower(attr(capitales$dist, "Labels")), names(capitales$logo)) w1 <- capitales$area if(adegraphicsLoaded()) { if(requireNamespace("sp", quietly = TRUE)) { g1 <- s.label(capitales$xy, lab = rownames(capitales$xy), porigin.include = FALSE, plot = FALSE) g2 <- s.logo(capitales$xy[sort(rownames(capitales$xy)), ], capitales$logo, Sp = capitales$Spatial, pbackground.col = "lightblue", pSp.col = "white", pgrid.draw = FALSE, plot = FALSE) g3 <- table.value(capitales$dist, ptable.margin = list(b = 5, l = 5, t = 15, r = 15), ptable.x.tck = 3, ptable.y.tck = 3, plot = FALSE) g4 <- s.logo(pcoscaled(lingoes(capitales$dist)), capitales$logo[index], plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } } else { if(requireNamespace("pixmap", quietly = TRUE)) { par(mfrow = c(2, 2)) s.label(capitales$xy, lab = attr(capitales$dist, "Labels"), include.origin = FALSE) area.plot(w1) rect(min(w1$x), min(w1$y), max(w1$x), max(w1$y), col = "lightblue") invisible(lapply(split(w1, w1$id), function(x) polygon(x[, -1], col = "white"))) s.logo(capitales$xy, capitales$logo, klogo = index, add.plot = TRUE, include.origin = FALSE, clogo = 0.5) # depends on pixmap table.dist(capitales$dist, lab = attr(capitales$dist, "Labels")) # depends on mva s.logo(pcoscaled(lingoes(capitales$dist)), capitales$logo, klogo = index, clogo = 0.5) # depends on pixmap par(mfrow = c(1, 1)) } }
data(capitales) attr(capitales$dist, "Labels") index <- pmatch(tolower(attr(capitales$dist, "Labels")), names(capitales$logo)) w1 <- capitales$area if(adegraphicsLoaded()) { if(requireNamespace("sp", quietly = TRUE)) { g1 <- s.label(capitales$xy, lab = rownames(capitales$xy), porigin.include = FALSE, plot = FALSE) g2 <- s.logo(capitales$xy[sort(rownames(capitales$xy)), ], capitales$logo, Sp = capitales$Spatial, pbackground.col = "lightblue", pSp.col = "white", pgrid.draw = FALSE, plot = FALSE) g3 <- table.value(capitales$dist, ptable.margin = list(b = 5, l = 5, t = 15, r = 15), ptable.x.tck = 3, ptable.y.tck = 3, plot = FALSE) g4 <- s.logo(pcoscaled(lingoes(capitales$dist)), capitales$logo[index], plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } } else { if(requireNamespace("pixmap", quietly = TRUE)) { par(mfrow = c(2, 2)) s.label(capitales$xy, lab = attr(capitales$dist, "Labels"), include.origin = FALSE) area.plot(w1) rect(min(w1$x), min(w1$y), max(w1$x), max(w1$y), col = "lightblue") invisible(lapply(split(w1, w1$id), function(x) polygon(x[, -1], col = "white"))) s.logo(capitales$xy, capitales$logo, klogo = index, add.plot = TRUE, include.origin = FALSE, clogo = 0.5) # depends on pixmap table.dist(capitales$dist, lab = attr(capitales$dist, "Labels")) # depends on mva s.logo(pcoscaled(lingoes(capitales$dist)), capitales$logo, klogo = index, clogo = 0.5) # depends on pixmap par(mfrow = c(1, 1)) } }
This data set describes the phylogeny of carnivora as reported by Diniz-Filho et al. (1998). It also gives the body mass of these 19 species.
data(carni19)
data(carni19)
carni19
is a list containing the 2 following objects :
is a character string giving the phylogenetic tree in Newick format.
is a numeric vector which values correspond to the body mass of the 19 species (log scale).
Diniz-Filho, J. A. F., de Sant'Ana, C.E.R. and Bini, L.M. (1998) An eigenvector method for estimating phylogenetic inertia. Evolution, 52, 1247–1262.
data(carni19) carni19.phy <- newick2phylog(carni19$tre) par(mfrow = c(1,2)) symbols.phylog(carni19.phy,carni19$bm-mean(carni19$bm)) dotchart.phylog(carni19.phy, carni19$bm, clabel.l=0.75) par(mfrow = c(1,1))
data(carni19) carni19.phy <- newick2phylog(carni19$tre) par(mfrow = c(1,2)) symbols.phylog(carni19.phy,carni19$bm-mean(carni19$bm)) dotchart.phylog(carni19.phy, carni19$bm, clabel.l=0.75) par(mfrow = c(1,1))
This data set describes the phylogeny of 70 carnivora as reported by Diniz-Filho and Torres (2002). It also gives the geographic range size and body size corresponding to these 70 species.
data(carni70)
data(carni70)
carni70
is a list containing the 2 following objects:
is a character string giving the phylogenetic tree in Newick format. Branch lengths are expressed as divergence times (millions of years)
is a data frame with 70 species and two traits: size (body size (kg)) ; range (geographic range size (km)).
Diniz-Filho, J. A. F., and N. M. Tôrres. (2002) Phylogenetic comparative methods and the geographic range size-body size relationship in new world terrestrial carnivora. Evolutionary Ecology, 16, 351–367.
## Not run: if (requireNamespace("adephylo", quietly = TRUE) & requireNamespace("ape", quietly = TRUE)) { data(carni70) carni70.phy <- newick2phylog(carni70$tre) plot(carni70.phy) size <- scalewt(log(carni70$tab))[,1] names(size) <- row.names(carni70$tab) symbols.phylog(carni70.phy,size) tre <- ape::read.tree(text = carni70$tre) adephylo::orthogram(size, tre = tre) yrange <- scalewt(carni70$tab[,2]) names(yrange) <- row.names(carni70$tab) symbols.phylog(carni70.phy,yrange) adephylo::orthogram(as.vector(yrange), tre = tre) if(adegraphicsLoaded()) { g1 <- s.label(cbind.data.frame(size, yrange), plabel.cex = 0) g2 <- addhist(g1) } else { s.hist(cbind.data.frame(size, yrange), clabel = 0) } } ## End(Not run)
## Not run: if (requireNamespace("adephylo", quietly = TRUE) & requireNamespace("ape", quietly = TRUE)) { data(carni70) carni70.phy <- newick2phylog(carni70$tre) plot(carni70.phy) size <- scalewt(log(carni70$tab))[,1] names(size) <- row.names(carni70$tab) symbols.phylog(carni70.phy,size) tre <- ape::read.tree(text = carni70$tre) adephylo::orthogram(size, tre = tre) yrange <- scalewt(carni70$tab[,2]) names(yrange) <- row.names(carni70$tab) symbols.phylog(carni70.phy,yrange) adephylo::orthogram(as.vector(yrange), tre = tre) if(adegraphicsLoaded()) { g1 <- s.label(cbind.data.frame(size, yrange), plabel.cex = 0) g2 <- addhist(g1) } else { s.hist(cbind.data.frame(size, yrange), clabel = 0) } } ## End(Not run)
This data set describes the taxonomic and phylogenetic relationships of 49 carnivora and herbivora species as reported by Garland and Janis (1993) and Garland et al. (1993). It also gives seven traits corresponding to these 49 species.
data(carniherbi49)
data(carniherbi49)
carniherbi49
is a list containing the 5 following objects :
is a data frame with 49 species and 2 columns : 'fam', a factor family with 14 levels and 'ord', a factor order with 3 levels.
is a character string giving the phylogenetic tree in Newick format as reported by Garland et al. (1993).
is a character string giving the phylogenetic tree in Newick format as reported by Garland and Janis (1993).
is a data frame with 49 species and 2 traits: 'bodymass' (body mass (kg)) and 'homerange' (home range (km)).
is a data frame with 49 species and 5 traits: 'clade' (dietary with two levels Carnivore
and Herbivore
), 'runningspeed' (maximal sprint running speed (km/h)), 'bodymass' (body mass (kg)),
'hindlength' (hind limb length (cm)) and 'mtfratio' (metatarsal/femur ratio).
Garland, T., Dickerman, A. W., Janis, C. M. and Jones, J. A. (1993) Phylogenetic analysis of covariance by computer simulation. Systematics Biology, 42, 265–292.
Garland, T. J. and Janis, C.M. (1993) Does metatarsal-femur ratio predict maximal running speed in cursorial mammals? Journal of Zoology, 229, 133–151.
## Not run: data(carniherbi49) par(mfrow=c(1,3)) plot(newick2phylog(carniherbi49$tre1), clabel.leaves = 0, f.phylog = 2, sub ="article 1") plot(newick2phylog(carniherbi49$tre2), clabel.leaves = 0, f.phylog = 2, sub = "article 2") taxo <- as.taxo(carniherbi49$taxo) plot(taxo2phylog(taxo), clabel.nodes = 1.2, clabel.leaves = 1.2) par(mfrow = c(1,1)) ## End(Not run)
## Not run: data(carniherbi49) par(mfrow=c(1,3)) plot(newick2phylog(carniherbi49$tre1), clabel.leaves = 0, f.phylog = 2, sub ="article 1") plot(newick2phylog(carniherbi49$tre2), clabel.leaves = 0, f.phylog = 2, sub = "article 2") taxo <- as.taxo(carniherbi49$taxo) plot(taxo2phylog(taxo), clabel.nodes = 1.2, clabel.leaves = 1.2) par(mfrow = c(1,1)) ## End(Not run)
This data set is a data frame with 74 rows (mice) and 15 columns (loci enzymatic polymorphism of the DNA mitochondrial). Each value contains 6 characters coding for two allelles. The missing values are coding by '000000'.
data(casitas)
data(casitas)
The 74 individuals of casitas
belong to 4 groups:
24 mice of the sub-species Mus musculus domesticus
11 mice of the sub-species Mus musculus castaneus
9 mice of the sub-species Mus musculus musculus
30 mice from a population of the lake Casitas (California)
Exemple du logiciel GENETIX.
Belkhir k. et al. GENETIX, logiciel sous WindowsTM pour la génétique des populations.
Laboratoire Génome, Populations, Interactions CNRS UMR 5000, Université de Montpellier II, Montpellier (France).
https://kimura.univ-montp2.fr/genetix/
Orth, A., T. Adama, W. Din and F. Bonhomme. (1998) Hybridation naturelle entre deux sous espèces de souris domestique Mus musculus domesticus et Mus musculus castaneus près de Lake Casitas (Californie). Genome, 41, 104–110.
data(casitas) str(casitas) names(casitas)
data(casitas) str(casitas) names(casitas)
This data set gives the age, the fecundity and the number of litters for 26 groups of cats.
data(chatcat)
data(chatcat)
chatcat
is a list of two objects :
is a data frame with 3 factors (age, feco, nport).
is a vector of numbers.
One row of tab
corresponds to one group of cats.
The value in eff
is the number of cats in this group.
Pontier, D. (1984) Contribution à la biologie et à la génétique des populations de chats domestiques (Felis catus). Thèse de 3ème cycle. Université Lyon 1, p. 67.
data(chatcat) summary(chatcat$tab) w <- acm.disjonctif(chatcat$tab) # Disjonctive table names(w) <- c(paste("A", 1:5, sep = ""), paste("B", 1:5, sep = ""), paste("C", 1:2, sep = "")) w <- t(w*chatcat$num) %*% as.matrix(w) w <- data.frame(w) w # BURT table
data(chatcat) summary(chatcat$tab) w <- acm.disjonctif(chatcat$tab) # Disjonctive table names(w) <- c(paste("A", 1:5, sep = ""), paste("B", 1:5, sep = ""), paste("C", 1:2, sep = "")) w <- t(w*chatcat$num) %*% as.matrix(w) w <- data.frame(w) w # BURT table
This data set is a contingency table of age classes and fecundity classes of cats Felis catus.
data(chats)
data(chats)
chats
is a data frame with 8 rows and 8 columns.
The 8 rows are age classes (age1, ..., age8).
The 8 columns are fecundity classes (f0, f12, f34, ..., fcd).
The values are cats numbers (contingency table).
Legay, J.M. and Pontier, D. (1985) Relation âge-fécondité dans les populations de Chats domestiques, Felis catus. Mammalia, 49, 395–402.
data(chats) chatsw <- as.table(t(chats)) chatscoa <- dudi.coa(data.frame(t(chats)), scann = FALSE) if(adegraphicsLoaded()) { g1 <- table.value(chatsw, ppoints.cex = 1.3, meanX = TRUE, ablineX = TRUE, plabel.cex = 1.5, plot = FALSE) g2 <- table.value(chatsw, ppoints.cex = 1.3, meanY = TRUE, ablineY = TRUE, plabel.cex = 1.5, plot = FALSE) g3 <- table.value(chatsw, ppoints.cex = 1.3, coordsx = chatscoa$c1[, 1], coordsy = chatscoa$l1[, 1], meanX = TRUE, ablineX = TRUE, plot = FALSE) g4 <- table.value(chatsw, ppoints.cex = 1.3, meanY = TRUE, ablineY = TRUE, coordsx = chatscoa$c1[, 1], coordsy = chatscoa$l1[, 1], plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) table.cont(chatsw, abmean.x = TRUE, csi = 2, abline.x = TRUE, clabel.r = 1.5, clabel.c = 1.5) table.cont(chatsw, abmean.y = TRUE, csi = 2, abline.y = TRUE, clabel.r = 1.5, clabel.c = 1.5) table.cont(chatsw, x = chatscoa$c1[, 1], y = chatscoa$l1[, 1], abmean.x = TRUE, csi = 2, abline.x = TRUE, clabel.r = 1.5, clabel.c = 1.5) table.cont(chatsw, x = chatscoa$c1[, 1], y = chatscoa$l1[, 1], abmean.y = TRUE, csi = 2, abline.y = TRUE, clabel.r = 1.5, clabel.c = 1.5) par(mfrow = c(1, 1)) }
data(chats) chatsw <- as.table(t(chats)) chatscoa <- dudi.coa(data.frame(t(chats)), scann = FALSE) if(adegraphicsLoaded()) { g1 <- table.value(chatsw, ppoints.cex = 1.3, meanX = TRUE, ablineX = TRUE, plabel.cex = 1.5, plot = FALSE) g2 <- table.value(chatsw, ppoints.cex = 1.3, meanY = TRUE, ablineY = TRUE, plabel.cex = 1.5, plot = FALSE) g3 <- table.value(chatsw, ppoints.cex = 1.3, coordsx = chatscoa$c1[, 1], coordsy = chatscoa$l1[, 1], meanX = TRUE, ablineX = TRUE, plot = FALSE) g4 <- table.value(chatsw, ppoints.cex = 1.3, meanY = TRUE, ablineY = TRUE, coordsx = chatscoa$c1[, 1], coordsy = chatscoa$l1[, 1], plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) table.cont(chatsw, abmean.x = TRUE, csi = 2, abline.x = TRUE, clabel.r = 1.5, clabel.c = 1.5) table.cont(chatsw, abmean.y = TRUE, csi = 2, abline.y = TRUE, clabel.r = 1.5, clabel.c = 1.5) table.cont(chatsw, x = chatscoa$c1[, 1], y = chatscoa$l1[, 1], abmean.x = TRUE, csi = 2, abline.x = TRUE, clabel.r = 1.5, clabel.c = 1.5) table.cont(chatsw, x = chatscoa$c1[, 1], y = chatscoa$l1[, 1], abmean.y = TRUE, csi = 2, abline.y = TRUE, clabel.r = 1.5, clabel.c = 1.5) par(mfrow = c(1, 1)) }
This data set gives six different weights of 23 charolais and zebu oxen.
data(chazeb)
data(chazeb)
chazeb
is a list of 2 components.
is a data frame with 23 rows and 6 columns.
is a factor with two levels "cha" and "zeb".
Tomassone, R., Danzard, M., Daudin, J. J. and Masson J. P. (1988) Discrimination et classement, Masson, Paris. p. 43
data(chazeb) if(!adegraphicsLoaded()) plot(discrimin(dudi.pca(chazeb$tab, scan = FALSE), chazeb$cla, scan = FALSE))
data(chazeb) if(!adegraphicsLoaded()) plot(discrimin(dudi.pca(chazeb$tab, scan = FALSE), chazeb$cla, scan = FALSE))
This data set contains a list of three components: spatial map, allellic profiles and sample sizes.
data(chevaine)
data(chevaine)
This data set is a list of three components:
a data frame with 27 populations and 9 allelic frequencies (4 locus)
a list containing all the elements to build a spatial map
a numeric containing the numbers of fish samples per station
Guinand B., Bouvet Y. and Brohon B. (1996) Spatial aspects of genetic differentiation of the European chub in the Rhone River basin. Journal of Fish Biology, 49, 714–726.
See a data description at http://pbil.univ-lyon1.fr/R/pdf/pps054.pdf (in French).
data(chevaine) names(chevaine) str(chevaine)
data(chevaine) names(chevaine) str(chevaine)
This data set contains information about potential risk factors for losses in broiler chickens
data(chickenk)
data(chickenk)
A list with 5 components:
a data frame with 351 observations and 4 variables which describe the losses (dependent dataset Y)
a data frame with 351 observations and 5 variables which describe the farm structure (explanatory dataset)
a data frame with 351 observations and 4 variables which describe the flock characteristics at placement (explanatory dataset)
a data frame with 351 observations and 6 variables which describe the flock characteristics during the rearing period (explanatory dataset)
a data frame with 351 observations and 5 variables which describe the transport, lairage conditions, slaughterhouse and inspection features (explanatory dataset)
Lupo C., le Bouquin S., Balaine L., Michel V., Peraste J., Petetin I., Colin P. and Chauvin C. (2009) Feasibility of screening broiler chicken flocks for risk markers as an aid for meat inspection. Epidemiology and Infection, 137, 1086-1098
data(chickenk) kta1 <- ktab.list.df(chickenk)
data(chickenk) kta1 <- ktab.list.df(chickenk)
The clementines
is a data set containing the fruit production of 20 clementine trees during 15 years.
data(clementines)
data(clementines)
A data frame with 15 rows and 20 columns
Tisné-Agostini, D. (1988) Description par analyse en composantes principales de l'évolution de la production du clémentinier en association avec 12 types de porte-greffe. Rapport technique, DEA Analyse et modélisation des systèmes biologiques, Université Lyon 1.
data(clementines) op <- par(no.readonly = TRUE) par(mfrow = c(5, 4)) par(mar = c(2, 2, 1, 1)) for(i in 1:20) { w0 <- 1:15 plot(w0, clementines[, i], type = "b") abline(lm(clementines[, i] ~ w0)) } par(op) pca1 <- dudi.pca(clementines, scan = FALSE) if(adegraphicsLoaded()) { g1 <- s.corcircle(pca1$co, plab.cex = 0.75) g2 <- s1d.barchart(pca1$li[, 1], p1d.hori = FALSE) } else { s.corcircle(pca1$co, clab = 0.75) barplot(pca1$li[, 1]) } op <- par(no.readonly = TRUE) par(mfrow = c(5, 4)) par(mar = c(2, 2, 1, 1)) clem0 <- pca1$tab croi <- 1:15 alter <- c(rep(c(1, -1), 7), 1) for(i in 1:20) { y <- clem0[,i] plot(w0, y, type = "b", ylim = c(-2, 2)) z <- predict(lm(clem0[, i] ~ croi * alter)) points(w0, z, pch = 20, cex = 2) for(j in 1:15) segments(j, y[j], j, z[j]) } par(op) par(mfrow = c(1, 1))
data(clementines) op <- par(no.readonly = TRUE) par(mfrow = c(5, 4)) par(mar = c(2, 2, 1, 1)) for(i in 1:20) { w0 <- 1:15 plot(w0, clementines[, i], type = "b") abline(lm(clementines[, i] ~ w0)) } par(op) pca1 <- dudi.pca(clementines, scan = FALSE) if(adegraphicsLoaded()) { g1 <- s.corcircle(pca1$co, plab.cex = 0.75) g2 <- s1d.barchart(pca1$li[, 1], p1d.hori = FALSE) } else { s.corcircle(pca1$co, clab = 0.75) barplot(pca1$li[, 1]) } op <- par(no.readonly = TRUE) par(mfrow = c(5, 4)) par(mar = c(2, 2, 1, 1)) clem0 <- pca1$tab croi <- 1:15 alter <- c(rep(c(1, -1), 7), 1) for(i in 1:20) { y <- clem0[,i] plot(w0, y, type = "b", ylim = c(-2, 2)) z <- predict(lm(clem0[, i] ~ croi * alter)) points(w0, z, pch = 20, cex = 2) for(j in 1:15) segments(j, y[j], j, z[j]) } par(op) par(mfrow = c(1, 1))
cnc2003
is a data frame with 94 rows (94 departments from continental Metropolitan France)and 12 variables.
data(cnc2003)
data(cnc2003)
This data frame contains the following variables:
is the population department in million inhabitants.
is the number of movie theater visitors in million.
is the takings from ticket offices.
is the number of proposed shows in thousands.
is the number of equipped communes in movie theaters (units).
is the number of active movie theaters (units).
is the number of active screens.
is the number of proposed seats.
is the number of movie theaters offering "Art and Essay" movies.
is the number of active multiplexes.
is the name of the department.
is the administrative region of the department.
National Center of Cinematography (CNC), september 2003
This dataset is compatible with elec88
and presid2002
data(cnc2003) sco.quant(cnc2003$popu, cnc2003[,2:10], abline = TRUE, csub = 3)
data(cnc2003) sco.quant(cnc2003$popu, cnc2003[,2:10], abline = TRUE, csub = 3)
The coinertia analysis performs a double inertia analysis of two tables.
coinertia(dudiX, dudiY, scannf = TRUE, nf = 2) ## S3 method for class 'coinertia' plot(x, xax = 1, yax = 2, ...) ## S3 method for class 'coinertia' print(x, ...) ## S3 method for class 'coinertia' summary(object, ...)
coinertia(dudiX, dudiY, scannf = TRUE, nf = 2) ## S3 method for class 'coinertia' plot(x, xax = 1, yax = 2, ...) ## S3 method for class 'coinertia' print(x, ...) ## S3 method for class 'coinertia' summary(object, ...)
dudiX |
a duality diagram providing from one of the functions dudi.coa, dudi.pca, ... |
dudiY |
a duality diagram providing from one of the functions dudi.coa, dudi.pca, ... |
scannf |
a logical value indicating whether the eigenvalues bar plot should be displayed |
nf |
if scannf FALSE, an integer indicating the number of kept axes |
x , object
|
an object of class 'coinertia' |
xax , yax
|
the numbers of the x-axis and the y-axis |
... |
further arguments passed to or from other methods |
Returns a list of class 'coinertia', sub-class 'dudi' containing:
call |
call |
rank |
rank |
nf |
a numeric value indicating the number of kept axes |
RV |
a numeric value, the RV coefficient |
eig |
a numeric vector with all the eigenvalues |
lw |
a numeric vector with the rows weigths (crossed table) |
cw |
a numeric vector with the columns weigths (crossed table) |
tab |
a crossed table (CT) |
li |
CT row scores (cols of dudiY) |
l1 |
Principal components (loadings for cols of dudiY) |
co |
CT col scores (cols of dudiX) |
c1 |
Principal axes (cols of dudiX) |
lX |
Row scores (rows of dudiX) |
mX |
Normed row scores (rows of dudiX) |
lY |
Row scores (rows of dudiY) |
mY |
Normed row scores (rows of dudiY) |
aX |
Correlations between dudiX axes and coinertia axes |
aY |
Correlations between dudiY axes and coinertia axes |
IMPORTANT : dudi1
and dudi2
must have identical row weights.
Daniel Chessel
Anne-Béatrice Dufour [email protected]
Dolédec, S. and Chessel, D. (1994) Co-inertia analysis: an alternative method for studying species-environment relationships.
Freshwater Biology, 31, 277–294.
Dray, S., Chessel, D. and J. Thioulouse (2003) Co-inertia analysis and the linking of the ecological data tables. Ecology, 84, 11, 3078–3089.
data(doubs) dudi1 <- dudi.pca(doubs$env, scale = TRUE, scan = FALSE, nf = 3) dudi2 <- dudi.pca(doubs$fish, scale = FALSE, scan = FALSE, nf = 2) coin1 <- coinertia(dudi1,dudi2, scan = FALSE, nf = 2) coin1 summary(coin1) if(adegraphicsLoaded()) { g1 <- s.arrow(coin1$l1, plab.cex = 0.7) g2 <- s.arrow(coin1$c1, plab.cex = 0.7) g3 <- s.corcircle(coin1$aX, plot = FALSE) g4 <- s.corcircle(coin1$aY, plot = FALSE) cbindADEg(g3, g4, plot = TRUE) g5 <- plot(coin1) } else { s.arrow(coin1$l1, clab = 0.7) s.arrow(coin1$c1, clab = 0.7) par(mfrow = c(1,2)) s.corcircle(coin1$aX) s.corcircle(coin1$aY) par(mfrow = c(1,1)) plot(coin1) }
data(doubs) dudi1 <- dudi.pca(doubs$env, scale = TRUE, scan = FALSE, nf = 3) dudi2 <- dudi.pca(doubs$fish, scale = FALSE, scan = FALSE, nf = 2) coin1 <- coinertia(dudi1,dudi2, scan = FALSE, nf = 2) coin1 summary(coin1) if(adegraphicsLoaded()) { g1 <- s.arrow(coin1$l1, plab.cex = 0.7) g2 <- s.arrow(coin1$c1, plab.cex = 0.7) g3 <- s.corcircle(coin1$aX, plot = FALSE) g4 <- s.corcircle(coin1$aY, plot = FALSE) cbindADEg(g3, g4, plot = TRUE) g5 <- plot(coin1) } else { s.arrow(coin1$l1, clab = 0.7) s.arrow(coin1$c1, clab = 0.7) par(mfrow = c(1,2)) s.corcircle(coin1$aX) s.corcircle(coin1$aY) par(mfrow = c(1,1)) plot(coin1) }
This data set coleo (coleoptera) is a a fuzzy biological traits table.
data(coleo)
data(coleo)
coleo
is a list of 5 components.
is a data frame with 110 rows (species) and 32 columns (categories).
is a vector of species names.
is a vector of fuzzy variables names.
is a factor species family.
is a vector containing the number of categories of each trait.
Bournaud, M., Richoux, P. and Usseglio-Polatera, P. (1992) An approach to the synthesis of qualitative ecological information from aquatic coleoptera communities. Regulated rivers: Research and Management, 7, 165–180.
data(coleo) op <- par(no.readonly = TRUE) coleo.fuzzy <- prep.fuzzy.var(coleo$tab, coleo$col.blocks) fca1 <- dudi.fca(coleo.fuzzy, sca = FALSE, nf = 3) indica <- factor(rep(names(coleo$col), coleo$col)) if(adegraphicsLoaded()) { glist <- list() for(i in levels(indica)) { df <- coleo$tab[, which(indica == i)] names(df) <- coleo$moda.names[which(indica == i)] glist[i] <- s.distri(fca1$l1, df, psub.text = as.character(i), ellipseSize = 0, starSize = 0.5, plot = FALSE, storeData = TRUE) } G <- ADEgS(glist, layout = c(3, 3)) } else { par(mfrow = c(3, 3)) for(j in levels(indica)) s.distri(fca1$l1, coleo$tab[, which(indica == j)], clab = 1.5, sub = as.character(j), cell = 0, csta = 0.5, csub = 3, label = coleo$moda.names[which(indica == j)]) par(op) par(mfrow = c(1, 1)) }
data(coleo) op <- par(no.readonly = TRUE) coleo.fuzzy <- prep.fuzzy.var(coleo$tab, coleo$col.blocks) fca1 <- dudi.fca(coleo.fuzzy, sca = FALSE, nf = 3) indica <- factor(rep(names(coleo$col), coleo$col)) if(adegraphicsLoaded()) { glist <- list() for(i in levels(indica)) { df <- coleo$tab[, which(indica == i)] names(df) <- coleo$moda.names[which(indica == i)] glist[i] <- s.distri(fca1$l1, df, psub.text = as.character(i), ellipseSize = 0, starSize = 0.5, plot = FALSE, storeData = TRUE) } G <- ADEgS(glist, layout = c(3, 3)) } else { par(mfrow = c(3, 3)) for(j in levels(indica)) s.distri(fca1$l1, coleo$tab[, which(indica == j)], clab = 1.5, sub = as.character(j), cell = 0, csta = 0.5, csub = 3, label = coleo$moda.names[which(indica == j)]) par(op) par(mfrow = c(1, 1)) }
Functions to combine and adjust the outputs of the fourthcorner
and
randtest.rlq
functions created using permutational models 2 and
4 (sequential approach).
combine.randtest.rlq(obj1, obj2, ...) combine.4thcorner(four1,four2) p.adjust.4thcorner(x, p.adjust.method.G = p.adjust.methods, p.adjust.method.D = p.adjust.methods, p.adjust.D = c("global", "levels"))
combine.randtest.rlq(obj1, obj2, ...) combine.4thcorner(four1,four2) p.adjust.4thcorner(x, p.adjust.method.G = p.adjust.methods, p.adjust.method.D = p.adjust.methods, p.adjust.D = c("global", "levels"))
four1 |
an object of the class 4thcorner created with modeltype = 2 (or 4) |
four2 |
an object of the class 4thcorner created with modeltype = 4 (or 2) |
obj1 |
an object created with |
obj2 |
an object created with |
x |
an object of the class 4thcorner |
p.adjust.method.G |
a string indicating a method for multiple
adjustment used for output tabG, see |
p.adjust.method.D |
a string indicating a method for multiple
adjustment used for output tabD/tabD2, see |
p.adjust.D |
a string indicating if multiple adjustment for tabD/tabD2 should be done globally or only between levels of a factor ("levels", as in the original paper of Legendre et al. 1997) |
... |
further arguments passed to or from other methods |
The functions combines the outputs of two objects (created by
fourthcorner
and randtest.rlq
functions) as described in
Dray and Legendre (2008) and ter Braak et al (2012).
The functions return objects of the same class than their argument. They simply create a new object where pvalues are equal to the maximum of pvalues of the two arguments.
Stéphane Dray [email protected]
Dray, S. and Legendre, P. (2008) Testing the species traits-environment relationships: the fourth-corner problem revisited. Ecology, 89, 3400–3412.
ter Braak, C., Cormont, A., and Dray, S. (2012) Improved testing of species traits-environment relationships in the fourth corner problem. Ecology, 93, 1525–1526.
rlq
, fourthcorner
, p.adjust.methods
data(aravo) four2 <- fourthcorner(aravo$env, aravo$spe, aravo$traits, nrepet=99,modeltype=2) four4 <- fourthcorner(aravo$env, aravo$spe, aravo$traits, nrepet=99,modeltype=4) four.comb <- combine.4thcorner(four2,four4) ## or directly : ## four.comb <- fourthcorner(aravo$env, aravo$spe, aravo$traits, nrepet=99,modeltype=6) summary(four.comb) plot(four.comb, stat = "G")
data(aravo) four2 <- fourthcorner(aravo$env, aravo$spe, aravo$traits, nrepet=99,modeltype=2) four4 <- fourthcorner(aravo$env, aravo$spe, aravo$traits, nrepet=99,modeltype=4) four.comb <- combine.4thcorner(four2,four4) ## or directly : ## four.comb <- fourthcorner(aravo$env, aravo$spe, aravo$traits, nrepet=99,modeltype=6) summary(four.comb) plot(four.comb, stat = "G")
The mantelkdist and RVkdist functions apply to blocks of distance matrices the mantel.rtest and RV.rtest functions.
mantelkdist (kd, nrepet = 999, ...) RVkdist (kd, nrepet = 999, ...) ## S3 method for class 'corkdist' plot(x, whichinrow = NULL, whichincol = NULL, gap = 4, nclass = 10,...)
mantelkdist (kd, nrepet = 999, ...) RVkdist (kd, nrepet = 999, ...) ## S3 method for class 'corkdist' plot(x, whichinrow = NULL, whichincol = NULL, gap = 4, nclass = 10,...)
kd |
a list of class |
nrepet |
the number of permutations |
x |
an objet of class |
whichinrow |
a vector of integers to select the graphs in rows (if NULL all the graphs are computed) |
whichincol |
a vector of integers to select the graphs in columns (if NULL all the graphs are computed) |
gap |
an integer to determinate the space between two graphs |
nclass |
a number of intervals for the histogram |
... |
further arguments passed to or from other methods |
The corkdist
class has some generic functions print
, plot
and summary
. The plot shows bivariate scatterplots between semi-matrices of distances or histograms of simulated values with an error position.
a list of class corkdist
containing for each pair of distances an object of class randtest
(permutation tests).
Daniel Chessel
Stéphane Dray [email protected]
data(friday87) fri.w <- ktab.data.frame(friday87$fau, friday87$fau.blo, tabnames = friday87$tab.names) fri.kc <- lapply(1:10, function(x) dist.binary(fri.w[[x]], 10)) names(fri.kc) <- substr(friday87$tab.names, 1, 4) fri.kd <- kdist(fri.kc) fri.mantel <- mantelkdist(kd = fri.kd, nrepet = 999) plot(fri.mantel, 1:5, 1:5) plot(fri.mantel, 1:5, 6:10) plot(fri.mantel, 6:10, 1:5) plot(fri.mantel, 6:10, 6:10) s.corcircle(dudi.pca(as.data.frame(fri.kd), scan = FALSE)$co) plot(RVkdist(fri.kd), 1:5, 1:5) data(yanomama) m1 <- mantelkdist(kdist(yanomama), 999) m1 summary(m1) plot(m1)
data(friday87) fri.w <- ktab.data.frame(friday87$fau, friday87$fau.blo, tabnames = friday87$tab.names) fri.kc <- lapply(1:10, function(x) dist.binary(fri.w[[x]], 10)) names(fri.kc) <- substr(friday87$tab.names, 1, 4) fri.kd <- kdist(fri.kc) fri.mantel <- mantelkdist(kd = fri.kd, nrepet = 999) plot(fri.mantel, 1:5, 1:5) plot(fri.mantel, 1:5, 6:10) plot(fri.mantel, 6:10, 1:5) plot(fri.mantel, 6:10, 6:10) s.corcircle(dudi.pca(as.data.frame(fri.kd), scan = FALSE)$co) plot(RVkdist(fri.kd), 1:5, 1:5) data(yanomama) m1 <- mantelkdist(kdist(yanomama), 999) m1 summary(m1) plot(m1)
This data set gives a morphological description of 28 species of the genus Corvus split in two habitat types and phylogeographic stocks.
data(corvus)
data(corvus)
corvus
is data frame with 28 observations (the species) and 4 variables :
: wing length (mm)
: bill length (mm)
: habitat with two levels clos
and open
: phylogeographic stock with three levels amer
(America), orien
(Oriental-Australian),
pale
(Paleoarctic-African)
Laiolo, P. and Rolando, A. (2003) The evolution of vocalisations in the genus Corvus: effects of phylogeny, morphology and habitat. Evolutionary Ecology, 17, 111–123.
data(corvus) if(adegraphicsLoaded()) { g1 <- s.label(corvus[, 1:2], plab.cex = 0, porigin.include = FALSE, pgrid.draw = FALSE, paxes.draw = TRUE, paxes.asp = "full", xlab = names(corvus)[2], ylab = names(corvus)[2], plot = FALSE) g2 <- s.class(corvus[, 1:2], corvus[, 4]:corvus[, 3], plot = FALSE) G <- superpose(g1, g2, plot = TRUE) } else { plot(corvus[, 1:2]) s.class(corvus[, 1:2], corvus[, 4]:corvus[, 3], add.p = TRUE) }
data(corvus) if(adegraphicsLoaded()) { g1 <- s.label(corvus[, 1:2], plab.cex = 0, porigin.include = FALSE, pgrid.draw = FALSE, paxes.draw = TRUE, paxes.asp = "full", xlab = names(corvus)[2], ylab = names(corvus)[2], plot = FALSE) g2 <- s.class(corvus[, 1:2], corvus[, 4]:corvus[, 3], plot = FALSE) G <- superpose(g1, g2, plot = TRUE) } else { plot(corvus[, 1:2]) s.class(corvus[, 1:2], corvus[, 4]:corvus[, 3], add.p = TRUE) }
Analysis of a series of pairs of ecological tables. This function uses Partial Triadic Analysis (pta) and coinertia to do the computations.
costatis(KTX, KTY, scannf = TRUE)
costatis(KTX, KTY, scannf = TRUE)
KTX |
an objet of class ktab |
KTY |
an objet of class ktab |
scannf |
a logical value indicating whether the eigenvalues bar plot should be displayed |
This function takes 2 ktabs. It does a PTA (partial triadic analysis: pta) on each ktab, and does a coinertia analysis (coinertia) on the compromises of the two PTAs.
a list of class coinertia, subclass dudi. See coinertia
IMPORTANT : KTX and KTY must have the same k-tables structure, the same number of columns, and the same column weights.
Jean Thioulouse [email protected]
Thioulouse J. (2011). Simultaneous analysis of a sequence of paired ecological tables: a comparison of several methods. Annals of Applied Statistics, 5, 2300-2325.
data(meau) wit1 <- withinpca(meau$env, meau$design$season, scan = FALSE, scal = "total") pcaspe <- dudi.pca(meau$spe, scale = FALSE, scan = FALSE, nf = 2) wit2 <- wca(pcaspe, meau$design$season, scan = FALSE, nf = 2) kta1 <- ktab.within(wit1, colnames = rep(c("S1","S2","S3","S4","S5","S6"), 4)) kta2 <- ktab.within(wit2, colnames = rep(c("S1","S2","S3","S4","S5","S6"), 4)) costatis1 <- costatis(kta1, kta2, scan = FALSE) plot(costatis1)
data(meau) wit1 <- withinpca(meau$env, meau$design$season, scan = FALSE, scal = "total") pcaspe <- dudi.pca(meau$spe, scale = FALSE, scan = FALSE, nf = 2) wit2 <- wca(pcaspe, meau$design$season, scan = FALSE, nf = 2) kta1 <- ktab.within(wit1, colnames = rep(c("S1","S2","S3","S4","S5","S6"), 4)) kta2 <- ktab.within(wit2, colnames = rep(c("S1","S2","S3","S4","S5","S6"), 4)) costatis1 <- costatis(kta1, kta2, scan = FALSE) plot(costatis1)
Performs a Monte-Carlo test on a Costatis analysis.
costatis.randtest(KTX, KTY, nrepet = 999, ...)
costatis.randtest(KTX, KTY, nrepet = 999, ...)
KTX |
an objet of class ktab |
KTY |
an objet of class ktab |
nrepet |
the number of permutations |
... |
further arguments passed to or from other methods |
a list of the class randtest
Jean Thioulouse [email protected]
Thioulouse J. (2011). Simultaneous analysis of a sequence of paired ecological tables: a comparison of several methods. Annals of Applied Statistics, 5, 2300-2325.
data(meau) wit1 <- withinpca(meau$env, meau$design$season, scan = FALSE, scal = "total") pcaspe <- dudi.pca(meau$spe, scale = FALSE, scan = FALSE, nf = 2) wit2 <- wca(pcaspe, meau$design$season, scan = FALSE, nf = 2) kta1 <- ktab.within(wit1, colnames = rep(c("S1","S2","S3","S4","S5","S6"), 4)) kta2 <- ktab.within(wit2, colnames = rep(c("S1","S2","S3","S4","S5","S6"), 4)) costatis1 <- costatis(kta1, kta2, scan = FALSE) costatis.randtest(kta1, kta2)
data(meau) wit1 <- withinpca(meau$env, meau$design$season, scan = FALSE, scal = "total") pcaspe <- dudi.pca(meau$spe, scale = FALSE, scan = FALSE, nf = 2) wit2 <- wca(pcaspe, meau$design$season, scan = FALSE, nf = 2) kta1 <- ktab.within(wit1, colnames = rep(c("S1","S2","S3","S4","S5","S6"), 4)) kta2 <- ktab.within(wit2, colnames = rep(c("S1","S2","S3","S4","S5","S6"), 4)) costatis1 <- costatis(kta1, kta2, scan = FALSE) costatis.randtest(kta1, kta2)
Compute Dagnelie test of multivariate normality on a data table of n objects (rows) and p variables (columns), with n > (p+1).
dagnelie.test(x)
dagnelie.test(x)
x |
Multivariate data table ( |
Dagnelie's goodness-of-fit test of multivariate normality is applicable to
multivariate data. Mahalanobis generalized distances are computed between each object and the multivariate centroid of all objects. Dagnelie’s approach is that, for multinormal data, the generalized distances should be normally distributed. The function computes a Shapiro-Wilk test of normality of the Mahalanobis distances; this is our improvement of Dagnelie’s method.
The null hypothesis (H0) is that the data are multinormal, a situation where the Mahalanobis distances should be normally distributed. In that case, the test should not reject H0, subject to type I error at the selected significance level.
Numerical simulations by D. Borcard have shown that the test had correct levels of type I error for values of n between 3p and 8p, where n is the number of objects and p is the number of variables in the data
matrix (simulations with 1 <= p <= 100). Outside that range of n values, the results were too liberal, meaning that the test rejected too often the null hypothesis of normality. For p = 2, the simulations showed the test to be valid for 6 <= n <= 13 and too liberal outside that range. If H0 is not rejected in a situation where the test is too liberal, the result is trustworthy.
Calculation of the Mahalanobis distances requires that n > p+1 (actually, n > rank+1). With fewer objects (n), all points are at equal Mahalanobis distances from the centroid in the resulting space, which has min(rank,(n-1))
dimensions. For data matrices that happen to be collinear, the function uses ginv
for inversion.
This test is not meant to be used with univariate data; in simulations, the type I error rate was higher than the 5% significance level for all values of n. Function shapiro.test
should be used in that situation.
A list containing the following results:
Shapiro.Wilk |
W statistic and p-value |
dim |
dimensions of the data matrix, n and p |
rank |
the rank of the covariance matrix |
D |
Vector containing the Mahalanobis distances of the objects to the multivariate centroid |
Daniel Borcard and Pierre Legendre
Dagnelie, P. 1975. L'analyse statistique a plusieurs variables.
Les Presses agronomiques de Gembloux, Gembloux, Belgium.
Legendre, P. and L. Legendre. 2012. Numerical ecology, 3rd English
edition. Elsevier Science BV, Amsterdam, The Netherlands.
# Example 1: 2 variables, n = 100 n <- 100; p <- 2 mat <- matrix(rnorm(n*p), n, p) (out <- dagnelie.test(mat)) # Example 2: 10 variables, n = 50 n <- 50; p <- 10 mat <- matrix(rnorm(n*p), n, p) (out <- dagnelie.test(mat)) # Example 3: 10 variables, n = 100 n <- 100; p <- 10 mat <- matrix(rnorm(n*p), n, p) (out <- dagnelie.test(mat)) # Plot a histogram of the Mahalanobis distances hist(out$D) # Example 4: 10 lognormal random variables, n = 50 n <- 50; p <- 10 mat <- matrix(round(exp(rnorm((n*p), mean = 0, sd = 2.5))), n, p) (out <- dagnelie.test(mat)) # Plot a histogram of the Mahalanobis distances hist(out$D)
# Example 1: 2 variables, n = 100 n <- 100; p <- 2 mat <- matrix(rnorm(n*p), n, p) (out <- dagnelie.test(mat)) # Example 2: 10 variables, n = 50 n <- 50; p <- 10 mat <- matrix(rnorm(n*p), n, p) (out <- dagnelie.test(mat)) # Example 3: 10 variables, n = 100 n <- 100; p <- 10 mat <- matrix(rnorm(n*p), n, p) (out <- dagnelie.test(mat)) # Plot a histogram of the Mahalanobis distances hist(out$D) # Example 4: 10 lognormal random variables, n = 50 n <- 50; p <- 10 mat <- matrix(round(exp(rnorm((n*p), mean = 0, sd = 2.5))), n, p) (out <- dagnelie.test(mat)) # Plot a histogram of the Mahalanobis distances hist(out$D)
The functions/data/methods listed below are deprecated.
The R code of the deprecated functions are stored for memory in the file ade4-deprecated.R
.
- between
: replaced by bca
- betweencoinertia
: replaced by bca.coinertia
- char2genet
: replaced by df2genind
and genind2genpop
in the adegenet
package
- count2genet
: replaced by df2genind
and genind2genpop
in the adegenet
package
- dist.genet
: replaced by dist.genpop
in the adegenet
package
- EH
: replaced by EH
in the adiv
package
- freq2genet
: replaced by df2genind
and genind2genpop
in the adegenet
package
- fuzzygenet
: replaced by df2genind
in the adegenet
package
- multispati
: replaced by multispati
in the adespatial
package
- multispati.rtest
- multispati.randtest
- optimEH
: replaced by optimEH
in the adiv
package
- orisaved
: replaced by orisaved
in the adiv
package
- orthogram
: replaced by orthogram
in the adephylo
package
- plot.multispati
: replaced by plot.multispati
in the adespatial
package
- print.multispati
: replaced by print.multispati
in the adespatial
package
- randEH
: replaced by randEH
in the adiv
package
- summary.multispati
: replaced by summary.multispati
in the adespatial
package
- within
: replaced by wca
- withincoinertia
: replaced by wca.coinertia
This data set gives the exam results of 104 students in the second year of a French University onto 9 subjects.
data(deug)
data(deug)
deug
is a list of three components.
is a data frame with 104 students and 9 subjects : Algebra, Analysis, Proba, Informatic, Economy, Option1, Option2, English, Sport.
is a factor of 104 components giving the final exam levels (A+, A, B, B-, C-, D).
is a vector of required marks by subject to get exactly 10/20 with a coefficient.
University of Lyon 1
data(deug) # decentred PCA pca1 <- dudi.pca(deug$tab, scal = FALSE, center = deug$cent, scan = FALSE) if(adegraphicsLoaded()) { g1 <- s.class(pca1$li, deug$result, plot = FALSE) g2 <- s.arrow(40 * pca1$c1, plot = FALSE) G <- superpose(g1, g2, plot = TRUE) } else { s.class(pca1$li, deug$result) s.arrow(40 * pca1$c1, add.plot = TRUE) }
data(deug) # decentred PCA pca1 <- dudi.pca(deug$tab, scal = FALSE, center = deug$cent, scan = FALSE) if(adegraphicsLoaded()) { g1 <- s.class(pca1$li, deug$result, plot = FALSE) g2 <- s.arrow(40 * pca1$c1, plot = FALSE) G <- superpose(g1, g2, plot = TRUE) } else { s.class(pca1$li, deug$result) s.arrow(40 * pca1$c1, add.plot = TRUE) }
Calculates the root square of Rao's dissimilarity coefficient between samples.
disc(samples, dis = NULL, structures = NULL)
disc(samples, dis = NULL, structures = NULL)
samples |
a data frame with elements as rows, samples as columns, and abundance, presence-absence or frequencies as entries |
dis |
an object of class |
structures |
a data frame containing, in the jth row and the kth column, the name of the group of level k to which the jth population belongs. |
Returns a list of objects of class dist
Sandrine Pavoine [email protected]
Rao, C.R. (1982) Diversity and dissimilarity coefficients: a unified approach. Theoretical Population Biology, 21, 24–43.
data(humDNAm) humDNA.dist <- disc(humDNAm$samples, sqrt(humDNAm$distances), humDNAm$structures) humDNA.dist is.euclid(humDNA.dist$samples) is.euclid(humDNA.dist$regions) ## Not run: data(ecomor) dtaxo <- dist.taxo(ecomor$taxo) ecomor.dist <- disc(ecomor$habitat, dtaxo) ecomor.dist is.euclid(ecomor.dist) ## End(Not run)
data(humDNAm) humDNA.dist <- disc(humDNAm$samples, sqrt(humDNAm$distances), humDNAm$structures) humDNA.dist is.euclid(humDNA.dist$samples) is.euclid(humDNA.dist$regions) ## Not run: data(ecomor) dtaxo <- dist.taxo(ecomor$taxo) ecomor.dist <- disc(ecomor$habitat, dtaxo) ecomor.dist is.euclid(ecomor.dist) ## End(Not run)
performs a linear discriminant analysis.
discrimin(dudi, fac, scannf = TRUE, nf = 2) ## S3 method for class 'discrimin' plot(x, xax = 1, yax = 2, ...) ## S3 method for class 'discrimin' print(x, ...)
discrimin(dudi, fac, scannf = TRUE, nf = 2) ## S3 method for class 'discrimin' plot(x, xax = 1, yax = 2, ...) ## S3 method for class 'discrimin' print(x, ...)
dudi |
a duality diagram, object of class |
fac |
a factor defining the classes of discriminant analysis |
scannf |
a logical value indicating whether the eigenvalues bar plot should be displayed |
nf |
if scannf FALSE, an integer indicating the number of kept axes |
x |
an object of class 'discrimin' |
xax |
the column number of the x-axis |
yax |
the column number of the y-axis |
... |
further arguments passed to or from other methods |
returns a list of class 'discrimin' containing :
nf |
a numeric value indicating the number of kept axes |
eig |
a numeric vector with all the eigenvalues |
fa |
a matrix with the loadings: the canonical weights |
li |
a data frame which gives the canonical scores |
va |
a matrix which gives the cosines between the variables and the canonical scores |
cp |
a matrix which gives the cosines between the components and the canonical scores |
gc |
a data frame which gives the class scores |
Daniel Chessel
Anne-Béatrice Dufour [email protected]
lda
in package MASS
data(chazeb) dis1 <- discrimin(dudi.pca(chazeb$tab, scan = FALSE), chazeb$cla, scan = FALSE) dis1 if(!adegraphicsLoaded()) plot(dis1) data(skulls) plot(discrimin(dudi.pca(skulls, scan = FALSE), gl(5,30), scan = FALSE))
data(chazeb) dis1 <- discrimin(dudi.pca(chazeb$tab, scan = FALSE), chazeb$cla, scan = FALSE) dis1 if(!adegraphicsLoaded()) plot(dis1) data(skulls) plot(discrimin(dudi.pca(skulls, scan = FALSE), gl(5,30), scan = FALSE))
performs a discriminant correspondence analysis.
discrimin.coa(df, fac, scannf = TRUE, nf = 2)
discrimin.coa(df, fac, scannf = TRUE, nf = 2)
df |
a data frame containing positive or null values |
fac |
a factor defining the classes of discriminant analysis |
scannf |
a logical value indicating whether the eigenvalues bar plot should be displayed |
nf |
if scannf FALSE, an integer indicating the number of kept axes |
a list of class discrimin
. See discrimin
Daniel Chessel
Anne-Béatrice Dufour [email protected]
Perriere, G.,Lobry, J. R. and Thioulouse J. (1996) Correspondence discriminant analysis: a multivariate method for comparing
classes of protein and nucleic acid sequences. CABIOS, 12, 519–524.
Perriere, G. and Thioulouse, J. (2003) Use of Correspondence Discriminant Analysis to predict the subcellular location of bacterial proteins. Computer Methods and Programs in Biomedicine, 70, 2, 99–105.
data(perthi02) plot(discrimin.coa(perthi02$tab, perthi02$cla, scan = FALSE))
data(perthi02) plot(discrimin.coa(perthi02$tab, perthi02$cla, scan = FALSE))
computes for binary data some distance matrice.
dist.binary(df, method = NULL, diag = FALSE, upper = FALSE)
dist.binary(df, method = NULL, diag = FALSE, upper = FALSE)
df |
a matrix or a data frame with positive or null numeric values. Used with |
method |
an integer between 1 and 10 . If NULL the choice is made with a console message. See details |
diag |
a logical value indicating whether the diagonal of the distance matrix should be printed by ‘print.dist’ |
upper |
a logical value indicating whether the upper triangle of the distance matrix should be printed by ‘print.dist’ |
Let be the contingency table of binary data such as ,
,
and
. All these distances are of type
with s a similarity coefficient.
S3 coefficient of Gower & Legendre
S4 coefficient of Gower & Legendre
S5 coefficient of Gower & Legendre
S6 coefficient of Gower & Legendre
S7 coefficient of Gower & Legendre
S9 index of Gower & Legendre (1986)
S12 coefficient of Gower & Legendre
S13 coefficient of Gower & Legendre
S14 coefficient of Gower & Legendre
returns a distance matrix of class dist
between the rows of the data frame
Daniel Chessel
Stéphane Dray [email protected]
Gower, J.C. and Legendre, P. (1986) Metric and Euclidean properties of dissimilarity coefficients. Journal of Classification, 3, 5–48.
data(aviurba) for (i in 1:10) { d <- dist.binary(aviurba$fau, method = i) cat(attr(d, "method"), is.euclid(d), "\n")}
data(aviurba) for (i in 1:10) { d <- dist.binary(aviurba$fau, method = i) cat(attr(d, "method"), is.euclid(d), "\n")}
computes for a statistical triplet a distance matrix.
dist.dudi(dudi, amongrow = TRUE)
dist.dudi(dudi, amongrow = TRUE)
dudi |
a duality diagram, object of class |
amongrow |
a logical value computing the distance if TRUE, between rows, if FALSE between columns. |
an object of class dist
Daniel Chessel
Stéphane Dray [email protected]
data (meaudret) pca1 <- dudi.pca(meaudret$env, scan = FALSE) sum((dist(scalewt(meaudret$env)) - dist.dudi(pca1))^2) #[1] 4.045e-29 the same thing
data (meaudret) pca1 <- dudi.pca(meaudret$env, scan = FALSE) sum((dist(scalewt(meaudret$env)) - dist.dudi(pca1))^2) #[1] 4.045e-29 the same thing
The mixed-variables coefficient of distance generalizes Gower's general coefficient of distance to allow the treatment of various statistical types of variables when calculating distances. This is especially important when measuring functional diversity. Indeed, most of the indices that measure functional diversity depend on variables (traits) that have various statistical types (e.g. circular, fuzzy, ordinal) and that go through a matrix of distances among species.
dist.ktab(x, type, option = c("scaledBYrange", "scaledBYsd", "noscale"), scann = FALSE, tol = 1e-8) ldist.ktab(x, type, option = c("scaledBYrange", "scaledBYsd", "noscale"), scann = FALSE, tol = 1e-8) kdist.cor(x, type, option = c("scaledBYrange", "scaledBYsd", "noscale"), scann = FALSE, tol = 1e-8, squared = TRUE) prep.fuzzy(df, col.blocks, row.w = rep(1, nrow(df)), labels = paste("F", 1:length(col.blocks), sep = "")) prep.binary(df, col.blocks, labels = paste("B", 1:length(col.blocks), sep = "")) prep.circular(df, rangemin = apply(df, 2, min, na.rm = TRUE), rangemax = apply(df, 2, max, na.rm = TRUE))
dist.ktab(x, type, option = c("scaledBYrange", "scaledBYsd", "noscale"), scann = FALSE, tol = 1e-8) ldist.ktab(x, type, option = c("scaledBYrange", "scaledBYsd", "noscale"), scann = FALSE, tol = 1e-8) kdist.cor(x, type, option = c("scaledBYrange", "scaledBYsd", "noscale"), scann = FALSE, tol = 1e-8, squared = TRUE) prep.fuzzy(df, col.blocks, row.w = rep(1, nrow(df)), labels = paste("F", 1:length(col.blocks), sep = "")) prep.binary(df, col.blocks, labels = paste("B", 1:length(col.blocks), sep = "")) prep.circular(df, rangemin = apply(df, 2, min, na.rm = TRUE), rangemax = apply(df, 2, max, na.rm = TRUE))
x |
Object of class |
type |
Vector that provide the type of each table in x. The possible types are "Q" (quantitative), "O" (ordinal), "N" (nominal), "D" (dichotomous), "F" (fuzzy, or expressed as a proportion), "B" (multichoice nominal variables, coded by binary columns), "C" (circular). Values in type must be in the same order as in x. |
option |
A string that can have three values: either "scaledBYrange" if the quantitative variables must be scaled by their range, or "scaledBYsd" if they must be scaled by their standard deviation, or "noscale" if they should not be scaled. This last option can be useful if the the values have already been normalized by the known range of the whole population instead of the observed range measured on the sample. If x contains data from various types, then the option "scaledBYsd" is not suitable (a warning will appear if the option selected with that condition). |
scann |
A logical. If TRUE, then the user will have to choose among several possible functions of distances for the quantitative, ordinal, fuzzy and binary variables. |
tol |
A tolerance threshold: a value less than tol is considered as null. |
squared |
A logical, if TRUE, the squared distances are considered. |
df |
Objet of class data.frame |
col.blocks |
A vector that contains the number of levels per variable (in the same order
as in |
row.w |
A vector of row weigths |
labels |
the names of the traits |
rangemin |
A numeric corresponding to the smallest level where the loop starts |
rangemax |
A numeric corresponding to the highest level where the loop closes |
When preparing the object of class ktab
(object x), variables of type "Q", "O", "D", "F", "B" and "C" should be of class numeric
(the class ordered
is not yet considered by dist.ktab
); variables of type "N" should be of class character
or factor
The functions provide the following results:
dist.ktab |
returns an object of class |
ldist.ktab |
returns a list of objects of class |
kdist.cor |
returns a list of three objects: "paircov" provides the covariance between traits in terms of (squared) distances between species; "paircor" provides the correlations between traits in terms of (squared) distances between species; "glocor" provides the correlations between the (squared) distances obtained for each trait and the global (squared) distances obtained by mixing all the traits (= contributions of traits to the global distances); |
prep.binary and prep.fuzzy |
returns a data frame with the following attributes: col.blocks specifies the number of columns per fuzzy variable; col.num specifies which variable each column belongs to; |
prep.circular |
returns a data frame with the following attributes: max specifies the number of levels in each circular variable. |
Sandrine Pavoine [email protected]
Pavoine S., Vallet, J., Dufour, A.-B., Gachet, S. and Daniel, H. (2009) On the challenge of treating various types of variables: Application for improving the measurement of functional diversity. Oikos, 118, 391–402. doi:10.1111/j.1600-0706.2008.16668.x
Appendix available at: http://www.oikosjournal.org/sites/oikosjournal.org/files/appendix/o16668.pdf http://www.oikosjournal.org/sites/oikosjournal.org/files/appendix/o16668_files.zip
daisy
in the case of ratio-scale (quantitative) and nominal variables;
and woangers
for an application.
# With fuzzy variables data(bsetal97) w <- prep.fuzzy(bsetal97$biol, bsetal97$biol.blo) w[1:6, 1:10] ktab1 <- ktab.list.df(list(w)) dis <- dist.ktab(ktab1, type = "F") as.matrix(dis)[1:5, 1:5] ## Not run: # With ratio-scale and multichoice variables data(ecomor) wM <- log(ecomor$morpho + 1) # Quantitative variables wD <- ecomor$diet # wD is a data frame containing a multichoice nominal variable # (diet habit), with 8 modalities (Granivorous, etc) # We must prepare it by prep.binary head(wD) wD <- prep.binary(wD, col.blocks = 8, label = "diet") wF <- ecomor$forsub # wF is also a data frame containing a multichoice nominal variable # (foraging substrat), with 6 modalities (Foliage, etc) # We must prepare it by prep.binary head(wF) wF <- prep.binary(wF, col.blocks = 6, label = "foraging") # Another possibility is to combine the two last data frames wD and wF as # they contain the same type of variables wB <- cbind.data.frame(ecomor$diet, ecomor$forsub) head(wB) wB <- prep.binary(wB, col.blocks = c(8, 6), label = c("diet", "foraging")) # The results given by the two alternatives are identical ktab2 <- ktab.list.df(list(wM, wD, wF)) disecomor <- dist.ktab(ktab2, type= c("Q", "B", "B")) as.matrix(disecomor)[1:5, 1:5] contrib2 <- kdist.cor(ktab2, type= c("Q", "B", "B")) contrib2 ktab3 <- ktab.list.df(list(wM, wB)) disecomor2 <- dist.ktab(ktab3, type= c("Q", "B")) as.matrix(disecomor2)[1:5, 1:5] contrib3 <- kdist.cor(ktab3, type= c("Q", "B")) contrib3 # With a range of variables data(woangers) traits <- woangers$traits # Nominal variables 'li', 'pr', 'lp' and 'le' # (see table 1 in the main text for the codes of the variables) tabN <- traits[,c(1:2, 7, 8)] # Circular variable 'fo' tabC <- traits[3] tabCp <- prep.circular(tabC, 1, 12) # The levels of the variable lie between 1 (January) and 12 (December). # Ordinal variables 'he', 'ae' and 'un' tabO <- traits[, 4:6] # Fuzzy variables 'mp', 'pe' and 'di' tabF <- traits[, 9:19] tabFp <- prep.fuzzy(tabF, c(3, 3, 5), labels = c("mp", "pe", "di")) # 'mp' has 3 levels, 'pe' has 3 levels and 'di' has 5 levels. # Quantitative variables 'lo' and 'lf' tabQ <- traits[, 20:21] ktab1 <- ktab.list.df(list(tabN, tabCp, tabO, tabFp, tabQ)) distrait <- dist.ktab(ktab1, c("N", "C", "O", "F", "Q")) is.euclid(distrait) contrib <- kdist.cor(ktab1, type = c("N", "C", "O", "F", "Q")) contrib dotchart(sort(contrib$glocor), labels = rownames(contrib$glocor)[order(contrib$glocor[, 1])]) ## End(Not run)
# With fuzzy variables data(bsetal97) w <- prep.fuzzy(bsetal97$biol, bsetal97$biol.blo) w[1:6, 1:10] ktab1 <- ktab.list.df(list(w)) dis <- dist.ktab(ktab1, type = "F") as.matrix(dis)[1:5, 1:5] ## Not run: # With ratio-scale and multichoice variables data(ecomor) wM <- log(ecomor$morpho + 1) # Quantitative variables wD <- ecomor$diet # wD is a data frame containing a multichoice nominal variable # (diet habit), with 8 modalities (Granivorous, etc) # We must prepare it by prep.binary head(wD) wD <- prep.binary(wD, col.blocks = 8, label = "diet") wF <- ecomor$forsub # wF is also a data frame containing a multichoice nominal variable # (foraging substrat), with 6 modalities (Foliage, etc) # We must prepare it by prep.binary head(wF) wF <- prep.binary(wF, col.blocks = 6, label = "foraging") # Another possibility is to combine the two last data frames wD and wF as # they contain the same type of variables wB <- cbind.data.frame(ecomor$diet, ecomor$forsub) head(wB) wB <- prep.binary(wB, col.blocks = c(8, 6), label = c("diet", "foraging")) # The results given by the two alternatives are identical ktab2 <- ktab.list.df(list(wM, wD, wF)) disecomor <- dist.ktab(ktab2, type= c("Q", "B", "B")) as.matrix(disecomor)[1:5, 1:5] contrib2 <- kdist.cor(ktab2, type= c("Q", "B", "B")) contrib2 ktab3 <- ktab.list.df(list(wM, wB)) disecomor2 <- dist.ktab(ktab3, type= c("Q", "B")) as.matrix(disecomor2)[1:5, 1:5] contrib3 <- kdist.cor(ktab3, type= c("Q", "B")) contrib3 # With a range of variables data(woangers) traits <- woangers$traits # Nominal variables 'li', 'pr', 'lp' and 'le' # (see table 1 in the main text for the codes of the variables) tabN <- traits[,c(1:2, 7, 8)] # Circular variable 'fo' tabC <- traits[3] tabCp <- prep.circular(tabC, 1, 12) # The levels of the variable lie between 1 (January) and 12 (December). # Ordinal variables 'he', 'ae' and 'un' tabO <- traits[, 4:6] # Fuzzy variables 'mp', 'pe' and 'di' tabF <- traits[, 9:19] tabFp <- prep.fuzzy(tabF, c(3, 3, 5), labels = c("mp", "pe", "di")) # 'mp' has 3 levels, 'pe' has 3 levels and 'di' has 5 levels. # Quantitative variables 'lo' and 'lf' tabQ <- traits[, 20:21] ktab1 <- ktab.list.df(list(tabN, tabCp, tabO, tabFp, tabQ)) distrait <- dist.ktab(ktab1, c("N", "C", "O", "F", "Q")) is.euclid(distrait) contrib <- kdist.cor(ktab1, type = c("N", "C", "O", "F", "Q")) contrib dotchart(sort(contrib$glocor), labels = rownames(contrib$glocor)[order(contrib$glocor[, 1])]) ## End(Not run)
This distance matrix between two points is the length of the shortest path between these points.
dist.neig(neig)
dist.neig(neig)
neig |
a neighbouring graph, object of class |
returns a distance matrix, object of class dist
Daniel Chessel
Stéphane Dray [email protected]
data(elec88) d0 <- dist.neig(nb2neig(elec88$nb)) plot(dist(elec88$xy),d0)
data(elec88) d0 <- dist.neig(nb2neig(elec88$nb)) plot(dist(elec88$xy),d0)
computes for percentage data some distance matrices.
dist.prop(df, method = NULL, diag = FALSE, upper = FALSE)
dist.prop(df, method = NULL, diag = FALSE, upper = FALSE)
df |
a data frame containing only positive or null values, used as row percentages |
method |
an integer between 1 and 5. If NULL the choice is made with a console message. See details |
diag |
a logical value indicating whether the diagonal of the distance matrix should be printed by ‘print.dist’ |
upper |
a logical value indicating whether the upper triangle of the distance matrix should be printed by ‘print.dist’ |
returns a distance matrix, object of class dist
Daniel Chessel
Stéphane Dray [email protected]
Edwards, A. W. F. (1971) Distance between populations on the basis of gene frequencies. Biometrics, 27, 873–881.
Manly, B. F. (1994) Multivariate Statistical Methods. A primer., Second edition. Chapman & Hall, London.
Nei, M. (1972) Genetic distances between populations. The American Naturalist, 106, 283–292.
data(microsatt) w <- microsatt$tab[1:microsatt$loci.eff[1]] if(adegraphicsLoaded()) { g1 <- scatter(dudi.pco(lingoes(dist.prop(w, 1)), scann = FALSE), plot = FALSE) g2 <- scatter(dudi.pco(lingoes(dist.prop(w, 2)), scann = FALSE), plot = FALSE) g3 <- scatter(dudi.pco(dist.prop(w, 3), scann = FALSE), plot = FALSE) g4 <- scatter(dudi.pco(lingoes(dist.prop(w, 4)), scann = FALSE), plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) scatter(dudi.pco(lingoes(dist.prop(w, 1)), scann = FALSE)) scatter(dudi.pco(lingoes(dist.prop(w, 2)), scann = FALSE)) scatter(dudi.pco(dist.prop(w, 3), scann = FALSE)) scatter(dudi.pco(lingoes(dist.prop(w, 4)), scann = FALSE)) par(mfrow = c(1, 1)) }
data(microsatt) w <- microsatt$tab[1:microsatt$loci.eff[1]] if(adegraphicsLoaded()) { g1 <- scatter(dudi.pco(lingoes(dist.prop(w, 1)), scann = FALSE), plot = FALSE) g2 <- scatter(dudi.pco(lingoes(dist.prop(w, 2)), scann = FALSE), plot = FALSE) g3 <- scatter(dudi.pco(dist.prop(w, 3), scann = FALSE), plot = FALSE) g4 <- scatter(dudi.pco(lingoes(dist.prop(w, 4)), scann = FALSE), plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) scatter(dudi.pco(lingoes(dist.prop(w, 1)), scann = FALSE)) scatter(dudi.pco(lingoes(dist.prop(w, 2)), scann = FALSE)) scatter(dudi.pco(dist.prop(w, 3), scann = FALSE)) scatter(dudi.pco(lingoes(dist.prop(w, 4)), scann = FALSE)) par(mfrow = c(1, 1)) }
computes on quantitative variables, some distance matrices as canonical, Joreskog and Mahalanobis.
dist.quant(df, method = NULL, diag = FALSE, upper = FALSE, tol = 1e-07)
dist.quant(df, method = NULL, diag = FALSE, upper = FALSE, tol = 1e-07)
df |
a data frame containing only quantitative variables |
method |
an integer between 1 and 3. If NULL the choice is made with a console message. See details |
diag |
a logical value indicating whether the diagonal of the distance matrix should be printed by ‘print.dist’ |
upper |
a logical value indicating whether the upper triangle of the distance matrix should be printed by ‘print.dist’ |
tol |
used in case 3 of |
All the distances are of type
A = Identity
A = inv(cov)
an object of class dist
Daniel Chessel
Stéphane Dray [email protected]
data(ecomor) if(adegraphicsLoaded()) { g1 <- scatter(dudi.pco(dist.quant(ecomor$morpho, 3), scan = FALSE), plot = FALSE) g2 <- scatter(dudi.pco(dist.quant(ecomor$morpho, 2), scan = FALSE), plot = FALSE) g3 <- scatter(dudi.pco(dist(scalewt(ecomor$morpho)), scan = FALSE), plot = FALSE) g4 <- scatter(dudi.pco(dist.quant(ecomor$morpho, 1), scan = FALSE), plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) scatter(dudi.pco(dist.quant(ecomor$morpho, 3), scan = FALSE)) scatter(dudi.pco(dist.quant(ecomor$morpho, 2), scan = FALSE)) scatter(dudi.pco(dist(scalewt(ecomor$morpho)), scan = FALSE)) scatter(dudi.pco(dist.quant(ecomor$morpho, 1), scan = FALSE)) par(mfrow = c(1, 1)) }
data(ecomor) if(adegraphicsLoaded()) { g1 <- scatter(dudi.pco(dist.quant(ecomor$morpho, 3), scan = FALSE), plot = FALSE) g2 <- scatter(dudi.pco(dist.quant(ecomor$morpho, 2), scan = FALSE), plot = FALSE) g3 <- scatter(dudi.pco(dist(scalewt(ecomor$morpho)), scan = FALSE), plot = FALSE) g4 <- scatter(dudi.pco(dist.quant(ecomor$morpho, 1), scan = FALSE), plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) scatter(dudi.pco(dist.quant(ecomor$morpho, 3), scan = FALSE)) scatter(dudi.pco(dist.quant(ecomor$morpho, 2), scan = FALSE)) scatter(dudi.pco(dist(scalewt(ecomor$morpho)), scan = FALSE)) scatter(dudi.pco(dist.quant(ecomor$morpho, 1), scan = FALSE)) par(mfrow = c(1, 1)) }
Calculates Rao's diversity coefficient within samples.
divc(df, dis, scale)
divc(df, dis, scale)
df |
a data frame with elements as rows, samples as columns, and abundance, presence-absence or frequencies as entries |
dis |
an object of class |
scale |
a logical value indicating whether or not the diversity coefficient should be scaled by its maximal value over all frequency distributions. |
Returns a data frame with samples as rows and the diversity coefficient within samples as columns
Sandrine Pavoine [email protected]
Rao, C.R. (1982) Diversity and dissimilarity coefficients: a unified approach. Theoretical Population Biology, 21, 24–43.
Gini, C. (1912) Variabilità e mutabilità. Universite di Cagliari III, Parte II.
Simpson, E.H. (1949) Measurement of diversity. Nature, 163, 688.
Champely, S. and Chessel, D. (2002) Measuring biological diversity using Euclidean metrics. Environmental and Ecological Statistics, 9, 167–177.
data(ecomor) dtaxo <- dist.taxo(ecomor$taxo) divc(ecomor$habitat, dtaxo) data(humDNAm) divc(humDNAm$samples, sqrt(humDNAm$distances))
data(ecomor) dtaxo <- dist.taxo(ecomor$taxo) divc(ecomor$habitat, dtaxo) data(humDNAm) divc(humDNAm$samples, sqrt(humDNAm$distances))
For a given dissimilarity matrix, this function calculates the maximal value of Rao's diversity coefficient over all frequency distribution. It uses an optimization technique based on Rosen's projection gradient algorithm and is verified using the Kuhn-Tucker conditions.
divcmax(dis, epsilon, comment)
divcmax(dis, epsilon, comment)
dis |
an object of class |
epsilon |
a tolerance threshold : a frequency is non null if it is higher than epsilon. |
comment |
a logical value indicating whether or not comments on the optimization technique should be printed. |
Returns a list
value |
the maximal value of Rao's diversity coefficient. |
vectors |
a data frame containing four frequency
distributions : |
Stéphane Champely [email protected]
Sandrine Pavoine [email protected]
Rao, C.R. (1982) Diversity and dissimilarity coefficients: a unified approach. Theoretical Population Biology, 21, 24–43.
Gini, C. (1912) Variabilità e mutabilità. Universite di Cagliari III, Parte II.
Simpson, E.H. (1949) Measurement of diversity. Nature, 163, 688.
Champely, S. and Chessel, D. (2002) Measuring biological diversity using Euclidean metrics. Environmental and Ecological Statistics, 9, 167–177.
Pavoine, S., Ollier, S. and Pontier, D. (2005) Measuring diversity from dissimilarities with Rao's quadratic entropy: are any dissimilarities suitable? Theoretical Population Biology, 67, 231–239.
data(elec88) # Dissimilarity matrix. d0 <- dist(elec88$xy/100) # Frequency distribution maximizing spatial diversity in France # according to Rao's quadratic entropy. France.m <- divcmax(d0) w0 <- France.m$vectors$num v0 <- France.m$value idx <- (1:94) [w0 > 0] if(!adegraphicsLoaded()) { # Smallest circle including all the 94 departments. # The squared radius of that circle is the maximal value of the # spatial diversity. w1 <- elec88$xy[idx, ]/100 w.c <- apply(w1 * w0[idx], 2, sum) plot(elec88$xy[, 1]/100, elec88$xy[, 2]/100, asp=1) symbols(w.c[1], w.c[2], circles = sqrt(v0), inches = FALSE, add = TRUE) s.value(elec88$xy/100, w0, add.plot = TRUE) }
data(elec88) # Dissimilarity matrix. d0 <- dist(elec88$xy/100) # Frequency distribution maximizing spatial diversity in France # according to Rao's quadratic entropy. France.m <- divcmax(d0) w0 <- France.m$vectors$num v0 <- France.m$value idx <- (1:94) [w0 > 0] if(!adegraphicsLoaded()) { # Smallest circle including all the 94 departments. # The squared radius of that circle is the maximal value of the # spatial diversity. w1 <- elec88$xy[idx, ]/100 w.c <- apply(w1 * w0[idx], 2, sum) plot(elec88$xy[, 1]/100, elec88$xy[, 2]/100, asp=1) symbols(w.c[1], w.c[2], circles = sqrt(v0), inches = FALSE, add = TRUE) s.value(elec88$xy/100, w0, add.plot = TRUE) }
dotchart.phylog
represents the phylogenetic tree and draws Cleveland dot
plot of each variable.
dotchart.phylog(phylog, values, y = NULL, scaling = TRUE, ranging = TRUE, yranging = NULL, joining = TRUE, yjoining = NULL, ceti = 1, cdot = 1, csub = 1, f.phylog = 1/(1 + ncol(values)), ...)
dotchart.phylog(phylog, values, y = NULL, scaling = TRUE, ranging = TRUE, yranging = NULL, joining = TRUE, yjoining = NULL, ceti = 1, cdot = 1, csub = 1, f.phylog = 1/(1 + ncol(values)), ...)
phylog |
an object of class |
values |
a vector or a data frame giving the variables |
y |
a vector which values correspond to leaves positions |
scaling |
if TRUE, data are scaled |
ranging |
if TRUE, dotplots are drawn with the same horizontal limits |
yranging |
a vector with two values giving the horizontal limits. If NULL, horizontal limits are defined by lower and upper values of data |
joining |
if TRUE, segments join each point to a central value |
yjoining |
a vector with the central value. If NULL, the central value equals 0 |
ceti |
a character size for editing horizontal limits, |
cdot |
a character size for plotting the points of the dot plot,
used with |
csub |
a character size for editing the names of variables, |
f.phylog |
a size coefficient for tree size (a parameter to draw the tree in proportion to leaves labels) |
... |
further arguments passed to or from other methods |
Daniel Chessel
Sébastien Ollier [email protected]
symbols.phylog
and table.phylog
# one variable tre <- c("((A,B),(C,D));") phy <- newick2phylog(tre) x <- 1:4 par(mfrow = c(2,2)) dotchart.phylog(phy, x, scaling = FALSE) dotchart.phylog(phy, x) dotchart.phylog(phy, x, joining = FALSE) dotchart.phylog(phy, x, scaling = FALSE, yjoining = 0, yranging = c(-1, 5)) par(mfrow = c(1,1)) # many variables data(mjrochet) phy <- newick2phylog(mjrochet$tre) tab <- data.frame(log(mjrochet$tab)) dotchart.phylog(phy, tab, ceti = 0.5, csub = 0.6, cleaves = 0, cdot = 0.6) par(mfrow=c(1,1))
# one variable tre <- c("((A,B),(C,D));") phy <- newick2phylog(tre) x <- 1:4 par(mfrow = c(2,2)) dotchart.phylog(phy, x, scaling = FALSE) dotchart.phylog(phy, x) dotchart.phylog(phy, x, joining = FALSE) dotchart.phylog(phy, x, scaling = FALSE, yjoining = 0, yranging = c(-1, 5)) par(mfrow = c(1,1)) # many variables data(mjrochet) phy <- newick2phylog(mjrochet$tre) tab <- data.frame(log(mjrochet$tab)) dotchart.phylog(phy, tab, ceti = 0.5, csub = 0.6, cleaves = 0, cdot = 0.6) par(mfrow=c(1,1))
This function represents n values on a circle. The n points are shared out regularly over the circle and put on the radius according to the value attributed to that measure.
dotcircle(z, alpha0 = pi/2, xlim = range(pretty(z)), labels = names(z), clabel = 1, cleg = 1)
dotcircle(z, alpha0 = pi/2, xlim = range(pretty(z)), labels = names(z), clabel = 1, cleg = 1)
z |
: a numeric vector |
alpha0 |
: polar angle to put the first value |
xlim |
: the ranges to be encompassed by the circle radius |
labels |
: a vector of strings of characters for the angle labels |
clabel |
: a character size for the labels, used with |
cleg |
: a character size for the ranges, used with |
Daniel Chessel
w <- scores.neig(neig(n.cir = 24)) par(mfrow = c(4,4)) for (k in 1:16) dotcircle(w[,k],labels = 1:24) par(mfrow = c(1,1))
w <- scores.neig(neig(n.cir = 24)) par(mfrow = c(4,4)) for (k in 1:16) dotcircle(w[,k],labels = 1:24) par(mfrow = c(1,1))
This data set gives environmental variables, fish species and spatial coordinates for 30 sites.
data(doubs)
data(doubs)
doubs
is a list with 4 components.
is a data frame with 30 rows (sites) and 11 environmental variables.
is a data frame with 30 rows (sites) and 27 fish species.
is a data frame with 30 rows (sites) and 2 spatial coordinates.
is a data frame with 27 rows (species) and 4 columns (names).
The rows of doubs$env
, doubs$fish
and doubs$xy
are 30 sites along the Doubs, a French and Switzerland river.
doubs$env
contains the following variables:
dfs - distance from the source (km * 10),
alt - altitude (m),
slo ( where x is the slope (per mil * 100),
flo - minimum average stream flow (m3/s * 100),
pH (* 10),
har - total hardness of water (mg/l of Calcium),
pho - phosphates (mg/l * 100),
nit - nitrates (mg/l * 100),
amm - ammonia nitrogen (mg/l * 100),
oxy - dissolved oxygen (mg/l * 10),
bdo - biological demand for oxygen (mg/l * 10).
doubs$fish
contains the abundance of the following fish species: Cottus gobio (Cogo), Salmo trutta fario (Satr),
Phoxinus phoxinus (Phph), Nemacheilus barbatulus (Neba), Thymallus thymallus (Thth), Telestes soufia agassizi (Teso),
Chondrostoma nasus (Chna), Chondostroma toxostoma (Chto), Leuciscus leuciscus (Lele), Leuciscus cephalus cephalus (Lece),
Barbus barbus (Baba), Spirlinus bipunctatus (Spbi), Gobio gobio (Gogo), Esox lucius (Eslu),
Perca fluviatilis (Pefl), Rhodeus amarus (Rham), Lepomis gibbosus (Legi), Scardinius erythrophtalmus (Scer),
Cyprinus carpio (Cyca), Tinca tinca (Titi), Abramis brama (Abbr), Ictalurus melas (Icme),
Acerina cernua (Acce), Rutilus rutilus (Ruru), Blicca bjoerkna (Blbj), Alburnus alburnus (Alal),
Anguilla anguilla (Anan).
doubs$species
contains the names of the 27 fish species. The four columns correspond to: 1 = scientific name (Genus species), 2 = French common name, 3 = English common name, 4 = Four character code.
Verneaux, J. (1973) Cours d'eau de Franche-Comté (Massif du Jura). Recherches écologiques sur le réseau hydrographique du Doubs. Essai de biotypologie. Thèse d'état, Besançon. 1–257.
See a French description of fish species at http://pbil.univ-lyon1.fr/R/pdf/pps047.pdf.
Chessel, D., Lebreton, J.D. and Yoccoz, N.G. (1987) Propriétés de l'analyse canonique des correspondances. Une illustration
en hydrobiologie. Revue de Statistique Appliquée, 35, 4, 55–72.
data(doubs) pca1 <- dudi.pca(doubs$env, scan = FALSE) pca2 <- dudi.pca(doubs$fish, scale = FALSE, scan = FALSE) coiner1 <- coinertia(pca1, pca2, scan = FALSE) if(adegraphicsLoaded()) { g1 <- s.corcircle(coiner1$aX, plot = FALSE) g2 <- s.value(doubs$xy, coiner1$lX[, 1], plot = FALSE) g3 <- s.value(doubs$xy, coiner1$lX[, 2], plot = FALSE) g4 <- s.arrow(coiner1$c1, plot = FALSE) g5 <- s.match(coiner1$mX, coiner1$mY, plot = FALSE) g6 <- s.corcircle(coiner1$aY, plot = FALSE) g7 <- s.arrow(coiner1$l1, plot = FALSE) g8 <- s.value(doubs$xy, coiner1$lY[, 1], plot = FALSE) g9 <- s.value(doubs$xy, coiner1$lY[, 2], plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4, g5, g6, g7, g8, g9), layout = c(3, 3)) } else { par(mfrow = c(3, 3)) s.corcircle(coiner1$aX) s.value(doubs$xy, coiner1$lX[, 1]) s.value(doubs$xy, coiner1$lX[, 2]) s.arrow(coiner1$c1) s.match(coiner1$mX, coiner1$mY) s.corcircle(coiner1$aY) s.arrow(coiner1$l1) s.value(doubs$xy, coiner1$lY[, 1]) s.value(doubs$xy, coiner1$lY[, 2]) par(mfrow = c(1, 1)) }
data(doubs) pca1 <- dudi.pca(doubs$env, scan = FALSE) pca2 <- dudi.pca(doubs$fish, scale = FALSE, scan = FALSE) coiner1 <- coinertia(pca1, pca2, scan = FALSE) if(adegraphicsLoaded()) { g1 <- s.corcircle(coiner1$aX, plot = FALSE) g2 <- s.value(doubs$xy, coiner1$lX[, 1], plot = FALSE) g3 <- s.value(doubs$xy, coiner1$lX[, 2], plot = FALSE) g4 <- s.arrow(coiner1$c1, plot = FALSE) g5 <- s.match(coiner1$mX, coiner1$mY, plot = FALSE) g6 <- s.corcircle(coiner1$aY, plot = FALSE) g7 <- s.arrow(coiner1$l1, plot = FALSE) g8 <- s.value(doubs$xy, coiner1$lY[, 1], plot = FALSE) g9 <- s.value(doubs$xy, coiner1$lY[, 2], plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4, g5, g6, g7, g8, g9), layout = c(3, 3)) } else { par(mfrow = c(3, 3)) s.corcircle(coiner1$aX) s.value(doubs$xy, coiner1$lX[, 1]) s.value(doubs$xy, coiner1$lX[, 2]) s.arrow(coiner1$c1) s.match(coiner1$mX, coiner1$mY) s.corcircle(coiner1$aY) s.arrow(coiner1$l1) s.value(doubs$xy, coiner1$lY[, 1]) s.value(doubs$xy, coiner1$lY[, 2]) par(mfrow = c(1, 1)) }
Performs a double principal coordinate analysis
dpcoa(df, dis = NULL, scannf = TRUE, nf = 2, full = FALSE, tol = 1e-07, RaoDecomp = TRUE) ## S3 method for class 'dpcoa' plot(x, xax = 1, yax = 2, ...) ## S3 method for class 'dpcoa' print(x, ...) ## S3 method for class 'dpcoa' summary(object, ...)
dpcoa(df, dis = NULL, scannf = TRUE, nf = 2, full = FALSE, tol = 1e-07, RaoDecomp = TRUE) ## S3 method for class 'dpcoa' plot(x, xax = 1, yax = 2, ...) ## S3 method for class 'dpcoa' print(x, ...) ## S3 method for class 'dpcoa' summary(object, ...)
df |
a data frame with samples as rows and categories (i.e. species) as columns and abundance or presence-absence as entries. Previous releases of ade4 (<=1.6-2) considered the transposed matrix as argument. |
dis |
an object of class |
scannf |
a logical value indicating whether the eigenvalues bar plot should be displayed |
RaoDecomp |
a logical value indicating whether Rao diversity decomposition should be performed |
nf |
if scannf is FALSE, an integer indicating the number of kept axes |
full |
a logical value indicating whether all non null eigenvalues should be kept |
tol |
a tolerance threshold for null eigenvalues (a value less than tol times the first one is considered as null) |
x , object
|
an object of class |
xax |
the column number for the x-axis |
yax |
the column number for the y-axis |
... |
|
Returns a list of class dpcoa
containing:
call |
call |
nf |
a numeric value indicating the number of kept axes |
dw |
a numeric vector containing the weights of the elements (was
|
lw |
a numeric vector containing the weights of the samples (was
|
eig |
a numeric vector with all the eigenvalues |
RaoDiv |
a numeric vector containing diversities within samples |
RaoDis |
an object of class |
RaoDecodiv |
a data frame with the decomposition of the diversity |
dls |
a data frame with the coordinates of the elements (was
|
li |
a data frame with the coordinates of the samples (was
|
c1 |
a data frame with the scores of the principal axes of the elements |
Daniel Chessel
Sandrine Pavoine [email protected]
Stéphane Dray [email protected]
Pavoine, S., Dufour, A.B. and Chessel, D. (2004) From dissimilarities among species to dissimilarities among communities: a double principal coordinate analysis. Journal of Theoretical Biology, 228, 523–537.
data(humDNAm) dpcoahum <- dpcoa(data.frame(t(humDNAm$samples)), sqrt(humDNAm$distances), scan = FALSE, nf = 2) dpcoahum if(adegraphicsLoaded()) { g1 <- plot(dpcoahum) } else { plot(dpcoahum) } ## Not run: data(ecomor) dtaxo <- dist.taxo(ecomor$taxo) dpcoaeco <- dpcoa(data.frame(t(ecomor$habitat)), dtaxo, scan = FALSE, nf = 2) dpcoaeco if(adegraphicsLoaded()) { g1 <- plot(dpcoaeco) } else { plot(dpcoaeco) } ## End(Not run)
data(humDNAm) dpcoahum <- dpcoa(data.frame(t(humDNAm$samples)), sqrt(humDNAm$distances), scan = FALSE, nf = 2) dpcoahum if(adegraphicsLoaded()) { g1 <- plot(dpcoahum) } else { plot(dpcoahum) } ## Not run: data(ecomor) dtaxo <- dist.taxo(ecomor$taxo) dpcoaeco <- dpcoa(data.frame(t(ecomor$habitat)), dtaxo, scan = FALSE, nf = 2) dpcoaeco if(adegraphicsLoaded()) { g1 <- plot(dpcoaeco) } else { plot(dpcoaeco) } ## End(Not run)
as.dudi
is called by many functions (dudi.pca
, dudi.coa
, dudi.acm
, ...)
and not directly by the user. It creates duality diagrams.
t.dudi
returns an object of class 'dudi
' where the rows are the columns and the columns are the rows
of the initial dudi
.
is.dudi
returns TRUE if the object is of class dudi
redo.dudi
computes again an analysis, eventually changing the number of kept axes. Used by other functions.
as.dudi(df, col.w, row.w, scannf, nf, call, type, tol = 1e-07, full = FALSE) ## S3 method for class 'dudi' print(x, ...) is.dudi(x) redo.dudi(dudi, newnf = 2) ## S3 method for class 'dudi' t(x) ## S3 method for class 'dudi' summary(object, ...) ## S3 method for class 'dudi' x[i,j]
as.dudi(df, col.w, row.w, scannf, nf, call, type, tol = 1e-07, full = FALSE) ## S3 method for class 'dudi' print(x, ...) is.dudi(x) redo.dudi(dudi, newnf = 2) ## S3 method for class 'dudi' t(x) ## S3 method for class 'dudi' summary(object, ...) ## S3 method for class 'dudi' x[i,j]
df |
a data frame with n rows and p columns |
col.w |
a numeric vector containing the row weights |
row.w |
a numeric vector containing the column weights |
scannf |
a logical value indicating whether the eigenvalues bar plot should be displayed |
nf |
if scannf FALSE, an integer indicating the number of kept axes |
call |
generally |
type |
a string of characters : the returned list will be of class |
tol |
a tolerance threshold for null eigenvalues (a value less than tol times the first one is considered as null) |
full |
a logical value indicating whether all non null eigenvalues should be kept |
x , dudi , object
|
objects of class |
... |
further arguments passed to or from other methods |
newnf |
an integer indicating the number of kept axes |
i , j
|
elements to extract (integer or empty): index of rows (i) and columns (j) |
as.dudi and all the functions that use it return a list with the following components :
tab |
a data frame with n rows and p columns |
cw |
column weights, a vector with n components |
lw |
row (lines) weights, a vector with p components |
eig |
eigenvalues, a vector with min(n,p) components |
nf |
integer, number of kept axes |
c1 |
principal axes, data frame with p rows and nf columns |
l1 |
principal components, data frame with n rows and nf columns |
co |
column coordinates, data frame with p rows and nf columns |
li |
row coordinates, data frame with n rows and nf columns |
call |
original call |
Daniel Chessel
Anne-Béatrice Dufour [email protected]
Stéphane Dray [email protected]
Escoufier, Y. (1987) The duality diagram : a means of better practical applications In Development in numerical ecology, Legendre, P. & Legendre, L. (Eds.) NATO advanced Institute, Serie G. Springer Verlag, Berlin, 139–156.
data(deug) dd1 <- dudi.pca(deug$tab, scannf = FALSE) dd1 t(dd1) is.dudi(dd1) redo.dudi(dd1,3) summary(dd1)
data(deug) dd1 <- dudi.pca(deug$tab, scannf = FALSE) dd1 t(dd1) is.dudi(dd1) redo.dudi(dd1,3) summary(dd1)
dudi.acm
performs the multiple correspondence analysis of a factor table.acm.burt
an utility giving the crossed Burt table of two factors table.acm.disjonctif
an utility giving the complete disjunctive table of a factor table.boxplot.acm
a graphic utility to interpret axes.
dudi.acm (df, row.w = rep(1, nrow(df)), scannf = TRUE, nf = 2) acm.burt (df1, df2, counts = rep(1, nrow(df1))) acm.disjonctif (df) ## S3 method for class 'acm' boxplot(x, xax = 1, ...)
dudi.acm (df, row.w = rep(1, nrow(df)), scannf = TRUE, nf = 2) acm.burt (df1, df2, counts = rep(1, nrow(df1))) acm.disjonctif (df) ## S3 method for class 'acm' boxplot(x, xax = 1, ...)
df , df1 , df2
|
data frames containing only factors |
row.w , counts
|
vector of row weights, by default, uniform weighting |
scannf |
a logical value indicating whether the eigenvalues bar plot should be displayed |
nf |
if scannf FALSE, an integer indicating the number of kept axes |
x |
an object of class |
xax |
the number of factor to display |
... |
further arguments passed to or from other methods |
dudi.acm
returns a list of class acm
and dudi
(see dudi) containing
cr |
a data frame which rows are the variables, columns are the kept scores and the values are the correlation ratios |
Daniel Chessel
Anne-Béatrice Dufour [email protected]
Tenenhaus, M. & Young, F.W. (1985) An analysis and synthesis of multiple correspondence analysis, optimal scaling, dual scaling, homogeneity analysis ans other methods for quantifying categorical multivariate data. Psychometrika, 50, 1, 91-119.
Lebart, L., A. Morineau, and M. Piron. 1995. Statistique exploratoire multidimensionnelle. Dunod, Paris.
data(ours) summary(ours) if(adegraphicsLoaded()) { g1 <- s1d.boxplot(dudi.acm(ours, scan = FALSE)$li[, 1], ours) } else { boxplot(dudi.acm(ours, scan = FALSE)) } ## Not run: data(banque) banque.acm <- dudi.acm(banque, scann = FALSE, nf = 3) if(adegraphicsLoaded()) { g2 <- adegraphics:::scatter.dudi(banque.acm) } else { scatter(banque.acm) } apply(banque.acm$cr, 2, mean) banque.acm$eig[1:banque.acm$nf] # the same thing if(adegraphicsLoaded()) { g3 <- s1d.boxplot(banque.acm$li[, 1], banque) g4 <- scatter(banque.acm) } else { boxplot(banque.acm) scatter(banque.acm) } s.value(banque.acm$li, banque.acm$li[,3]) bb <- acm.burt(banque, banque) bbcoa <- dudi.coa(bb, scann = FALSE) plot(banque.acm$c1[,1], bbcoa$c1[,1]) # mca and coa of Burt table. Lebart & coll. section 1.4 bd <- acm.disjonctif(banque) bdcoa <- dudi.coa(bd, scann = FALSE) plot(banque.acm$li[,1], bdcoa$li[,1]) # mca and coa of disjonctive table. Lebart & coll. section 1.4 plot(banque.acm$co[,1], dudi.coa(bd, scann = FALSE)$co[,1]) ## End(Not run)
data(ours) summary(ours) if(adegraphicsLoaded()) { g1 <- s1d.boxplot(dudi.acm(ours, scan = FALSE)$li[, 1], ours) } else { boxplot(dudi.acm(ours, scan = FALSE)) } ## Not run: data(banque) banque.acm <- dudi.acm(banque, scann = FALSE, nf = 3) if(adegraphicsLoaded()) { g2 <- adegraphics:::scatter.dudi(banque.acm) } else { scatter(banque.acm) } apply(banque.acm$cr, 2, mean) banque.acm$eig[1:banque.acm$nf] # the same thing if(adegraphicsLoaded()) { g3 <- s1d.boxplot(banque.acm$li[, 1], banque) g4 <- scatter(banque.acm) } else { boxplot(banque.acm) scatter(banque.acm) } s.value(banque.acm$li, banque.acm$li[,3]) bb <- acm.burt(banque, banque) bbcoa <- dudi.coa(bb, scann = FALSE) plot(banque.acm$c1[,1], bbcoa$c1[,1]) # mca and coa of Burt table. Lebart & coll. section 1.4 bd <- acm.disjonctif(banque) bdcoa <- dudi.coa(bd, scann = FALSE) plot(banque.acm$li[,1], bdcoa$li[,1]) # mca and coa of disjonctive table. Lebart & coll. section 1.4 plot(banque.acm$co[,1], dudi.coa(bd, scann = FALSE)$co[,1]) ## End(Not run)
performs a correspondence analysis.
dudi.coa(df, scannf = TRUE, nf = 2)
dudi.coa(df, scannf = TRUE, nf = 2)
df |
a data frame containing positive or null values |
scannf |
a logical value indicating whether the eigenvalues bar plot should be displayed |
nf |
if scannf FALSE, an integer indicating the number of kept axes |
returns a list of class coa
and dudi
(see dudi) containing
N |
the sum of all the values of the initial table |
Daniel Chessel
Anne-Béatrice Dufour [email protected]
Benzécri, J.P. and Coll. (1973) L'analyse des données. II L'analyse des correspondances, Bordas, Paris. 1–620.
Greenacre, M. J. (1984) Theory and applications of correspondence analysis, Academic Press, London.
data(rpjdl) chisq.test(rpjdl$fau)$statistic rpjdl.coa <- dudi.coa(rpjdl$fau, scannf = FALSE, nf = 4) sum(rpjdl.coa$eig)*rpjdl.coa$N # the same if(adegraphicsLoaded()) { g1 <- s.label(rpjdl.coa$co, plab.cex = 0.6, lab = rpjdl$frlab, plot = FALSE) g2 <- s.label(rpjdl.coa$li, plab.cex = 0.6, plot = FALSE) cbindADEg(g1, g2, plot = TRUE) } else { par(mfrow = c(1,2)) s.label(rpjdl.coa$co, clab = 0.6, lab = rpjdl$frlab) s.label(rpjdl.coa$li, clab = 0.6) par(mfrow = c(1,1)) } data(bordeaux) db <- dudi.coa(bordeaux, scan = FALSE) db score(db)
data(rpjdl) chisq.test(rpjdl$fau)$statistic rpjdl.coa <- dudi.coa(rpjdl$fau, scannf = FALSE, nf = 4) sum(rpjdl.coa$eig)*rpjdl.coa$N # the same if(adegraphicsLoaded()) { g1 <- s.label(rpjdl.coa$co, plab.cex = 0.6, lab = rpjdl$frlab, plot = FALSE) g2 <- s.label(rpjdl.coa$li, plab.cex = 0.6, plot = FALSE) cbindADEg(g1, g2, plot = TRUE) } else { par(mfrow = c(1,2)) s.label(rpjdl.coa$co, clab = 0.6, lab = rpjdl$frlab) s.label(rpjdl.coa$li, clab = 0.6) par(mfrow = c(1,1)) } data(bordeaux) db <- dudi.coa(bordeaux, scan = FALSE) db score(db)
performs a decentred correspondence analysis.
dudi.dec(df, eff, scannf = TRUE, nf = 2)
dudi.dec(df, eff, scannf = TRUE, nf = 2)
df |
a data frame containing positive or null values |
eff |
a vector containing the reference distribution. Its length is equal to the number of rows of df |
scannf |
a logical value indicating whether the eigenvalues bar plot should be displayed |
nf |
if scannf FALSE, an integer indicating the number of kept axes |
Returns a list of class dec
and dudi
(see dudi
) containing also
R |
sum of all the values of the initial table |
Daniel Chessel
Anne-Béatrice Dufour [email protected]
Dolédec, S., Chessel, D. and Olivier J. M. (1995) L'analyse des correspondances décentrée: application aux peuplements ichtyologiques du haut-Rhône. Bulletin Français de la Pêche et de la Pisciculture, 336, 29–40.
data(ichtyo) dudi1 <- dudi.dec(ichtyo$tab, ichtyo$eff, scan = FALSE) sum(apply(ichtyo$tab, 2, function(x) chisq.test(x, p = ichtyo$eff/sum(ichtyo$eff))$statistic)) sum(dudi1$eig) * sum(ichtyo$eff) # the same s.class(dudi1$li, ichtyo$dat, wt = ichtyo$eff/sum(ichtyo$eff))
data(ichtyo) dudi1 <- dudi.dec(ichtyo$tab, ichtyo$eff, scan = FALSE) sum(apply(ichtyo$tab, 2, function(x) chisq.test(x, p = ichtyo$eff/sum(ichtyo$eff))$statistic)) sum(dudi1$eig) * sum(ichtyo$eff) # the same s.class(dudi1$li, ichtyo$dat, wt = ichtyo$eff/sum(ichtyo$eff))
Theses functions analyse a table of fuzzy variables.
A fuzzy variable takes values of type
giving the importance of k categories.
A missing data is denoted (0,...,0).
Only the profile a/sum(a) is used, and missing data are replaced by
the mean profile of the others in the function prep.fuzzy.var
. See ref. for details.
prep.fuzzy.var (df, col.blocks, row.w = rep(1, nrow(df))) dudi.fca(df, scannf = TRUE, nf = 2) dudi.fpca(df, scannf = TRUE, nf = 2)
prep.fuzzy.var (df, col.blocks, row.w = rep(1, nrow(df))) dudi.fca(df, scannf = TRUE, nf = 2) dudi.fpca(df, scannf = TRUE, nf = 2)
df |
a data frame containing positive or null values |
col.blocks |
a vector containing the number of categories for each fuzzy variable |
row.w |
a vector of row weights |
scannf |
a logical value indicating whether the eigenvalues bar plot should be displayed |
nf |
if scannf FALSE, an integer indicating the number of kept axes |
The function prep.fuzzy.var
returns a data frame with the attribute col.blocks
.
The function dudi.fca
returns a list of class fca
and dudi
(see dudi) containing also
cr |
a data frame which rows are the blocs, columns are the kept axes, and values are the correlation ratios. |
The function dudi.fpca
returns a list of class pca
and dudi
(see dudi) containing also
cent
norm
blo
indica
FST
inertia
Daniel Chessel
Anne-Béatrice Dufour [email protected]
Chevenet, F., Dolédec, S. and Chessel, D. (1994) A fuzzy coding approach for the analysis of long-term ecological data. Freshwater Biology, 31, 295–309.
w1 <- matrix(c(1,0,0,2,1,1,0,2,2,0,1,0,1,1,1,0,1,3,1,0), 4, 5) w1 <- data.frame(w1) w2 <- prep.fuzzy.var(w1, c(2, 3)) w1 w2 attributes(w2) data(bsetal97) w <- prep.fuzzy.var(bsetal97$biol, bsetal97$biol.blo) if(adegraphicsLoaded()) { g1 <- plot(dudi.fca(w, scann = FALSE, nf = 3), plabels.cex = 1.5) } else { scatter(dudi.fca(w, scann = FALSE, nf = 3), csub = 3, clab.moda = 1.5) scatter(dudi.fpca(w, scann = FALSE, nf = 3), csub = 3, clab.moda = 1.5) } ## Not run: w1 <- prep.fuzzy.var(bsetal97$biol, bsetal97$biol.blo) w2 <- prep.fuzzy.var(bsetal97$ecol, bsetal97$ecol.blo) d1 <- dudi.fca(w1, scannf = FALSE, nf = 3) d2 <- dudi.fca(w2, scannf = FALSE, nf = 3) plot(coinertia(d1, d2, scannf = FALSE)) ## End(Not run)
w1 <- matrix(c(1,0,0,2,1,1,0,2,2,0,1,0,1,1,1,0,1,3,1,0), 4, 5) w1 <- data.frame(w1) w2 <- prep.fuzzy.var(w1, c(2, 3)) w1 w2 attributes(w2) data(bsetal97) w <- prep.fuzzy.var(bsetal97$biol, bsetal97$biol.blo) if(adegraphicsLoaded()) { g1 <- plot(dudi.fca(w, scann = FALSE, nf = 3), plabels.cex = 1.5) } else { scatter(dudi.fca(w, scann = FALSE, nf = 3), csub = 3, clab.moda = 1.5) scatter(dudi.fpca(w, scann = FALSE, nf = 3), csub = 3, clab.moda = 1.5) } ## Not run: w1 <- prep.fuzzy.var(bsetal97$biol, bsetal97$biol.blo) w2 <- prep.fuzzy.var(bsetal97$ecol, bsetal97$ecol.blo) d1 <- dudi.fca(w1, scannf = FALSE, nf = 3) d2 <- dudi.fca(w2, scannf = FALSE, nf = 3) plot(coinertia(d1, d2, scannf = FALSE)) ## End(Not run)
performs a multivariate analysis with mixed quantitative variables and factors.
dudi.hillsmith(df, row.w = rep(1, nrow(df))/nrow(df), scannf = TRUE, nf = 2)
dudi.hillsmith(df, row.w = rep(1, nrow(df))/nrow(df), scannf = TRUE, nf = 2)
df |
a data frame with mixed type variables (quantitative and factor) |
row.w |
a vector of row weights, by default uniform row weights are used |
scannf |
a logical value indicating whether the eigenvalues bar plot should be displayed |
nf |
if scannf FALSE, an integer indicating the number of kept axes |
If df
contains only quantitative variables, this is equivalent to a normed PCA.
If df
contains only factors, this is equivalent to a MCA.
This analysis is the Hill and Smith method and is very similar to dudi.mix
function.
The differences are that dudi.hillsmith
allow to use various row weights, while
dudi.mix
deals with ordered variables.
The principal components of this analysis are centered and normed vectors maximizing the sum of :
squared correlation coefficients with quantitative variables
correlation ratios with factors
Returns a list of class mix
and dudi
(see dudi) containing also
index |
a factor giving the type of each variable : f = factor, q = quantitative |
assign |
a factor indicating the initial variable for each column of the transformed table |
cr |
a data frame giving for each variable and each score: |
Stéphane Dray [email protected]
Anne-Béatrice Dufour [email protected]
Hill, M. O., and A. J. E. Smith. 1976. Principal component analysis of taxonomic data with multi-state discrete characters. Taxon, 25, 249-255.
dudi.mix
data(dunedata) attributes(dunedata$envir$use)$class <- "factor" # use dudi.mix for ordered data dd1 <- dudi.hillsmith(dunedata$envir, scann = FALSE) if(adegraphicsLoaded()) { g <- scatter(dd1, row.plab.cex = 1, col.plab.cex = 1.5) } else { scatter(dd1, clab.r = 1, clab.c = 1.5) }
data(dunedata) attributes(dunedata$envir$use)$class <- "factor" # use dudi.mix for ordered data dd1 <- dudi.hillsmith(dunedata$envir, scann = FALSE) if(adegraphicsLoaded()) { g <- scatter(dd1, row.plab.cex = 1, col.plab.cex = 1.5) } else { scatter(dd1, clab.r = 1, clab.c = 1.5) }
performs a multivariate analysis with mixed quantitative variables and factors.
dudi.mix(df, add.square = FALSE, scannf = TRUE, nf = 2)
dudi.mix(df, add.square = FALSE, scannf = TRUE, nf = 2)
df |
a data frame with mixed type variables (quantitative, factor and ordered) |
add.square |
a logical value indicating whether the squares of quantitative variables should be added |
scannf |
a logical value indicating whether the eigenvalues bar plot should be displayed |
nf |
if scannf FALSE, an integer indicating the number of kept axes |
If df contains only quantitative variables, this is equivalent to a normed PCA.
If df contains only factors, this is equivalent to a MCA.
Ordered factors are replaced by poly(x,deg=2)
.
This analysis generalizes the Hill and Smith method.
The principal components of this analysis are centered and normed vectors maximizing the sum of the:
squared correlation coefficients with quantitative variables
squared multiple correlation coefficients with polynoms
correlation ratios with factors.
Returns a list of class mix
and dudi
(see dudi) containing also
index |
a factor giving the type of each variable : f = factor, o = ordered, q = quantitative |
assign |
a factor indicating the initial variable for each column of the transformed table |
cr |
a data frame giving for each variable and each score: |
Daniel Chessel
Anne-Béatrice Dufour [email protected]
Hill, M. O., and A. J. E. Smith. 1976. Principal component analysis of taxonomic data with multi-state discrete characters. Taxon, 25, 249-255.
De Leeuw, J., J. van Rijckevorsel, and . 1980. HOMALS and PRINCALS - Some generalizations of principal components analysis. Pages 231-242 in E. Diday and Coll., editors. Data Analysis and Informatics II. Elsevier Science Publisher, North Holland, Amsterdam.
Kiers, H. A. L. 1994. Simple structure in component analysis techniques for mixtures of qualitative ans quantitative variables. Psychometrika, 56, 197-212.
data(dunedata) dd1 <- dudi.mix(dunedata$envir, scann = FALSE) if(adegraphicsLoaded()) { g1 <- scatter(dd1, row.plab.cex = 1, col.plab.cex = 1.5) } else { scatter(dd1, clab.r = 1, clab.c = 1.5) } dd2 <- dudi.mix(dunedata$envir, scann = FALSE, add.square = TRUE) if(adegraphicsLoaded()) { g2 <- scatter(dd2, row.plab.cex = 1, col.plab.cex = 1.5) } else { scatter(dd2, clab.r = 1, clab.c = 1.5) }
data(dunedata) dd1 <- dudi.mix(dunedata$envir, scann = FALSE) if(adegraphicsLoaded()) { g1 <- scatter(dd1, row.plab.cex = 1, col.plab.cex = 1.5) } else { scatter(dd1, clab.r = 1, clab.c = 1.5) } dd2 <- dudi.mix(dunedata$envir, scann = FALSE, add.square = TRUE) if(adegraphicsLoaded()) { g2 <- scatter(dd2, row.plab.cex = 1, col.plab.cex = 1.5) } else { scatter(dd2, clab.r = 1, clab.c = 1.5) }
performs a non symmetric correspondence analysis.
dudi.nsc(df, scannf = TRUE, nf = 2)
dudi.nsc(df, scannf = TRUE, nf = 2)
df |
a data frame containing positive or null values |
scannf |
a logical value indicating whether the eigenvalues bar plot should be displayed |
nf |
if scannf FALSE, an integer indicating the number of kept axes |
Returns a list of class nsc
and dudi
(see dudi
) containing also
N |
sum of the values of the initial table |
Daniel Chessel
Anne-Béatrice Dufour [email protected]
Kroonenberg, P. M., and Lombardo R. (1999) Nonsymmetric correspondence analysis: a tool for analysing contingency tables with a dependence structure. Multivariate Behavioral Research, 34, 367–396.
data(housetasks) nsc1 <- dudi.nsc(housetasks, scan = FALSE) if(adegraphicsLoaded()) { g1 <- s.label(nsc1$c1, plab.cex = 1.25) g2 <- s.arrow(nsc1$li, add = TRUE, plab.cex = 0.75) } else { s.label(nsc1$c1, clab = 1.25) s.arrow(nsc1$li, add.pl = TRUE, clab = 0.75) # see ref p.383 }
data(housetasks) nsc1 <- dudi.nsc(housetasks, scan = FALSE) if(adegraphicsLoaded()) { g1 <- s.label(nsc1$c1, plab.cex = 1.25) g2 <- s.arrow(nsc1$li, add = TRUE, plab.cex = 0.75) } else { s.label(nsc1$c1, clab = 1.25) s.arrow(nsc1$li, add.pl = TRUE, clab = 0.75) # see ref p.383 }
dudi.pca
performs a principal component analysis of a data frame and
returns the results as objects of class pca
and dudi
.
dudi.pca(df, row.w = rep(1, nrow(df))/nrow(df), col.w = rep(1, ncol(df)), center = TRUE, scale = TRUE, scannf = TRUE, nf = 2)
dudi.pca(df, row.w = rep(1, nrow(df))/nrow(df), col.w = rep(1, ncol(df)), center = TRUE, scale = TRUE, scannf = TRUE, nf = 2)
df |
a data frame with n rows (individuals) and p columns (numeric variables) |
row.w |
an optional row weights (by default, uniform row weights) |
col.w |
an optional column weights (by default, unit column weights) |
center |
a logical or numeric value, centring option |
scale |
a logical value indicating whether the column vectors should be normed for the row.w weighting |
scannf |
a logical value indicating whether the screeplot should be displayed |
nf |
if scannf FALSE, an integer indicating the number of kept axes |
Returns a list of classes pca
and dudi
(see dudi) containing the used information
for computing the principal component analysis :
tab |
the data frame to be analyzed depending of the transformation arguments (center and scale) |
cw |
the column weights |
lw |
the row weights |
eig |
the eigenvalues |
rank |
the rank of the analyzed matrice |
nf |
the number of kept factors |
c1 |
the column normed scores i.e. the principal axes |
l1 |
the row normed scores |
co |
the column coordinates |
li |
the row coordinates i.e. the principal components |
call |
the call function |
cent |
the p vector containing the means for variables (Note that if |
norm |
the p vector containing the standard deviations for variables i.e. the root
of the sum of squares deviations of the values from their means divided by n (Note that if |
Daniel Chessel
Anne-Béatrice Dufour [email protected]
prcomp
, princomp
in the mva
library
data(deug) deug.dudi <- dudi.pca(deug$tab, center = deug$cent, scale = FALSE, scan = FALSE) deug.dudi1 <- dudi.pca(deug$tab, center = TRUE, scale = TRUE, scan = FALSE) if(adegraphicsLoaded()) { g1 <- s.class(deug.dudi$li, deug$result, plot = FALSE) g2 <- s.arrow(deug.dudi$c1, lab = names(deug$tab), plot = FALSE) g3 <- s.class(deug.dudi1$li, deug$result, plot = FALSE) g4 <- s.corcircle(deug.dudi1$co, lab = names(deug$tab), full = FALSE, plot = FALSE) G1 <- rbindADEg(cbindADEg(g1, g2, plot = FALSE), cbindADEg(g3, g4, plot = FALSE), plot = TRUE) G2 <- s1d.hist(deug.dudi$tab, breaks = seq(-45, 35, by = 5), type = "density", xlim = c(-40, 40), right = FALSE, ylim = c(0, 0.1), porigin.lwd = 2) } else { par(mfrow = c(2, 2)) s.class(deug.dudi$li, deug$result, cpoint = 1) s.arrow(deug.dudi$c1, lab = names(deug$tab)) s.class(deug.dudi1$li, deug$result, cpoint = 1) s.corcircle(deug.dudi1$co, lab = names(deug$tab), full = FALSE, box = TRUE) par(mfrow = c(1, 1)) # for interpretations par(mfrow = c(3, 3)) par(mar = c(2.1, 2.1, 2.1, 1.1)) for(i in 1:9) { hist(deug.dudi$tab[,i], xlim = c(-40, 40), breaks = seq(-45, 35, by = 5), prob = TRUE, right = FALSE, main = names(deug$tab)[i], xlab = "", ylim = c(0, 0.10)) abline(v = 0, lwd = 3) } par(mfrow = c(1, 1)) }
data(deug) deug.dudi <- dudi.pca(deug$tab, center = deug$cent, scale = FALSE, scan = FALSE) deug.dudi1 <- dudi.pca(deug$tab, center = TRUE, scale = TRUE, scan = FALSE) if(adegraphicsLoaded()) { g1 <- s.class(deug.dudi$li, deug$result, plot = FALSE) g2 <- s.arrow(deug.dudi$c1, lab = names(deug$tab), plot = FALSE) g3 <- s.class(deug.dudi1$li, deug$result, plot = FALSE) g4 <- s.corcircle(deug.dudi1$co, lab = names(deug$tab), full = FALSE, plot = FALSE) G1 <- rbindADEg(cbindADEg(g1, g2, plot = FALSE), cbindADEg(g3, g4, plot = FALSE), plot = TRUE) G2 <- s1d.hist(deug.dudi$tab, breaks = seq(-45, 35, by = 5), type = "density", xlim = c(-40, 40), right = FALSE, ylim = c(0, 0.1), porigin.lwd = 2) } else { par(mfrow = c(2, 2)) s.class(deug.dudi$li, deug$result, cpoint = 1) s.arrow(deug.dudi$c1, lab = names(deug$tab)) s.class(deug.dudi1$li, deug$result, cpoint = 1) s.corcircle(deug.dudi1$co, lab = names(deug$tab), full = FALSE, box = TRUE) par(mfrow = c(1, 1)) # for interpretations par(mfrow = c(3, 3)) par(mar = c(2.1, 2.1, 2.1, 1.1)) for(i in 1:9) { hist(deug.dudi$tab[,i], xlim = c(-40, 40), breaks = seq(-45, 35, by = 5), prob = TRUE, right = FALSE, main = names(deug$tab)[i], xlab = "", ylim = c(0, 0.10)) abline(v = 0, lwd = 3) } par(mfrow = c(1, 1)) }
dudi.pco
performs a principal coordinates analysis of a Euclidean distance matrix
and returns the results as objects of class pco
and dudi
.
dudi.pco(d, row.w = "uniform", scannf = TRUE, nf = 2, full = FALSE, tol = 1e-07) ## S3 method for class 'pco' scatter(x, xax = 1, yax = 2, clab.row = 1, posieig = "top", sub = NULL, csub = 2, ...)
dudi.pco(d, row.w = "uniform", scannf = TRUE, nf = 2, full = FALSE, tol = 1e-07) ## S3 method for class 'pco' scatter(x, xax = 1, yax = 2, clab.row = 1, posieig = "top", sub = NULL, csub = 2, ...)
d |
an object of class |
row.w |
an optional distance matrix row weights. If not NULL, must be a vector of positive numbers with length equal to the size of the distance matrix |
scannf |
a logical value indicating whether the eigenvalues bar plot should be displayed |
nf |
if scannf FALSE, an integer indicating the number of kept axes |
full |
a logical value indicating whether all the axes should be kept |
tol |
a tolerance threshold to test whether the distance matrix is Euclidean :
an eigenvalue is considered positive if it is larger than
|
x |
an object of class |
xax |
the column number for the x-axis |
yax |
the column number for the y-axis |
clab.row |
a character size for the row labels |
posieig |
if "top" the eigenvalues bar plot is upside, if "bottom" it is downside, if "none" no plot |
sub |
a string of characters to be inserted as legend |
csub |
a character size for the legend, used with |
... |
further arguments passed to or from other methods |
dudi.pco
returns a list of class pco
and dudi
. See dudi
Daniel Chessel
Anne-Béatrice Dufour [email protected]
Gower, J. C. (1966) Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika, 53, 325–338.
data(yanomama) gen <- quasieuclid(as.dist(yanomama$gen)) geo <- quasieuclid(as.dist(yanomama$geo)) ant <- quasieuclid(as.dist(yanomama$ant)) geo1 <- dudi.pco(geo, scann = FALSE, nf = 3) gen1 <- dudi.pco(gen, scann = FALSE, nf = 3) ant1 <- dudi.pco(ant, scann = FALSE, nf = 3) plot(coinertia(ant1, gen1, scann = FALSE))
data(yanomama) gen <- quasieuclid(as.dist(yanomama$gen)) geo <- quasieuclid(as.dist(yanomama$geo)) ant <- quasieuclid(as.dist(yanomama$ant)) geo1 <- dudi.pco(geo, scann = FALSE, nf = 3) gen1 <- dudi.pco(gen, scann = FALSE, nf = 3) ant1 <- dudi.pco(ant, scann = FALSE, nf = 3) plot(coinertia(ant1, gen1, scann = FALSE))
dunedata
is a data set containing for 20 sites, environmental variables and plant species.
data(dunedata)
data(dunedata)
dunedata
is a list with 2 components.
is a data frame with 20 rows (sites) 5 columns (environnemental variables).
is a data frame with 20 rows (sites) 30 columns (plant species).
Jongman, R. H., ter Braak, C. J. F. and van Tongeren, O. F. R. (1987) Data analysis in community and landscape ecology, Pudoc, Wageningen.
data(dunedata) summary(dunedata$envir) is.ordered(dunedata$envir$use) score(dudi.mix(dunedata$envir, scan = FALSE))
data(dunedata) summary(dunedata$envir) is.ordered(dunedata$envir$use) score(dudi.mix(dunedata$envir, scan = FALSE))
These data were measured during the normal sinus rhythm of a patient who occasionally experiences arrhythmia. There are 2048 observations measured in units of millivolts and collected at a rate of 180 samples per second. This time series is a good candidate for a multiresolution analysis because its components are on different scales. For example, the large scale (low frequency) fluctuations, known as baseline drift, are due to the patient respiration, while the prominent short scale (high frequency) intermittent fluctuations between 3 and 4 seconds are evidently due to patient movement. Heart rhythm determines most of the remaining features in the series. The large spikes occurring about 0.7 seconds apart the R waves of normal heart rhythm; the smaller, but sharp peak coming just prior to an R wave is known as a P wave; and the broader peak that comes after a R wave is a T wave.
data(ecg)
data(ecg)
A vector of class ts
containing 2048 observations.
Gust Bardy and Per Reinhall, University of Washington
Percival, D. B., and Walden, A.T. (2000) Wavelet Methods for Time Series Analysis, Cambridge University Press.
## Not run: # figure 130 in Percival and Walden (2000) if (requireNamespace("waveslim") == TRUE) { data(ecg) ecg.level <- haar2level(ecg) ecg.haar <- orthobasis.haar(length(ecg)) ecg.mld <- mld(ecg, ecg.haar, ecg.level, plot = FALSE) res <- cbind.data.frame(apply(ecg.mld[,1:5],1,sum), ecg.mld[,6:11]) par(mfrow = c(8,1)) par(mar = c(2, 5, 1.5, 0.6)) plot(as.ts(ecg), ylab = "ECG") apply(res, 2, function(x) plot(as.ts(x), ylim = range(res), ylab = "")) par(mfrow = c(1,1)) } ## End(Not run)
## Not run: # figure 130 in Percival and Walden (2000) if (requireNamespace("waveslim") == TRUE) { data(ecg) ecg.level <- haar2level(ecg) ecg.haar <- orthobasis.haar(length(ecg)) ecg.mld <- mld(ecg, ecg.haar, ecg.level, plot = FALSE) res <- cbind.data.frame(apply(ecg.mld[,1:5],1,sum), ecg.mld[,6:11]) par(mfrow = c(8,1)) par(mar = c(2, 5, 1.5, 0.6)) plot(as.ts(ecg), ylab = "ECG") apply(res, 2, function(x) plot(as.ts(x), ylim = range(res), ylab = "")) par(mfrow = c(1,1)) } ## End(Not run)
This data set gives ecomorphological informations about 129 bird species.
data(ecomor)
data(ecomor)
ecomor
is a list of 7 components.
is a data frame with 129 species, 6 variables (the feeding place classes): foliage, ground , twig , bush, trunk and aerial feeders. These dummy variables indicate the use (1) or no use (0) of a given feeding place by a species.
is a data frame with 129 species and 8 variables (diet types): Gr (granivorous: seeds), Fr (frugivorous: berries, acorns, drupes), Ne (frugivorous: nectar), Fo (folivorous: leaves), In (invertebrate feeder: insects, spiders, myriapods, isopods, snails, worms), Ca (carnivorous: flesh of small vertebrates), Li (limnivorous: invertebrates in fresh water), and Ch (carrion feeder). These dummy variables indicate the use (1) or no use (0) of a given diet type by a species.
is a data frame with 129 species, 16 dummy variables (the habitats). These variables indicate the species presence (1) or the species absence (0) in a given habitat.
is a data frame with 129 species abd 8 morphological variables: wingl (Wing length, mm), taill (Tail length, mm), culml (Culmen length, mm), bilh (Bill height, mm), bilw (Bill width, mm), tarsl (Tarsus length, mm), midtl (Middle toe length, mm) and weig (Weight, g).
is a data frame with 129 species and 3 factors: Genus, Family and Order.
It is a data frame of class 'taxo'
: the variables are factors giving nested classifications.
is a data frame with vectors of the names of species (complete and in abbreviated form.
is a data frame with 129 species, 2 factors : 'forsub' summarizing the feeding place and 'diet' the diet type.
Blondel, J., Vuilleumier, F., Marcus, L.F., and Terouanne, E. (1984). Is there ecomorphological convergence among mediterranean bird communities of Chile, California, and France. In Evolutionary Biology (eds M.K. Hecht, B. Wallace and R.J. MacIntyre), 141–213, 18. Plenum Press, New York.
See a data description at http://pbil.univ-lyon1.fr/R/pdf/pps023.pdf (in French).
data(ecomor) ric <- apply(ecomor$habitat, 2, sum) s.corcircle(dudi.pca(log(ecomor$morpho), scan = FALSE)$co) forsub <- data.frame(t(apply(ecomor$forsub, 1, function (x) x / sum(x)))) pca1 <- dudi.pca(forsub, scan = FALSE, scale = FALSE) w1 <- as.matrix(forsub) %*% as.matrix(pca1$c1) if(adegraphicsLoaded()) { g1 <- s.arrow(pca1$c1, plot = FALSE) g2 <- s.label(w1, plab.cex = 0, ppoi.cex = 2, plot = FALSE) G1 <- superpose(g1, g2, plot = TRUE) } else { s.arrow(pca1$c1) s.label(w1, clab = 0, add.p = TRUE, cpoi = 2) } diet <- data.frame(t(apply(ecomor$diet, 1, function (x) x / sum(x)))) pca2 <- dudi.pca(diet, scan = FALSE, scale = FALSE) w2 <- as.matrix(diet) %*% as.matrix(pca2$c1) if(adegraphicsLoaded()) { g3 <- s.arrow(pca2$c1, plot = FALSE) g4 <- s.label(w2, plab.cex = 0, ppoi.cex = 2, plot = FALSE) G2 <- superpose(g3, g4, plot = TRUE) } else { s.arrow(pca2$c1) s.label(w2, clab = 0, add.p = TRUE, cpoi = 2) } ## Not run: dmorpho <- dist.quant(log(ecomor$morpho), 3) dhabitat <- dist.binary(ecomor$habitat, 1) dtaxo <- dist.taxo(ecomor$taxo) mantel.randtest(dmorpho, dhabitat) RV.rtest(pcoscaled(dmorpho), pcoscaled(dhabitat), 999) procuste.randtest(pcoscaled(dmorpho), pcoscaled(dhabitat)) ecophy <- taxo2phylog(ecomor$taxo, add.tools=TRUE) table.phylog(ecomor$habitat, ecophy, clabel.n = 0.5, f = 0.6, clabel.c = 0.75, clabel.r = 0.5, csi = 0.75, cleg = 0) plot(ecophy, clabel.n = 0.75, clabel.l = 0.75, labels.l = ecomor$labels[,"latin"]) mantel.randtest(dmorpho, dtaxo) mantel.randtest(dhabitat, dtaxo) ## End(Not run)
data(ecomor) ric <- apply(ecomor$habitat, 2, sum) s.corcircle(dudi.pca(log(ecomor$morpho), scan = FALSE)$co) forsub <- data.frame(t(apply(ecomor$forsub, 1, function (x) x / sum(x)))) pca1 <- dudi.pca(forsub, scan = FALSE, scale = FALSE) w1 <- as.matrix(forsub) %*% as.matrix(pca1$c1) if(adegraphicsLoaded()) { g1 <- s.arrow(pca1$c1, plot = FALSE) g2 <- s.label(w1, plab.cex = 0, ppoi.cex = 2, plot = FALSE) G1 <- superpose(g1, g2, plot = TRUE) } else { s.arrow(pca1$c1) s.label(w1, clab = 0, add.p = TRUE, cpoi = 2) } diet <- data.frame(t(apply(ecomor$diet, 1, function (x) x / sum(x)))) pca2 <- dudi.pca(diet, scan = FALSE, scale = FALSE) w2 <- as.matrix(diet) %*% as.matrix(pca2$c1) if(adegraphicsLoaded()) { g3 <- s.arrow(pca2$c1, plot = FALSE) g4 <- s.label(w2, plab.cex = 0, ppoi.cex = 2, plot = FALSE) G2 <- superpose(g3, g4, plot = TRUE) } else { s.arrow(pca2$c1) s.label(w2, clab = 0, add.p = TRUE, cpoi = 2) } ## Not run: dmorpho <- dist.quant(log(ecomor$morpho), 3) dhabitat <- dist.binary(ecomor$habitat, 1) dtaxo <- dist.taxo(ecomor$taxo) mantel.randtest(dmorpho, dhabitat) RV.rtest(pcoscaled(dmorpho), pcoscaled(dhabitat), 999) procuste.randtest(pcoscaled(dmorpho), pcoscaled(dhabitat)) ecophy <- taxo2phylog(ecomor$taxo, add.tools=TRUE) table.phylog(ecomor$habitat, ecophy, clabel.n = 0.5, f = 0.6, clabel.c = 0.75, clabel.r = 0.5, csi = 0.75, cleg = 0) plot(ecophy, clabel.n = 0.75, clabel.l = 0.75, labels.l = ecomor$labels[,"latin"]) mantel.randtest(dmorpho, dtaxo) mantel.randtest(dhabitat, dtaxo) ## End(Not run)
This data set gives the results of the presidential election in France in 1988 for each department and all the candidates.
data(elec88)
data(elec88)
elec88
is a list with the following components:
a data frame with 94 rows (departments) and 9 variables (candidates)
the global result of the election all-over the country
a data frame with two variables: elec88$lab$dep
is a
vector containing the names of the 94 french departments,
elec88$lab$reg
is a vector containing the names of the
21 French administrative regions.
the data frame of 3 variables returning the boundary lines of each department.
The first variable is a factor. The levels of this one are the row.names of tab
.
The second and third variables return the coordinates (x, y) of the points of the boundary line.
a data frame with 4 variables (x1, y1, x2, y2) for the contour display of France
a data frame with two variables (x, y) giving the position of the center for each department
the neighbouring graph between departments, object of the class nb
the map of the french departments in Lambert II coordinates
(an object of the class SpatialPolygons
of sp
)
the contour of the map of France in Lambert II
coordinates (an object of the class SpatialPolygons
of sp
)
Public data
This dataset is compatible with presid2002
and cnc2003
data(elec88) apply(elec88$tab, 2, mean) summary(elec88$res) pca1 <- dudi.pca(elec88$tab, scale = FALSE, scannf = FALSE) if(adegraphicsLoaded()) { if(requireNamespace("sp", quietly = TRUE)) { data1 <- as.data.frame(as.numeric(rownames(elec88$tab) == "D25")) rownames(data1) <- row.names(elec88$Spatial) obj1 <- sp::SpatialPolygonsDataFrame(Sr = elec88$Spatial, data = data1) g1 <- s.Spatial(obj1, psub.text = "", plot = FALSE) g2 <- s.Spatial(obj1, psub.text = "", nb = elec88$nb, pnb.node.cex = 0, plot = FALSE) data3 <- as.data.frame(elec88$xy[, 1] + elec88$xy[, 2]) rownames(data3) <- row.names(elec88$Spatial) obj3 <- sp::SpatialPolygonsDataFrame(Sr = elec88$Spatial, data = data3) g3 <- s.Spatial(obj3, psub.text = "", plot = FALSE) data4 <- as.data.frame(pca1$li[, 1]) rownames(data4) <- row.names(elec88$Spatial) obj4 <- sp::SpatialPolygonsDataFrame(Sr = elec88$Spatial, data = data4) g4 <- s.Spatial(obj4, psub.text = "F1 PCA", plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } } else { par(mfrow = c(2, 2)) plot(elec88$area[, 2:3], type = "n", asp = 1) lpoly <- split(elec88$area[, 2:3], elec88$area[, 1]) lapply(lpoly, function(x) {points(x, type = "l"); invisible()}) polygon(elec88$area[elec88$area$V1 == "D25", 2:3], col = 1) area.plot(elec88$area, val = elec88$xy[, 1] + elec88$xy[, 2]) area.plot(elec88$area, val = pca1$li[, 1], sub = "F1 PCA", csub = 2, cleg = 1.5) par(mfrow = c(1, 1)) }
data(elec88) apply(elec88$tab, 2, mean) summary(elec88$res) pca1 <- dudi.pca(elec88$tab, scale = FALSE, scannf = FALSE) if(adegraphicsLoaded()) { if(requireNamespace("sp", quietly = TRUE)) { data1 <- as.data.frame(as.numeric(rownames(elec88$tab) == "D25")) rownames(data1) <- row.names(elec88$Spatial) obj1 <- sp::SpatialPolygonsDataFrame(Sr = elec88$Spatial, data = data1) g1 <- s.Spatial(obj1, psub.text = "", plot = FALSE) g2 <- s.Spatial(obj1, psub.text = "", nb = elec88$nb, pnb.node.cex = 0, plot = FALSE) data3 <- as.data.frame(elec88$xy[, 1] + elec88$xy[, 2]) rownames(data3) <- row.names(elec88$Spatial) obj3 <- sp::SpatialPolygonsDataFrame(Sr = elec88$Spatial, data = data3) g3 <- s.Spatial(obj3, psub.text = "", plot = FALSE) data4 <- as.data.frame(pca1$li[, 1]) rownames(data4) <- row.names(elec88$Spatial) obj4 <- sp::SpatialPolygonsDataFrame(Sr = elec88$Spatial, data = data4) g4 <- s.Spatial(obj4, psub.text = "F1 PCA", plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } } else { par(mfrow = c(2, 2)) plot(elec88$area[, 2:3], type = "n", asp = 1) lpoly <- split(elec88$area[, 2:3], elec88$area[, 1]) lapply(lpoly, function(x) {points(x, type = "l"); invisible()}) polygon(elec88$area[elec88$area$V1 == "D25", 2:3], col = 1) area.plot(elec88$area, val = elec88$xy[, 1] + elec88$xy[, 2]) area.plot(elec88$area, val = pca1$li[, 1], sub = "F1 PCA", csub = 2, cleg = 1.5) par(mfrow = c(1, 1)) }
This data set describes 27 characteristics of 21 wines distributed in four fields : rest, visual, olfactory and global.
data(escopage)
data(escopage)
escopage
is a list of 3 components.
is a data frame with 21 observations (wines) and 27 variables.
is the vector of the names of sub-tables : "rest" "visual" "olfactory" "global".
is a vector of the numbers of variables for each sub-table.
Escofier, B. and Pagès, J. (1990) Analyses factorielles simples et multiples : objectifs, méthodes et interprétation Dunod, Paris. 1–267.
Escofier, B. and Pagès, J. (1994) Multiple factor analysis (AFMULT package). Computational Statistics and Data Analysis, 18, 121–140.
data(escopage) w <- data.frame(scale(escopage$tab)) w <- ktab.data.frame(w, escopage$blo) names(w)[1:4] <- escopage$tab.names plot(mfa(w, scan = FALSE))
data(escopage) w <- data.frame(scale(escopage$tab)) w <- ktab.data.frame(w, escopage$blo) names(w)[1:4] <- escopage$tab.names plot(mfa(w, scan = FALSE))
This data set gives the proportions of employement in the primary, secondary and tertiary sectors for 12 European countries in 1978, 1986 and 1997.
data(euro123)
data(euro123)
euro123
is a list of 4 components.
is a data frame with 12 rows and 3 variables.
: idem in 1986
: idem in 1997
is a data frame with two factors to both organize the 3 tables.
Encyclopaedia Universalis, Symposium, Les chiffres du Monde. Encyclopaedia Universalis, Paris. 519.
data(euro123) if(adegraphicsLoaded()) { g1 <- triangle.label(euro123$in78, addaxes = TRUE, plabels.cex = 0, plot = FALSE) g2 <- triangle.label(euro123$in86, addaxes = TRUE, plabels.cex = 0, plot = FALSE) g3 <- triangle.label(euro123$in97, addaxes = TRUE, plabels.cex = 0, plot = FALSE) g4 <- triangle.match(euro123$in78, euro123$in97, plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2,2)) triangle.plot(euro123$in78, addaxes = TRUE) triangle.plot(euro123$in86, addaxes = TRUE) triangle.plot(euro123$in97, addaxes = TRUE) triangle.biplot(euro123$in78, euro123$in97) par(mfrow = c(1,1)) }
data(euro123) if(adegraphicsLoaded()) { g1 <- triangle.label(euro123$in78, addaxes = TRUE, plabels.cex = 0, plot = FALSE) g2 <- triangle.label(euro123$in86, addaxes = TRUE, plabels.cex = 0, plot = FALSE) g3 <- triangle.label(euro123$in97, addaxes = TRUE, plabels.cex = 0, plot = FALSE) g4 <- triangle.match(euro123$in78, euro123$in97, plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2,2)) triangle.plot(euro123$in78, addaxes = TRUE) triangle.plot(euro123$in86, addaxes = TRUE) triangle.plot(euro123$in97, addaxes = TRUE) triangle.biplot(euro123$in78, euro123$in97) par(mfrow = c(1,1)) }
This data set contains the mean values of five highly heritable linear combinations of cranial metric (GM1-GM3) and non metric (GN1-GN2) for 8 social groups of Rhesus Macaques on Cayo Santiago. It also describes the fission tree depicting the historical phyletic relationships.
data(fission)
data(fission)
fission
is a list containing the 2 following objects :
is a character string giving the fission tree in Newick format.
is a data frame with 8 social groups and five traits : cranial metrics (GM1, GM2, GM3) and cranial non metrics (GN1, GN2)
Cheverud, J. and Dow, M.M. (1985) An autocorrelation analysis of genetic variation due to lineal fission in social groups of rhesus macaques. American Journal of Physical Anthropology, 67, 113–122.
data(fission) fis.phy <- newick2phylog(fission$tre) table.phylog(fission$tab[names(fis.phy$leaves),], fis.phy, csi = 2) gearymoran(fis.phy$Amat, fission$tab)
data(fission) fis.phy <- newick2phylog(fission$tre) table.phylog(fission$tab[names(fis.phy$leaves),], fis.phy, csi = 2) gearymoran(fis.phy$Amat, fission$tab)
K tables have the same rows and the same columns.
Each table is transformed by P = X/sum(X). The average of P is computing.
A correspondence analysis is realized on this average.
The initial rows and the initial columns are projected in supplementary elements.
foucart(X, scannf = TRUE, nf = 2) ## S3 method for class 'foucart' plot(x, xax = 1, yax = 2, clab = 1, csub = 2, possub = "bottomright", ...) ## S3 method for class 'foucart' print(x, ...)
foucart(X, scannf = TRUE, nf = 2) ## S3 method for class 'foucart' plot(x, xax = 1, yax = 2, clab = 1, csub = 2, possub = "bottomright", ...) ## S3 method for class 'foucart' print(x, ...)
X |
a list of data frame where the row names and the column names are the same for each table |
scannf |
a logical value indicating whether the eigenvalues bar plot should be displayed |
nf |
if scannf FALSE, an integer indicating the number of kept axes |
x |
an object of class 'foucart' |
xax |
the column number of the x-axis |
yax |
the column number of the y-axis |
clab |
if not NULL, a character size for the labels, used with |
csub |
a character size for the legend, used with |
possub |
a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", "bottomright") |
... |
further arguments passed to or from other methods |
foucart
returns a list of the classes 'dudi', 'coa' and 'foucart'
call |
origine |
nf |
axes-components saved |
rank |
rank |
blo |
useful vector |
cw |
vector: column weights |
lw |
vector: row weights |
eig |
vector: eigen values |
tab |
data.frame: modified array |
li |
data.frame: row coordinates |
l1 |
data.frame: row normed scores |
co |
data.frame: column coordinates |
c1 |
data.frame: column normed scores |
Tli |
data.frame: row coordinates (each table) |
Tco |
data.frame: col coordinates (each table) |
TL |
data.frame: factors for Tli |
TC |
data.frame: factors for Tco |
Pierre Bady [email protected]
Anne-Béatrice Dufour [email protected]
Foucart, T. (1984) Analyse factorielle de tableaux multiples, Masson, Paris.
data(bf88) fou1 <- foucart(bf88, scann = FALSE, nf = 3) fou1 plot(fou1) data(meaudret) l1 <- split(meaudret$spe, meaudret$design$season) l1 <- lapply(l1, function(x) {row.names(x) <- paste("Sit",1:5,sep="");x}) fou2 <- foucart(l1, scan = FALSE) if(adegraphicsLoaded()) { kplot(fou2, row.plabels.cex = 2) } else { kplot(fou2, clab.r = 2) }
data(bf88) fou1 <- foucart(bf88, scann = FALSE, nf = 3) fou1 plot(fou1) data(meaudret) l1 <- split(meaudret$spe, meaudret$design$season) l1 <- lapply(l1, function(x) {row.names(x) <- paste("Sit",1:5,sep="");x}) fou2 <- foucart(l1, scan = FALSE) if(adegraphicsLoaded()) { kplot(fou2, row.plabels.cex = 2) } else { kplot(fou2, clab.r = 2) }
These functions allow to compute the fourth-corner statistic for abundance or presence-absence data. The fourth-corner statistic has been developed by Legendre et al (1997) and extended in Dray and Legendre (2008). The statistic measures the link between three tables: a table L (n x p) containing the abundances of p species at n sites, a second table R (n x m) containing the measurements of m environmental variables for the n sites, and a third table Q (p x s) describing s species traits for the p species.
fourthcorner(tabR, tabL, tabQ, modeltype = 6, nrepet = 999, tr01 = FALSE, p.adjust.method.G = p.adjust.methods, p.adjust.method.D = p.adjust.methods, p.adjust.D = c("global", "levels"), ...) fourthcorner2(tabR, tabL, tabQ, modeltype = 6, nrepet = 999, tr01 = FALSE, p.adjust.method.G = p.adjust.methods, ...) ## S3 method for class '4thcorner' print(x, varQ = 1:length(x$varnames.Q), varR = 1:length(x$varnames.R), stat = c("D", "D2"), ...) ## S3 method for class '4thcorner' summary(object,...) ## S3 method for class '4thcorner' plot(x, stat = c("D", "D2", "G"), type = c("table", "biplot"), xax = 1, yax = 2, x.rlq = NULL, alpha = 0.05, col = c("lightgrey", "red", "deepskyblue", "purple"), ...) fourthcorner.rlq(xtest, nrepet = 999, modeltype = 6, typetest = c("axes", "Q.axes", "R.axes"), p.adjust.method.G = p.adjust.methods, p.adjust.method.D = p.adjust.methods, p.adjust.D = c("global", "levels"), ...)
fourthcorner(tabR, tabL, tabQ, modeltype = 6, nrepet = 999, tr01 = FALSE, p.adjust.method.G = p.adjust.methods, p.adjust.method.D = p.adjust.methods, p.adjust.D = c("global", "levels"), ...) fourthcorner2(tabR, tabL, tabQ, modeltype = 6, nrepet = 999, tr01 = FALSE, p.adjust.method.G = p.adjust.methods, ...) ## S3 method for class '4thcorner' print(x, varQ = 1:length(x$varnames.Q), varR = 1:length(x$varnames.R), stat = c("D", "D2"), ...) ## S3 method for class '4thcorner' summary(object,...) ## S3 method for class '4thcorner' plot(x, stat = c("D", "D2", "G"), type = c("table", "biplot"), xax = 1, yax = 2, x.rlq = NULL, alpha = 0.05, col = c("lightgrey", "red", "deepskyblue", "purple"), ...) fourthcorner.rlq(xtest, nrepet = 999, modeltype = 6, typetest = c("axes", "Q.axes", "R.axes"), p.adjust.method.G = p.adjust.methods, p.adjust.method.D = p.adjust.methods, p.adjust.D = c("global", "levels"), ...)
tabR |
a dataframe containing the measurements (numeric values or factors) of m environmental variables (columns) for the n sites (rows). |
tabL |
a dataframe containing the abundances of p species (columns) at n sites (rows). |
tabQ |
a dataframe containing numeric values or factors describing s species traits (columns) for the p species (rows). |
modeltype |
an integer (1-6) indicating the permutation model used in the testing procedure (see details). |
nrepet |
the number of permutations |
tr01 |
a logical indicating if data in |
object |
an object of the class 4thcorner |
x |
an object of the class 4thcorner |
varR |
a vector containing indices for variables in |
varQ |
a vector containing indices for variables in |
type |
results are represented by a table or on a biplot (see x.rlq) |
alpha |
a value of significance level |
p.adjust.method.G |
a string indicating a method for multiple
adjustment used for output tabG, see |
p.adjust.method.D |
a string indicating a method for multiple
adjustment used for output tabD/tabD2, see |
p.adjust.D |
a string indicating if multiple adjustment for tabD/tabD2 should be done globally or only between levels of a factor ("levels", as in the original paper of Legendre et al. 1997) |
stat |
a character to specify if results should be plotted for cells (D and D2) or variables (G) |
xax |
an integer indicating which rlq axis should be plotted on the x-axis |
yax |
an integer indicating which rlq axis should be plotted on the y-axis |
x.rlq |
an object created by the |
col |
a vector of length 4 containing four colors used for the
graphical representations. The first is used to represent non-significant
associations, the second positive significant, the third negative
significant. For the 'biplot' method and objects created by the
|
xtest |
an object created by the |
typetest |
a string indicating which tests should be performed |
... |
further arguments passed to or from other methods |
For the fourthcorner
function, the link is measured by a Pearson correlation coefficient for two quantitative variables (trait and environmental variable), by a Pearson Chi2 and G statistic for two qualitative variables and by a Pseudo-F and Pearson r for one quantitative variable and one qualitative variable. The fourthcorner2 function offers a multivariate statistic (equal to the sum of eigenvalues of RLQ analysis) and measures the link between two variables by a square correlation coefficient (quant/quant), a Chi2/sum(L) (qual/qual) and a correlation ratio (quant/qual). The significance is tested by a permutation procedure. Different models are available:
model 1 (modeltype
=1): Permute values for each species independently (i.e., permute within each column of table L)
model 2 (modeltype
=2): Permute values of sites (i.e., permute entire rows of table L)
model 3 (modeltype
=3): Permute values for each site independently (i.e., permute within each row of table L)
model 4 (modeltype
=4): Permute values of species (i.e., permute entire columns of table L)
model 5 (modeltype
=5): Permute values of species and after
(or before) permute values of sites (i.e., permute entire columns and
after (or before) entire rows of table L)
model 6 (modeltype
=6): combination of the outputs of models
2 and 4. Dray and Legendre (2008) and ter Braak et al. (20012) showed
that all models (except model 6) have inflated type I error.
Note that the model 5 is strictly equivalent to permuting simultaneously the rows of tables R and Q, as proposed by Doledec et al. (1996).
The function summary
returns results for variables (G). The
function print
returns results for cells (D and D2). In the case
of qualitative variables, Holm's corrected pvalues are also provided.
The function plot
produces a graphical representation of the
results (white for non significant, light grey for negative significant
and dark grey for positive significant relationships). Results can be
plotted for variables (G) or for cells (D and D2). In the case of
qualitative / quantitative association, homogeneity (D) or correlation
(D2) are plotted.
The fourthcorner
function returns a a list where:
tabD
is a krandtest
object giving the results of tests for cells of the fourth-corner (homogeneity for quant./qual.).
tabD2
is a krandtest
object giving the results of tests for cells of the fourth-corner (Pearson r for quant./qual.).
tabG
is a krandtest
object giving the results of tests for variables (Pearson's Chi2 for qual./qual.).
The fourthcorner2
function returns a list where:
tabG
is a krandtest
object giving the results of tests for variables.
trRLQ
is a krandtest
object giving the results of tests for the multivariate statistic (i.e. equivalent to randtest.rlq
function).
Stéphane Dray [email protected]
Doledec, S., Chessel, D., ter Braak, C.J.F. and Champely, S. (1996) Matching species traits to environmental variables: a new three-table ordination method. Environmental and Ecological Statistics, 3, 143–166.
Legendre, P., R. Galzin, and M. L. Harmelin-Vivien. (1997) Relating behavior to habitat: solutions to the fourth-corner problem. Ecology, 78, 547–562.
Dray, S. and Legendre, P. (2008) Testing the species traits-environment relationships: the fourth-corner problem revisited. Ecology, 89, 3400–3412.
ter Braak, C., Cormont, A., and Dray, S. (2012) Improved testing of species traits-environment relationships in the fourth corner problem. Ecology, 93, 1525–1526.
Dray, S., Choler, P., Doledec, S., Peres-Neto, P.R., Thuiller, W., Pavoine, S. and ter Braak, C.J.F (2014) Combining the fourth-corner and the RLQ methods for assessing trait responses to environmental variation. Ecology, 95, 14–21. doi:10.1890/13-0196.1
rlq
, combine.4thcorner
, p.adjust.methods
data(aviurba) ## Version using the sequential test (ter Braak et al 2012) ## as recommended in Dray et al (2013), ## using Holm correction of P-values (only 99 permutations here) four.comb.default <- fourthcorner(aviurba$mil,aviurba$fau,aviurba$traits,nrepet=99) summary(four.comb.default) plot(four.comb.default, stat = "G") ## using fdr correction of P-values four.comb.fdr <- fourthcorner(aviurba$mil, aviurba$fau, aviurba$traits, nrepet = 99, p.adjust.method.G = 'fdr', p.adjust.method.D = 'fdr') summary(four.comb.fdr) plot(four.comb.fdr, stat = "G") ## Explicit procedure to combine the results of two models ## proposed in Dray and Legendre (2008);the above does this implicitly four2 <- fourthcorner(aviurba$mil,aviurba$fau,aviurba$traits,nrepet=99,modeltype=2) four4 <- fourthcorner(aviurba$mil,aviurba$fau,aviurba$traits,nrepet=99,modeltype=4) four.comb <- combine.4thcorner(four2, four4) summary(four.comb) plot(four.comb, stat = "G")
data(aviurba) ## Version using the sequential test (ter Braak et al 2012) ## as recommended in Dray et al (2013), ## using Holm correction of P-values (only 99 permutations here) four.comb.default <- fourthcorner(aviurba$mil,aviurba$fau,aviurba$traits,nrepet=99) summary(four.comb.default) plot(four.comb.default, stat = "G") ## using fdr correction of P-values four.comb.fdr <- fourthcorner(aviurba$mil, aviurba$fau, aviurba$traits, nrepet = 99, p.adjust.method.G = 'fdr', p.adjust.method.D = 'fdr') summary(four.comb.fdr) plot(four.comb.fdr, stat = "G") ## Explicit procedure to combine the results of two models ## proposed in Dray and Legendre (2008);the above does this implicitly four2 <- fourthcorner(aviurba$mil,aviurba$fau,aviurba$traits,nrepet=99,modeltype=2) four4 <- fourthcorner(aviurba$mil,aviurba$fau,aviurba$traits,nrepet=99,modeltype=4) four.comb <- combine.4thcorner(four2, four4) summary(four.comb) plot(four.comb, stat = "G")
This data set gives informations about sites, species and environmental variables.
data(friday87)
data(friday87)
friday87
is a list of 4 components.
is a data frame containing a faunistic table with 16 sites and 91 species.
is a data frame with 16 sites and 11 environmental variables.
is a vector of the number of species per group.
is the name of each group of species.
Friday, L.E. (1987) The diversity of macroinvertebrate and macrophyte communities in ponds, Freshwater Biology, 18, 87–104.
data(friday87) wfri <- data.frame(scale(friday87$fau, scal = FALSE)) wfri <- ktab.data.frame(wfri, friday87$fau.blo, tabnames = friday87$tab.names) if(adegraphicsLoaded()) { g1 <- kplot(sepan(wfri), row.plabels.cex = 2) } else { kplot(sepan(wfri), clab.r = 2, clab.c = 1) }
data(friday87) wfri <- data.frame(scale(friday87$fau, scal = FALSE)) wfri <- ktab.data.frame(wfri, friday87$fau.blo, tabnames = friday87$tab.names) if(adegraphicsLoaded()) { g1 <- kplot(sepan(wfri), row.plabels.cex = 2) } else { kplot(sepan(wfri), clab.r = 2, clab.c = 1) }
28 batches of fruits -two types- are judged by two different ways.
They are classified in order of preference, without ex aequo, by 16 individuals.
15 quantitative variables described the batches of fruits.
data(fruits)
data(fruits)
fruits
is a list of 3 components:
is a vector returning the type of the 28 batches of fruits (peaches or nectarines).
is a data frame of 28 rows and 16 columns (judges).
is a data frame of 28 rows and 16 measures (average of 2 judgements).
fruits$var
is a data frame of 15 variables:
taches: quantity of cork blemishes (0=absent - maximum 5)
stries: quantity of stria (1/none - maximum 4)
abmucr: abundance of mucron (1/absent - 4)
irform: shape irregularity (0/none - 3)
allong: length of the fruit (1/round fruit - 4)
suroug: percentage of the red surface (minimum 40% - maximum 90%)
homlot: homogeneity of the intra-batch coloring (1/strong - 4)
homfru: homogeneity of the intra-fruit coloring (1/strong - 4)
pubesc: pubescence (0/none - 4)
verrou: intensity of green in red area (1/none - 4)
foncee: intensity of dark area (0/pink - 4)
comucr: intensity of the mucron color (1=no contrast - 4/dark)
impres: kind of impression (1/watched - 4/pointillé)
coldom: intensity of the predominating color (0/clear - 4)
calibr: grade (1/<90g - 5/>200g)
Kervella, J. (1991) Analyse de l'attrait d'un produit : exemple d'une comparaison de lots de pêches. Agro-Industrie et méthodes statistiques. Compte-rendu des secondes journées européennes. Nantes 13-14 juin 1991. Association pour la Statistique et ses Utilisations, Paris, 313–325.
data(fruits) pcajug <- dudi.pca(fruits$jug, scann = FALSE) pcavar <- dudi.pca(fruits$var, scann = FALSE) if(adegraphicsLoaded()) { g1 <- s.corcircle(pcajug$co, plot = FALSE) g2 <- s.class(pcajug$li, fac = fruits$type, plot = FALSE) g3 <- s.corcircle(pcavar$co, plot = FALSE) g4 <- s.class(pcavar$li, fac = fruits$type, plot = FALSE) G1 <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) G2 <- plot(coinertia(pcajug, pcavar, scan = FALSE)) } else { par(mfrow = c(2,2)) s.corcircle(pcajug$co) s.class(pcajug$li, fac = fruits$type) s.corcircle(pcavar$co) s.class(pcavar$li, fac = fruits$type) par(mfrow = c(1,1)) plot(coinertia(pcajug, pcavar, scan = FALSE)) }
data(fruits) pcajug <- dudi.pca(fruits$jug, scann = FALSE) pcavar <- dudi.pca(fruits$var, scann = FALSE) if(adegraphicsLoaded()) { g1 <- s.corcircle(pcajug$co, plot = FALSE) g2 <- s.class(pcajug$li, fac = fruits$type, plot = FALSE) g3 <- s.corcircle(pcavar$co, plot = FALSE) g4 <- s.class(pcavar$li, fac = fruits$type, plot = FALSE) G1 <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) G2 <- plot(coinertia(pcajug, pcavar, scan = FALSE)) } else { par(mfrow = c(2,2)) s.corcircle(pcajug$co) s.class(pcajug$li, fac = fruits$type) s.corcircle(pcavar$co) s.class(pcavar$li, fac = fruits$type) par(mfrow = c(1,1)) plot(coinertia(pcajug, pcavar, scan = FALSE)) }
This function performs Moran's I test using phylogenetic and spatial link matrix (binary or general). It uses neighbouring weights so Moran's I and Geary's c randomization tests are equivalent.
gearymoran(bilis, X, nrepet = 999, alter=c("greater", "less", "two-sided"))
gearymoran(bilis, X, nrepet = 999, alter=c("greater", "less", "two-sided"))
bilis |
: a n by n link matrix where n is the row number of X |
X |
: a data frame with continuous variables |
nrepet |
: number of random vectors for the randomization test |
alter |
a character string specifying the alternative hypothesis, must be one of "greater" (default), "less" or "two-sided" |
bilis
is a squared symmetric matrix which terms are all positive or null.
bilis
is firstly transformed in frequency matrix A by dividing it by the total sum of data matrix :
The neighbouring weights is defined by the matrix where
.
For each vector x of the data frame X, the test is based on the Moran statistic
where x is D-centred.
Returns an object of class krandtest
(randomization tests).
Sébastien Ollier [email protected]
Daniel Chessel
Cliff, A. D. and Ord, J. K. (1973) Spatial autocorrelation, Pion, London.
Thioulouse, J., Chessel, D. and Champely, S. (1995) Multivariate analysis of spatial patterns: a unified approach to local and global structures. Environmental and Ecological Statistics, 2, 1–14.
moran.test
and geary.test
for classical versions of Moran's test and Geary's one
# a spatial example data(mafragh) tab0 <- (as.data.frame(scalewt(mafragh$env))) bilis0 <- neig2mat(nb2neig(mafragh$nb)) gm0 <- gearymoran(bilis0, tab0, 999) gm0 plot(gm0, nclass = 20) ## Not run: # a phylogenetic example data(mjrochet) mjr.phy <- newick2phylog(mjrochet$tre) mjr.tab <- log(mjrochet$tab) gearymoran(mjr.phy$Amat, mjr.tab) gearymoran(mjr.phy$Wmat, mjr.tab) if(adegraphicsLoaded()) { g1 <- table.value(mjr.phy$Wmat, ppoints.cex = 0.35, nclass = 5, axis.text = list(cex = 0), plot = FALSE) g2 <- table.value(mjr.phy$Amat, ppoints.cex = 0.35, nclass = 5, axis.text = list(cex = 0), plot = FALSE) G <- cbindADEg(g1, g2, plot = TRUE) } else { par(mfrow = c(1, 2)) table.value(mjr.phy$Wmat, csi = 0.25, clabel.r = 0) table.value(mjr.phy$Amat, csi = 0.35, clabel.r = 0) par(mfrow = c(1, 1)) } ## End(Not run)
# a spatial example data(mafragh) tab0 <- (as.data.frame(scalewt(mafragh$env))) bilis0 <- neig2mat(nb2neig(mafragh$nb)) gm0 <- gearymoran(bilis0, tab0, 999) gm0 plot(gm0, nclass = 20) ## Not run: # a phylogenetic example data(mjrochet) mjr.phy <- newick2phylog(mjrochet$tre) mjr.tab <- log(mjrochet$tab) gearymoran(mjr.phy$Amat, mjr.tab) gearymoran(mjr.phy$Wmat, mjr.tab) if(adegraphicsLoaded()) { g1 <- table.value(mjr.phy$Wmat, ppoints.cex = 0.35, nclass = 5, axis.text = list(cex = 0), plot = FALSE) g2 <- table.value(mjr.phy$Amat, ppoints.cex = 0.35, nclass = 5, axis.text = list(cex = 0), plot = FALSE) G <- cbindADEg(g1, g2, plot = TRUE) } else { par(mfrow = c(1, 2)) table.value(mjr.phy$Wmat, csi = 0.25, clabel.r = 0) table.value(mjr.phy$Amat, csi = 0.35, clabel.r = 0) par(mfrow = c(1, 1)) } ## End(Not run)
This data set gives genetic relationships between Galapagos tortoises populations with 10 microsatellites.
data(ggtortoises)
data(ggtortoises)
ggtortoises
is a list with the following components:
a data frame designed to be used in the area.plot
function
a list of three pixmap icons representing the tortoises morphotypes
a data frame containing meta informations about populations
a data frame containing the coordinates of the island labels
a numeric vector giving the number of alleles by marker
a data frame containing the number of alleles by populations for 10 microsatellites
an object of the class SpatialPolygons
of sp
,
containing the map
M.C. Ciofi, C. Milinkovitch, J.P. Gibbs, A. Caccone, and J.R. Powell (2002) Microsatellite analysis of genetic divergence among populations of giant galapagos tortoises. Molecular Ecology 11: 2265-2283.
M.C. Ciofi, C. Milinkovitch, J.P. Gibbs, A. Caccone, and J.R. Powell (2002). Microsatellite analysis of genetic divergence among populations of giant galapagos tortoises. Molecular Ecology 11: 2265-2283.
See a data description at http://pbil.univ-lyon1.fr/R/pdf/pps069.pdf (in French).
if(requireNamespace("pixmap", quietly=TRUE)) { data(ggtortoises) if(adegraphicsLoaded()) { if(requireNamespace("sp", quietly = TRUE)) { g1 <- s.logo(ggtortoises$pop, ggtortoises$ico[as.character(ggtortoises$pop$carap)], Sp = ggtortoises$Spatial, pbackground.col = "lightblue", pSp.col = "white", pgrid.draw = FALSE, ppoints.cex = 0.5) g1 <- s.label(ggtortoises$misc, pgrid.draw = FALSE, porigin.include = FALSE, paxes.draw = FALSE, add = TRUE) } } else { a1 <- ggtortoises$area area.plot(a1) rect(min(a1$x), min(a1$y), max(a1$x), max(a1$y), col = "lightblue") invisible(lapply(split(a1, a1$id), function(x) polygon(x[, -1], col = "white"))) s.label(ggtortoises$misc, grid = FALSE, include.ori = FALSE, addaxes = FALSE, add.p = TRUE) listico <- ggtortoises$ico[as.character(ggtortoises$pop$carap)] s.logo(ggtortoises$pop, listico, add.p = TRUE) } }
if(requireNamespace("pixmap", quietly=TRUE)) { data(ggtortoises) if(adegraphicsLoaded()) { if(requireNamespace("sp", quietly = TRUE)) { g1 <- s.logo(ggtortoises$pop, ggtortoises$ico[as.character(ggtortoises$pop$carap)], Sp = ggtortoises$Spatial, pbackground.col = "lightblue", pSp.col = "white", pgrid.draw = FALSE, ppoints.cex = 0.5) g1 <- s.label(ggtortoises$misc, pgrid.draw = FALSE, porigin.include = FALSE, paxes.draw = FALSE, add = TRUE) } } else { a1 <- ggtortoises$area area.plot(a1) rect(min(a1$x), min(a1$y), max(a1$x), max(a1$y), col = "lightblue") invisible(lapply(split(a1, a1$id), function(x) polygon(x[, -1], col = "white"))) s.label(ggtortoises$misc, grid = FALSE, include.ori = FALSE, addaxes = FALSE, add.p = TRUE) listico <- ggtortoises$ico[as.character(ggtortoises$pop$carap)] s.logo(ggtortoises$pop, listico, add.p = TRUE) } }
This data set gives the repartition in diameter classes of deposit samples.
data(granulo)
data(granulo)
granulo
is a list of 2 components.
contains the 49 deposit samples, 9 diameter classes, weight of grains by size class
contains the boundaries of the diameter classes
Gaschignard-Fossati, O. (1986) Répartition spatiale des macroinvertébrés benthiques d'un bras vif du Rhône. Rôle des crues et dynamique saisonnière. Thèse de doctorat, Université Lyon 1.
data(granulo) w <- t(apply(granulo$tab, 1, function (x) x / sum(x))) w <- data.frame(w) wtr <- data.frame(t(w)) wmoy <- data.frame(matrix(apply(wtr, 1, mean), 1)) d1 <- dudi.pca(w, scal = FALSE, scan = FALSE) wmoy <- suprow(d1, wmoy)$lisup if(adegraphicsLoaded()) { s.arrow(d1$c1, plab.cex = 1.5) s.distri(d1$c1, wtr, starSize = 0.33, ellipseSize = 0, add = TRUE, plab.cex = 0.75) s.label(wmoy, ppoints.cex = 5, plab.cex = 0, add = TRUE) } else { s.arrow(d1$c1, clab = 1.5) s.distri(d1$c1, wtr, cstar = 0.33, cell = 0, axesell = FALSE, add.p = TRUE, clab = 0.75) s.label(wmoy, cpoi = 5, clab = 0, add.p = TRUE) }
data(granulo) w <- t(apply(granulo$tab, 1, function (x) x / sum(x))) w <- data.frame(w) wtr <- data.frame(t(w)) wmoy <- data.frame(matrix(apply(wtr, 1, mean), 1)) d1 <- dudi.pca(w, scal = FALSE, scan = FALSE) wmoy <- suprow(d1, wmoy)$lisup if(adegraphicsLoaded()) { s.arrow(d1$c1, plab.cex = 1.5) s.distri(d1$c1, wtr, starSize = 0.33, ellipseSize = 0, add = TRUE, plab.cex = 0.75) s.label(wmoy, ppoints.cex = 5, plab.cex = 0, add = TRUE) } else { s.arrow(d1$c1, clab = 1.5) s.distri(d1$c1, wtr, cstar = 0.33, cell = 0, axesell = FALSE, add.p = TRUE, clab = 0.75) s.label(wmoy, cpoi = 5, clab = 0, add.p = TRUE) }
This function defines objects to analyse data sets associated with complete regular grid.
gridrowcol(nrow, ncol, cell.names = NULL)
gridrowcol(nrow, ncol, cell.names = NULL)
nrow |
size of the grid (number of rows) |
ncol |
size of the grid (number of columns) |
cell.names |
grid cell labels |
Returns a list containing the following items :
xy |
: a data frame with grid cell coordinates |
area |
: a data frame with three variables to display grid cells as areas |
neig |
: an object of class |
orthobasis |
: an object of class |
Sébastien Ollier [email protected]
Daniel Chessel
Méot, A., Chessel, D. and Sabatier, D. (1993) Opérateurs de voisinage et analyse des données spatio-temporelles. in J.D. Lebreton and B. Asselain, editors. Biométrie et environnement. Masson, 45-72.
Cornillon, P.A. (1998) Prise en compte de proximités en analyse factorielle et comparative. Thèse, Ecole Nationale Supérieure Agronomique, Montpellier.
w <- gridrowcol(8, 5) par(mfrow = c(1, 2)) area.plot(w$area, center = w$xy, graph = w$neig, clab = 0.75) area.plot(w$area, center = w$xy, graph = w$neig, clab = 0.75, label = as.character(1:40)) par(mfrow = c(1, 1)) if(adegraphicsLoaded()) { fac1 <- w$orthobasis names(fac1) <- as.character(signif(attr(w$orthobasis, "values"), 3)) s.value(w$xy, fac1, porigin.include = FALSE, plegend.drawKey = FALSE, pgrid.text.cex = 0, ylim = c(0, 10)) } else { par(mfrow = c(5,8)) for(k in 1:39) s.value(w$xy, w$orthobasis[, k], csi = 3, cleg = 0, csub = 2, sub = as.character(signif(attr(w$orthobasis, "values")[k], 3)), incl = FALSE, addax = FALSE, cgr = 0, ylim = c(0,10)) par(mfrow = c(1,1)) }
w <- gridrowcol(8, 5) par(mfrow = c(1, 2)) area.plot(w$area, center = w$xy, graph = w$neig, clab = 0.75) area.plot(w$area, center = w$xy, graph = w$neig, clab = 0.75, label = as.character(1:40)) par(mfrow = c(1, 1)) if(adegraphicsLoaded()) { fac1 <- w$orthobasis names(fac1) <- as.character(signif(attr(w$orthobasis, "values"), 3)) s.value(w$xy, fac1, porigin.include = FALSE, plegend.drawKey = FALSE, pgrid.text.cex = 0, ylim = c(0, 10)) } else { par(mfrow = c(5,8)) for(k in 1:39) s.value(w$xy, w$orthobasis[, k], csi = 3, cleg = 0, csub = 2, sub = as.character(signif(attr(w$orthobasis, "values")[k], 3)), incl = FALSE, addax = FALSE, cgr = 0, ylim = c(0,10)) par(mfrow = c(1,1)) }
This data set gives genotypes variation of 1066 individuals belonging to 52 predefined populations, for 404 microsatellite markers.
data(hdpg)
data(hdpg)
hdpg
is a list of 3 components.
is a data frame with the genotypes of 1066 individuals encoded with 6 characters (individuals in row, locus in column), for example ‘123098’ for a heterozygote carrying alleles ‘123’ and ‘098’, ‘123123’ for a homozygote carrying two alleles ‘123’ and, ‘000000’ for a not classified locus (missing data).
is a a data frame with 4 columns containing information about the 1066 individuals:
hdpg$ind$id
containing the Diversity Panel identification number of each individual,
and three factors hdpg$ind$sex
, hdpg$ind$population
and hdpg$ind$region
containing the names of the 52 populations belonging to 7 major geographic regions (see details).
is a dataframe containing four columns: hdpg$locus$marknames
a vector of names of the microsatellite markers, hdpg$locus$allbyloc
a vector containing the number of alleles by loci, hdpg$locus$chromosome
a factor defining a number for one chromosome and,
hdpg$locus$maposition
indicating the position of the locus in the chromosome.
The rows of hdpg$pop
are the names of the 52 populations belonging to the geographic regions
contained in the rows of hdpg$region
. The chosen regions are: America, Asia, Europe,
Middle East North Africa, Oceania, Subsaharan AFRICA.
The 52 populations are: Adygei, Balochi, Bantu, Basque, Bedouin, Bergamo, Biaka Pygmies,
Brahui, Burusho, Cambodian, Columbian, Dai, Daur, Druze, French,
Han, Hazara, Hezhen, Japanese, Kalash, Karitiana, Lahu, Makrani, Mandenka, Maya,
Mbuti Pygmies, Melanesian, Miaozu, Mongola, Mozabite, Naxi, NewGuinea, Nilote, Orcadian,
Oroqen, Palestinian, Pathan, Pima, Russian, San, Sardinian, She, Sindhi, Surui, Tu, Tujia, Tuscan,
Uygur, Xibo, Yakut, Yizu, Yoruba.
hdpg$freq
is a data frame with 52 rows,
corresponding to the 52 populations described above, and 4992 microsatellite markers.
Extract of data prepared by the Human Diversity Panel Genotypes (invalid http://research.marshfieldclinic.org/genetics/Freq/FreqInfo.htm)
prepared by Hinda Haned, from data used in: Noah A. Rosenberg, Jonatahan K. Pritchard, James L. Weber, Howard M. Cabb, Kenneth K. Kidds, Lev A. Zhivotovsky, Marcus W. Feldman (2002) Genetic Structure of human Populations Science, 298, 2381–2385.
Lev A. Zhivotovsky, Noah Rosenberg, and Marcus W. Feldman (2003). Features of Evolution and Expansion of Modern Humans, Inferred from Genomewide Microsatellite Markers Am. J. Hum. Genet, 72, 1171–1186.
data(hdpg) names(hdpg) str(hdpg)
data(hdpg) names(hdpg) str(hdpg)
Morphometric data set describing the shape of the first upper molar in populations of the Western European house mouse (Mus musculus domesticus)
data(houmousr)
data(houmousr)
houmousr
is a list with 2 components.
is a data frame with 214 rows (mice) and 128 morphometric variables.
is a factor giving the sampling location of the 214 mice.
The rows of houmousr$dfcc
correspond to 214 mice sampled in five locations in France and Italy. The 128 columns are 128 aligned coordinates describing the shape of the occlusal surface of the first upper molar (UM1).
houmousr$faccc
is a factor giving the location where mice were sampled: Montpellier, Frontignan, Gardouch (South of France), Lombardy (Northern Italy), and Corsica.
Thioulouse, J., Renaud, S., Dufour, AB. et al. Overcoming the Spurious Groups Problem in Between-Group PCA. Evol Biol (2021). https://doi.org/10.1007/s11692-021-09550-0
Renaud S, Pantalacci S, Auffray J (2011) Differential evolvability along lines of least resistance of upper and lower molars in island house mice. PLoS ONE 6, https://doi.org/10.1371/journal.pone.0018951
Renaud S, Dufour A, Hardouin E, Ledevin R, Auffray J (2015) Once upon multivariate analyses: when they tell several stories about biological evolution. PLoS ONE 10, https://doi.org/10.1371/journal.pone.0132801
Renaud S, Ledevin R, Souquet L, Gomes Rodrigues H, Ginot S, Agret S, Claude J, Herrel A, Hautier L (2018) Evolving teeth within a stable masticatory apparatus in Orkney mice. Evolutionary Biology 45:405–424
data(houmousr) fac1 <- houmousr$faccc df1 <- houmousr$dfcc nf1 <- nlevels(fac1) - 1 # Compute PCA pca1 <- dudi.pca(df1, scale = FALSE, scannf = FALSE, nf = nf1) # Compute BGA bca1 <- bca(pca1, fac1, scannf = FALSE, nf = nf1) if(adegraphicsLoaded()) { s.class(bca1$ls, fac1, starSize = 0, chullSize = 1, ellipseSize = 0, ppoint.cex = 0, plabel.cex = 0, plegend.drawKey = FALSE, col = TRUE) s.class(bca1$ls, fac1, starSize = 1, ellipseSize = 0, col = TRUE, add = T) } else { col1 <- c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00") s.class(bca1$ls, fac1, cstar = 1, cellipse = 0, col = col1) s.chull(bca1$ls, fac1, optchull = 1, add.plot = TRUE, col = col1) } ## Not run: # Compute cross-validated coordinates xbca1 <- loocv(bca1) plot(xbca1) ## End(Not run)
data(houmousr) fac1 <- houmousr$faccc df1 <- houmousr$dfcc nf1 <- nlevels(fac1) - 1 # Compute PCA pca1 <- dudi.pca(df1, scale = FALSE, scannf = FALSE, nf = nf1) # Compute BGA bca1 <- bca(pca1, fac1, scannf = FALSE, nf = nf1) if(adegraphicsLoaded()) { s.class(bca1$ls, fac1, starSize = 0, chullSize = 1, ellipseSize = 0, ppoint.cex = 0, plabel.cex = 0, plegend.drawKey = FALSE, col = TRUE) s.class(bca1$ls, fac1, starSize = 1, ellipseSize = 0, col = TRUE, add = T) } else { col1 <- c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00") s.class(bca1$ls, fac1, cstar = 1, cellipse = 0, col = col1) s.chull(bca1$ls, fac1, optchull = 1, add.plot = TRUE, col = col1) } ## Not run: # Compute cross-validated coordinates xbca1 <- loocv(bca1) plot(xbca1) ## End(Not run)
The housetasks
data frame gives 13 housetasks and their repartition in the couple.
data(housetasks)
data(housetasks)
This data frame contains four columns : wife, alternating, husband and jointly. Each column is a numeric vector.
Kroonenberg, P. M. and Lombardo, R. (1999) Nonsymmetric correspondence analysis: a tool for analysing contingency tables with a dependence structure. Multivariate Behavioral Research, 34, 367–396
data(housetasks) nsc1 <- dudi.nsc(housetasks, scan = FALSE) if(adegraphicsLoaded()) { s.label(nsc1$c1, plab.cex = 1.25) s.arrow(nsc1$li, add = TRUE, plab.cex = 0.75) } else { s.label(nsc1$c1, clab = 1.25) s.arrow(nsc1$li, add.pl = TRUE, clab = 0.75) }
data(housetasks) nsc1 <- dudi.nsc(housetasks, scan = FALSE) if(adegraphicsLoaded()) { s.label(nsc1$c1, plab.cex = 1.25) s.arrow(nsc1$li, add = TRUE, plab.cex = 0.75) } else { s.label(nsc1$c1, clab = 1.25) s.arrow(nsc1$li, add.pl = TRUE, clab = 0.75) }
This data set gives the frequencies of haplotypes of mitochondrial DNA restriction data in ten populations all over the world.
It gives also distances among the haplotypes.
data(humDNAm)
data(humDNAm)
humDNAm
is a list of 3 components.
is an object of class dist
with 56 haplotypes.
These distances are computed by counting the number of differences in restriction sites between two haplotypes.
is a data frame with 56 haplotypes, 10 abundance variables (populations). These variables give the haplotype abundance in a given population.
is a data frame with 10 populations, 1 variable (classification). This variable gives the name of the continent in which a given population is located.
Excoffier, L., Smouse, P.E. and Quattro, J.M. (1992) Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics, 131, 479–491.
data(humDNAm) dpcoahum <- dpcoa(data.frame(t(humDNAm$samples)), sqrt(humDNAm$distances), scan = FALSE, nf = 2) plot(dpcoahum)
data(humDNAm) dpcoahum <- dpcoa(data.frame(t(humDNAm$samples)), sqrt(humDNAm$distances), scan = FALSE, nf = 2) plot(dpcoahum)
This data set gives informations between a faunistic array, the total number of sampling points made at each sampling occasion and the year of the sampling occasion.
data(ichtyo)
data(ichtyo)
ichtyo
is a list of 3 components.
is a faunistic array with 9 columns and 32 rows.
is a vector of the 32 sampling effort.
is a factor where the levels are the 10 years of the sampling occasion.
The value n(i,j) at the ith row and the jth column in tab
corresponds
to the number of sampling points of the ith sampling occasion (in eff
) that contains the jth species.
Dolédec, S., Chessel, D. and Olivier, J. M. (1995) L'analyse des correspondances décentrée: application aux peuplements ichtyologiques du haut-Rhône. Bulletin Français de la Pêche et de la Pisciculture, 336, 29–40.
data(ichtyo) dudi1 <- dudi.dec(ichtyo$tab, ichtyo$eff, scannf = FALSE) s.class(dudi1$li, ichtyo$dat, wt = ichtyo$eff / sum(ichtyo$eff))
data(ichtyo) dudi1 <- dudi.dec(ichtyo$tab, ichtyo$eff, scannf = FALSE) s.class(dudi1$li, ichtyo$dat, wt = ichtyo$eff / sum(ichtyo$eff))
Computes the decomposition of inertia to measure the contributions of row and/or columns in multivariate methods
## S3 method for class 'dudi' inertia(x, row.inertia = FALSE, col.inertia = FALSE, ...) ## S3 method for class 'inertia' print(x, ...) ## S3 method for class 'inertia' summary(object, sort.axis = 1, subset = 5, ...)
## S3 method for class 'dudi' inertia(x, row.inertia = FALSE, col.inertia = FALSE, ...) ## S3 method for class 'inertia' print(x, ...) ## S3 method for class 'inertia' summary(object, sort.axis = 1, subset = 5, ...)
x , object
|
a duality diagram, object of class |
row.inertia |
if TRUE, returns the decomposition of inertia for the rows |
col.inertia |
if TRUE, returns the decomposition of inertia for the columns |
sort.axis |
the kept axis used to sort the contributions in decreasing order |
subset |
the number of rows and/or columns to display in the summary |
... |
further arguments passed to or from other methods |
Contributions are printed in percentage and the sign is the sign of the coordinates
An object of class inertia
, i.e. a list containing :
tot.inertia |
repartition of the total inertia between axes |
row.contrib |
contributions of the rows to the total inertia |
row.abs |
absolute contributions of the rows (i.e. decomposition per axis) |
row.rel |
relative contributions of the rows |
row.cum |
cumulative relative contributions of the rows (i.e. decomposition per row) |
col.contrib |
contributions of the columns to the total inertia |
col.abs |
absolute contributions of the columns (i.e. decomposition per axis) |
col.rel |
relative contributions of the columns |
col.cum |
cumulative relative contributions of the columns (i.e. decomposition per column) |
nf |
the number of kept axes |
Daniel Chessel
Stéphane Dray [email protected]
Anne-Béatrice Dufour [email protected]
Lebart, L., Morineau, A. and Tabart, N. (1977) Techniques de la description statistique, méthodes et logiciels pour la description des grands tableaux, Dunod, Paris, 61–62.
Volle, M. (1981) Analyse des données, Economica, Paris, 89–90 and 118
Lebart, L., Morineau, L. and Warwick, K.M. (1984) Multivariate descriptive analysis: correspondence and related techniques for large matrices, John Wiley and Sons, New York.
Greenacre, M. (1984) Theory and applications of correspondence analysis, Academic Press, London, 66.
Rouanet, H. and Le Roux, B. (1993) Analyse des données multidimensionnelles, Dunod, Paris, 143–144.
Tenenhaus, M. (1994) Méthodes statistiques en gestion, Dunod, Paris, p. 160, 161, 166, 204.
Lebart, L., Morineau, A. and Piron, M. (1995) Statistique exploratoire multidimensionnelle, Dunod, Paris, p. 56,95-96.
data(housetasks) coa1 <- dudi.coa(housetasks, scann = FALSE) res <- inertia(coa1, col = TRUE, row = FALSE) res summary(res)
data(housetasks) coa1 <- dudi.coa(housetasks, scann = FALSE) res <- inertia(coa1, col = TRUE, row = FALSE) res summary(res)
This data set contains geographical informations about 25 counties of Ireland.
data(irishdata)
data(irishdata)
irishdata
is a list of 13 components:
a data frame with polygons for each of the 25 contiguous counties
a vector with the names of the 25 counties
a data frame with the coordinates centers of the 25 counties
a data frame with 25 rows (counties) and 12 variables
a data frame with the global polygon of all the 25 counties
a matrix containing the common length between two counties
from area
a data frame with polygons for each of the 25 contiguous counties expressed in Universal Transverse Mercator (UTM) coordinates
a data frame with the UTM coordinates centers of the 25 counties
a matrix containing the common length between two counties
from area.utm
a data frame with the 25 counties (explicitly named) and 12 variables
a data frame with the global polygon of all the 25 counties expressed in UTM coordinates
the map of the 25 counties of Ireland (an object of the
class SpatialPolygons
of sp
)
the contour of the map of the 25 counties of
Ireland (an object of the class SpatialPolygons
of sp
)
Geary, R.C. (1954) The contiguity ratio and statistical mapping. The incorporated Statistician, 5, 3, 115–145.
Cliff, A.D. and Ord, J.K. (1973) Spatial autocorrelation, Pion, London. 1–178.
data(irishdata) if(adegraphicsLoaded()) { if(requireNamespace("sp", quietly = TRUE)){ g1 <- s.label(irishdata$xy.utm, Sp = irishdata$Spatial, pSp.col = "white", plot = FALSE) g21 <- s.label(irishdata$xy.utm, Sp = irishdata$Spatial, pSp.col = "white", plab.cex = 0, ppoints.cex = 0, plot = FALSE) g22 <- s.label(irishdata$xy.utm, Sp = irishdata$Spatial.contour, pSp.col = "transparent", plab.cex = 0, ppoints.cex = 0, pSp.lwd = 3, plot = FALSE) g2 <- superpose(g21, g22) g3 <- s.corcircle(dudi.pca(irishdata$tab, scan = FALSE)$co, plot = FALSE) score <- dudi.pca(irishdata$tab, scannf = FALSE, nf = 1)$li$Axis1 names(score) <- row.names(irishdata$Spatial) obj <- sp::SpatialPolygonsDataFrame(Sr = irishdata$Spatial, data = as.data.frame(score)) g4 <- s.Spatial(obj, plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } } else { par(mfrow = c(2, 2)) area.plot(irishdata$area, lab = irishdata$county.names, clab = 0.75) area.plot(irishdata$area) apply(irishdata$contour, 1, function(x) segments(x[1], x[2], x[3], x[4], lwd = 3)) s.corcircle(dudi.pca(irishdata$tab, scannf = FALSE)$co) score <- dudi.pca(irishdata$tab, scannf = FALSE, nf = 1)$li$Axis1 names(score) <- row.names(irishdata$tab) area.plot(irishdata$area, score) par(mfrow = c(1, 1)) }
data(irishdata) if(adegraphicsLoaded()) { if(requireNamespace("sp", quietly = TRUE)){ g1 <- s.label(irishdata$xy.utm, Sp = irishdata$Spatial, pSp.col = "white", plot = FALSE) g21 <- s.label(irishdata$xy.utm, Sp = irishdata$Spatial, pSp.col = "white", plab.cex = 0, ppoints.cex = 0, plot = FALSE) g22 <- s.label(irishdata$xy.utm, Sp = irishdata$Spatial.contour, pSp.col = "transparent", plab.cex = 0, ppoints.cex = 0, pSp.lwd = 3, plot = FALSE) g2 <- superpose(g21, g22) g3 <- s.corcircle(dudi.pca(irishdata$tab, scan = FALSE)$co, plot = FALSE) score <- dudi.pca(irishdata$tab, scannf = FALSE, nf = 1)$li$Axis1 names(score) <- row.names(irishdata$Spatial) obj <- sp::SpatialPolygonsDataFrame(Sr = irishdata$Spatial, data = as.data.frame(score)) g4 <- s.Spatial(obj, plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } } else { par(mfrow = c(2, 2)) area.plot(irishdata$area, lab = irishdata$county.names, clab = 0.75) area.plot(irishdata$area) apply(irishdata$contour, 1, function(x) segments(x[1], x[2], x[3], x[4], lwd = 3)) s.corcircle(dudi.pca(irishdata$tab, scannf = FALSE)$co) score <- dudi.pca(irishdata$tab, scannf = FALSE, nf = 1)$li$Axis1 names(score) <- row.names(irishdata$tab) area.plot(irishdata$area, score) par(mfrow = c(1, 1)) }
Confirmation of the Euclidean nature of a distance matrix by the Gower's theorem.is.euclid
is used in summary.dist
.
is.euclid(distmat, plot = FALSE, print = FALSE, tol = 1e-07) ## S3 method for class 'dist' summary(object, ...)
is.euclid(distmat, plot = FALSE, print = FALSE, tol = 1e-07) ## S3 method for class 'dist' summary(object, ...)
distmat |
an object of class 'dist' |
plot |
a logical value indicating whether the eigenvalues bar plot of the matrix of the term |
print |
a logical value indicating whether the eigenvalues of the matrix of the term |
tol |
a tolerance threshold : an eigenvalue is considered positive if it is larger than |
object |
an object of class 'dist' |
... |
further arguments passed to or from other methods |
returns a logical value indicating if all the eigenvalues are positive or equal to zero
Daniel Chessel
Stéphane Dray [email protected]
Gower, J.C. and Legendre, P. (1986) Metric and Euclidean properties of dissimilarity coefficients. Journal of Classification, 3, 5–48.
w <- matrix(runif(10000), 100, 100) w <- dist(w) summary(w) is.euclid (w) # TRUE w <- quasieuclid(w) # no correction need in: quasieuclid(w) w <- lingoes(w) # no correction need in: lingoes(w) w <- cailliez(w) # no correction need in: cailliez(w) rm(w)
w <- matrix(runif(10000), 100, 100) w <- dist(w) summary(w) is.euclid (w) # TRUE w <- quasieuclid(w) # no correction need in: quasieuclid(w) w <- lingoes(w) # no correction need in: lingoes(w) w <- cailliez(w) # no correction need in: cailliez(w) rm(w)
This data set gives the spatial distribution of seeds (quadrats counts) of seven species in the understorey of tropical rainforest.
data(julliot)
data(julliot)
julliot
is a list with the following components:
a data frame with 160 rows (quadrats) and 7 variables (species)
a data frame with the coordinates of the 160 quadrats (positioned by their centers)
a data frame with 3 variables returning the boundary lines of
each quadrat. The first variable is a factor. The levels of this one are
the row.names of tab
. The second and third variables return the
coordinates (x,y) of the points of the boundary line.
an object of the class SpatialPolygons
of sp
,
containing the map
Species names of julliot$tab
are:
Pouteria torta,
Minquartia guianensis,
Quiina obovata,
Chrysophyllum lucentifolium,
Parahancornia fasciculata,
Virola michelii,
and Pourouma spp.
Julliot, C. (1992). Utilisation des ressources alimentaires par le singe hurleur roux, Alouatta seniculus (Atelidae, Primates), en Guyane : impact de la dissémination des graines sur la régénération forestière. Thèse de troisième cycle, Université de Tours.
Julliot, C. (1997). Impact of seed dispersal by red howler monkeys Alouatta seniculus on the seedling population in the understorey of tropical rain forest. Journal of Ecology, 85, 431–440.
data(julliot) ## Not run: if(adegraphicsLoaded()) { if(requireNamespace("sp", quietly = TRUE)) { obj1 <- sp::SpatialPolygonsDataFrame(Sr = julliot$Spatial, data = log(julliot$tab + 1)) g1 <- s.Spatial(obj1) g2 <- s.value(julliot$xy, scalewt(log(julliot$tab + 1)), Sp = julliot$Spatial, pSp.col = "white", pgrid.draw = FALSE) } } else { if(requireNamespace("splancs", quietly = TRUE)) { par(mfrow = c(3, 3)) for(k in 1:7) area.plot(julliot$area, val = log(julliot$tab[, k] + 1), sub = names(julliot$tab)[k], csub = 2.5) par(mfrow = c(1, 1)) par(mfrow = c(3, 3)) for(k in 1:7) { area.plot(julliot$area) s.value(julliot$xy, scalewt(log(julliot$tab[, k] + 1)), sub = names(julliot$tab)[k], csub = 2.5, add.p = TRUE) } par(mfrow = c(1, 1)) } } ## End(Not run) if(adegraphicsLoaded()) { if(requireNamespace("sp", quietly = TRUE)) { g3 <- s.image(julliot$xy, log(julliot$tab + 1), span = 0.25) } g4 <- s.value(julliot$xy, log(julliot$tab + 1)) } else { if(requireNamespace("splancs", quietly = TRUE)) { par(mfrow = c(3, 3)) for(k in 1:7) s.image(julliot$xy, log(julliot$tab[, k] + 1), kgrid = 3, span = 0.25, sub = names(julliot$tab)[k], csub = 2.5) par(mfrow = c(1, 1)) par(mfrow = c(3, 3)) for(k in 1:7) s.value(julliot$xy, log(julliot$tab[, k] + 1), sub = names(julliot$tab)[k], csub = 2.5) par(mfrow = c(1, 1)) } } ## Not run: if (requireNamespace("spdep", quietly = TRUE)) { neig0 <- nb2neig(spdep::dnearneigh(as.matrix(julliot$xy), 1, 1.8)) if(adegraphicsLoaded()) { g5 <- s.label(julliot$xy, nb = spdep::dnearneigh(as.matrix(julliot$xy), 1, 1.8)) } else { par(mfrow = c(1, 1)) s.label(julliot$xy, neig = neig0, clab = 0.75, incl = FALSE, addax = FALSE, grid = FALSE) } gearymoran(ade4:::neig.util.LtoG(neig0), log(julliot$tab + 1)) if (requireNamespace("adephylo", quietly = TRUE)) { adephylo::orthogram(log(julliot$tab[, 3] + 1), ortho = scores.neig(neig0)) } } ## End(Not run)
data(julliot) ## Not run: if(adegraphicsLoaded()) { if(requireNamespace("sp", quietly = TRUE)) { obj1 <- sp::SpatialPolygonsDataFrame(Sr = julliot$Spatial, data = log(julliot$tab + 1)) g1 <- s.Spatial(obj1) g2 <- s.value(julliot$xy, scalewt(log(julliot$tab + 1)), Sp = julliot$Spatial, pSp.col = "white", pgrid.draw = FALSE) } } else { if(requireNamespace("splancs", quietly = TRUE)) { par(mfrow = c(3, 3)) for(k in 1:7) area.plot(julliot$area, val = log(julliot$tab[, k] + 1), sub = names(julliot$tab)[k], csub = 2.5) par(mfrow = c(1, 1)) par(mfrow = c(3, 3)) for(k in 1:7) { area.plot(julliot$area) s.value(julliot$xy, scalewt(log(julliot$tab[, k] + 1)), sub = names(julliot$tab)[k], csub = 2.5, add.p = TRUE) } par(mfrow = c(1, 1)) } } ## End(Not run) if(adegraphicsLoaded()) { if(requireNamespace("sp", quietly = TRUE)) { g3 <- s.image(julliot$xy, log(julliot$tab + 1), span = 0.25) } g4 <- s.value(julliot$xy, log(julliot$tab + 1)) } else { if(requireNamespace("splancs", quietly = TRUE)) { par(mfrow = c(3, 3)) for(k in 1:7) s.image(julliot$xy, log(julliot$tab[, k] + 1), kgrid = 3, span = 0.25, sub = names(julliot$tab)[k], csub = 2.5) par(mfrow = c(1, 1)) par(mfrow = c(3, 3)) for(k in 1:7) s.value(julliot$xy, log(julliot$tab[, k] + 1), sub = names(julliot$tab)[k], csub = 2.5) par(mfrow = c(1, 1)) } } ## Not run: if (requireNamespace("spdep", quietly = TRUE)) { neig0 <- nb2neig(spdep::dnearneigh(as.matrix(julliot$xy), 1, 1.8)) if(adegraphicsLoaded()) { g5 <- s.label(julliot$xy, nb = spdep::dnearneigh(as.matrix(julliot$xy), 1, 1.8)) } else { par(mfrow = c(1, 1)) s.label(julliot$xy, neig = neig0, clab = 0.75, incl = FALSE, addax = FALSE, grid = FALSE) } gearymoran(ade4:::neig.util.LtoG(neig0), log(julliot$tab + 1)) if (requireNamespace("adephylo", quietly = TRUE)) { adephylo::orthogram(log(julliot$tab[, 3] + 1), ortho = scores.neig(neig0)) } } ## End(Not run)
This data set gives physical and physico-chemical variables, fish species, spatial coordinates about 92 sites.
data(jv73)
data(jv73)
jv73
is a list with the following components:
a data frame with 92 sites and 6 physical variables
a data frame with 92 sites and 12 physico-chemical variables
a data frame with 92 sites and 19 fish species
a data frame with 92 sites and 2 spatial coordinates
a data frame for mapping
a factor distributing the 92 sites on 12 rivers
an object of the class SpatialLines
of sp
,
containing the map
Verneaux, J. (1973) Cours d'eau de Franche-Comté (Massif du Jura). Recherches écologiques sur le réseau hydrographique du Doubs. Essai de biotypologie. Thèse d'Etat, Besançon.
See a data description at http://pbil.univ-lyon1.fr/R/pdf/pps047.pdf (in French).
data(jv73) w <- split(jv73$morpho, jv73$fac.riv) w <- lapply(w, function(x) t(dudi.pca(x, scann = FALSE))) w <- ktab.list.dudi(w) if(adegraphicsLoaded()) { if(requireNamespace("sp", quietly = TRUE)) { g11 <- s.label(jv73$xy, Sp = jv73$Spatial, pori.incl = FALSE, plab.cex = 0.75, plot = FALSE) g12 <- s.class(jv73$xy, jv73$fac.riv, ellipseSize = 0, pellipses.axes.draw = FALSE, starSize = 0, ppoints.cex = 0, plab.cex = 1.25, plot = FALSE) g1 <- superpose(g11, g12, plot = TRUE) g2 <- kplot(sepan(w), perm = TRUE, row.plab.cex = 0, posieig = "none") } } else { s.label(jv73$xy, contour = jv73$contour, incl = FALSE, clab = 0.75) s.class(jv73$xy, jv73$fac.riv, add.p = TRUE, cell = 0, axese = FALSE, csta = 0, cpoi = 0, clab = 1.25) kplot(sepan(w), perm = TRUE, clab.r = 0, clab.c = 2, show = FALSE) }
data(jv73) w <- split(jv73$morpho, jv73$fac.riv) w <- lapply(w, function(x) t(dudi.pca(x, scann = FALSE))) w <- ktab.list.dudi(w) if(adegraphicsLoaded()) { if(requireNamespace("sp", quietly = TRUE)) { g11 <- s.label(jv73$xy, Sp = jv73$Spatial, pori.incl = FALSE, plab.cex = 0.75, plot = FALSE) g12 <- s.class(jv73$xy, jv73$fac.riv, ellipseSize = 0, pellipses.axes.draw = FALSE, starSize = 0, ppoints.cex = 0, plab.cex = 1.25, plot = FALSE) g1 <- superpose(g11, g12, plot = TRUE) g2 <- kplot(sepan(w), perm = TRUE, row.plab.cex = 0, posieig = "none") } } else { s.label(jv73$xy, contour = jv73$contour, incl = FALSE, clab = 0.75) s.class(jv73$xy, jv73$fac.riv, add.p = TRUE, cell = 0, axese = FALSE, csta = 0, cpoi = 0, clab = 1.25) kplot(sepan(w), perm = TRUE, clab.r = 0, clab.c = 2, show = FALSE) }
This data set contains informations about 33 ponds in De Maten reserve (Genk, Belgium).
data(kcponds)
data(kcponds)
kponds
is a list with the following components:
a data frame with 15 environmental variables (columns) on 33 ponds (rows)
an object of class area
a data frame with the coordinates of ponds
the neighbourhood graph of the 33 sites (an object of class nb
)
an object of the class SpatialPolygons
of sp
, containing the map
Variables of kcponds$tab
are the following ones : depth, area, O2 (oxygen concentration),
cond (conductivity), pH, Fe (Fe concentration), secchi (Secchi disk depth), N (NNO concentration),
TP (total phosphorus concentration), chla (chlorophyll-a concentration), EM (emergent macrophyte cover),
FM (floating macrophyte cover), SM (submerged macrophyte cover), denMI (total density of macroinvertebrates),
divMI (diversity macroinvertebrates)
Cottenie, K. (2002) Local and regional processes in a zooplankton metacommunity. PhD, Katholieke Universiteit Leuven, Leuven, Belgium.
data(kcponds) w <- as.numeric(scalewt(kcponds$tab$N)) if(adegraphicsLoaded()) { if(requireNamespace("sp", quietly = TRUE)) { g1 <- s.label(kcponds$xy, Sp = kcponds$Spatial, pSp.col = "white", nb = kcponds$nb, plab.cex = 0, paxes.asp = "fill", plot = FALSE) g2 <- s.label(kcponds$xy, Sp = kcponds$Spatial, pSp.col = "white", plabels.cex = 0.8, paxes.asp = "fill", plot = FALSE) g3 <- s.value(kcponds$xy, w, psub.text = "Nitrogen concentration", paxe.asp = "fill", plot = FALSE) G <- rbindADEg(g1, g2, g3, plot = TRUE) } } else { par(mfrow = c(3, 1)) area.plot(kcponds$area) s.label(kcponds$xy, add.p = TRUE, cpoi = 2, clab = 0) s.label(kcponds$xy, add.p = TRUE, cpoi = 3, clab = 0) area.plot(kcponds$area) s.label(kcponds$xy, add.p = TRUE, clab = 1.5) s.value(kcponds$xy, w, cleg = 2, sub = "Nitrogen concentration", csub = 4, possub = "topright", include = FALSE) par(mfrow = c(1, 1)) } ## Not run: par(mfrow = c(3, 1)) pca1 <- dudi.pca(kcponds$tab, scan = FALSE, nf = 4) if(requireNamespace("spdep", quietly = TRUE) & requireNamespace("adespatial", quietly = TRUE)) { multi1 <- adespatial::multispati(pca1, spdep::nb2listw(kcponds$nb), scannf = FALSE, nfposi = 2, nfnega = 1) summary(multi1) } par(mfrow = c(1, 1)) ## End(Not run)
data(kcponds) w <- as.numeric(scalewt(kcponds$tab$N)) if(adegraphicsLoaded()) { if(requireNamespace("sp", quietly = TRUE)) { g1 <- s.label(kcponds$xy, Sp = kcponds$Spatial, pSp.col = "white", nb = kcponds$nb, plab.cex = 0, paxes.asp = "fill", plot = FALSE) g2 <- s.label(kcponds$xy, Sp = kcponds$Spatial, pSp.col = "white", plabels.cex = 0.8, paxes.asp = "fill", plot = FALSE) g3 <- s.value(kcponds$xy, w, psub.text = "Nitrogen concentration", paxe.asp = "fill", plot = FALSE) G <- rbindADEg(g1, g2, g3, plot = TRUE) } } else { par(mfrow = c(3, 1)) area.plot(kcponds$area) s.label(kcponds$xy, add.p = TRUE, cpoi = 2, clab = 0) s.label(kcponds$xy, add.p = TRUE, cpoi = 3, clab = 0) area.plot(kcponds$area) s.label(kcponds$xy, add.p = TRUE, clab = 1.5) s.value(kcponds$xy, w, cleg = 2, sub = "Nitrogen concentration", csub = 4, possub = "topright", include = FALSE) par(mfrow = c(1, 1)) } ## Not run: par(mfrow = c(3, 1)) pca1 <- dudi.pca(kcponds$tab, scan = FALSE, nf = 4) if(requireNamespace("spdep", quietly = TRUE) & requireNamespace("adespatial", quietly = TRUE)) { multi1 <- adespatial::multispati(pca1, spdep::nb2listw(kcponds$nb), scannf = FALSE, nfposi = 2, nfnega = 1) summary(multi1) } par(mfrow = c(1, 1)) ## End(Not run)
An object of class kdist
is a list of distance matrices observed on the same individuals
kdist(..., epsi = 1e-07, upper = FALSE)
kdist(..., epsi = 1e-07, upper = FALSE)
... |
a sequence of objects of the class |
epsi |
a tolerance threshold to test if distances are Euclidean (Gower's theorem) using |
upper |
a logical value indicating whether the upper of a distance matrix is used (TRUE) or not (FALSE). |
The attributs of a 'kdist' object are:names
: the names of the distancessize
: the number of points between distances are knownlabels
: the labels of pointseuclid
: a logical vector indicating whether each distance of the list is Euclidean or not.call
: a call orderclass
: object 'kdist'
returns an object of class 'kdist' containing a list of semidefinite matrices.
Daniel Chessel
Anne-Béatrice Dufour [email protected]
Gower, J. C. (1966) Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika, 53, 325–338.
# starting from a list of matrices data(yanomama) lapply(yanomama,class) kd1 = kdist(yanomama) print(kd1) # giving the correlations of Mantel's test cor(as.data.frame(kd1)) pairs(as.data.frame(kd1)) # starting from a list of objects 'dist' data(friday87) fri.w <- ktab.data.frame(friday87$fau, friday87$fau.blo, tabnames = friday87$tab.names) fri.kd = lapply(1:10, function(x) dist.binary(fri.w[[x]],2)) names(fri.kd) = friday87$tab.names unlist(lapply(fri.kd,class)) # a list of distances fri.kd = kdist(fri.kd) fri.kd s.corcircle(dudi.pca(as.data.frame(fri.kd), scan = FALSE)$co) # starting from several distances data(ecomor) d1 <- dist.binary(ecomor$habitat, 1) d2 <- dist.prop(ecomor$forsub, 5) d3 <- dist.prop(ecomor$diet, 5) d4 <- dist.quant(ecomor$morpho, 3) d5 <- dist.taxo(ecomor$taxo) ecomor.kd <- kdist(d1, d2, d3, d4, d5) names(ecomor.kd) = c("habitat", "forsub", "diet", "morpho", "taxo") class(ecomor.kd) s.corcircle(dudi.pca(as.data.frame(ecomor.kd), scan = FALSE)$co) data(bsetal97) X <- prep.fuzzy.var(bsetal97$biol, bsetal97$biol.blo) w1 <- attr(X, "col.num") w2 <- levels(w1) w3 <- lapply(w2, function(x) dist.quant(X[,w1==x], method = 1)) names(w3) <- names(attr(X, "col.blocks")) w3 <- kdist(list = w3) s.corcircle(dudi.pca(as.data.frame(w3), scan = FALSE)$co) data(rpjdl) w1 = lapply(1:10, function(x) dist.binary(rpjdl$fau, method = x)) w2 = c("JACCARD", "SOKAL_MICHENER", "SOKAL_SNEATH_S4", "ROGERS_TANIMOTO") w2 = c(w2, "CZEKANOWSKI", "S9_GOWER_LEGENDRE", "OCHIAI", "SOKAL_SNEATH_S13") w2 <- c(w2, "Phi_PEARSON", "S2_GOWER_LEGENDRE") names(w1) <- w2 w3 = kdist(list = w1) w4 <- dudi.pca(as.data.frame(w3), scan = FALSE)$co w4
# starting from a list of matrices data(yanomama) lapply(yanomama,class) kd1 = kdist(yanomama) print(kd1) # giving the correlations of Mantel's test cor(as.data.frame(kd1)) pairs(as.data.frame(kd1)) # starting from a list of objects 'dist' data(friday87) fri.w <- ktab.data.frame(friday87$fau, friday87$fau.blo, tabnames = friday87$tab.names) fri.kd = lapply(1:10, function(x) dist.binary(fri.w[[x]],2)) names(fri.kd) = friday87$tab.names unlist(lapply(fri.kd,class)) # a list of distances fri.kd = kdist(fri.kd) fri.kd s.corcircle(dudi.pca(as.data.frame(fri.kd), scan = FALSE)$co) # starting from several distances data(ecomor) d1 <- dist.binary(ecomor$habitat, 1) d2 <- dist.prop(ecomor$forsub, 5) d3 <- dist.prop(ecomor$diet, 5) d4 <- dist.quant(ecomor$morpho, 3) d5 <- dist.taxo(ecomor$taxo) ecomor.kd <- kdist(d1, d2, d3, d4, d5) names(ecomor.kd) = c("habitat", "forsub", "diet", "morpho", "taxo") class(ecomor.kd) s.corcircle(dudi.pca(as.data.frame(ecomor.kd), scan = FALSE)$co) data(bsetal97) X <- prep.fuzzy.var(bsetal97$biol, bsetal97$biol.blo) w1 <- attr(X, "col.num") w2 <- levels(w1) w3 <- lapply(w2, function(x) dist.quant(X[,w1==x], method = 1)) names(w3) <- names(attr(X, "col.blocks")) w3 <- kdist(list = w3) s.corcircle(dudi.pca(as.data.frame(w3), scan = FALSE)$co) data(rpjdl) w1 = lapply(1:10, function(x) dist.binary(rpjdl$fau, method = x)) w2 = c("JACCARD", "SOKAL_MICHENER", "SOKAL_SNEATH_S4", "ROGERS_TANIMOTO") w2 = c(w2, "CZEKANOWSKI", "S9_GOWER_LEGENDRE", "OCHIAI", "SOKAL_SNEATH_S13") w2 <- c(w2, "Phi_PEARSON", "S2_GOWER_LEGENDRE") names(w1) <- w2 w3 = kdist(list = w1) w4 <- dudi.pca(as.data.frame(w3), scan = FALSE)$co w4
The function creates a ktab
object with the Euclidean representations from a kdist
object. Notice that the euclid attribute must be TRUE for all elements.
kdist2ktab(kd, scale = TRUE, tol = 1e-07)
kdist2ktab(kd, scale = TRUE, tol = 1e-07)
kd |
an object of class |
scale |
a logical value indicating whether the inertia of Euclidean representations are equal to 1 (TRUE) or not (FALSE). |
tol |
a tolerance threshold, an eigenvalue is considered equal to zero if |
returns a list of class ktab
containing for each distance of kd
the data frame of its Euclidean representation
Daniel Chessel
Anne-Béatrice Dufour [email protected]
data(friday87) fri.w <- ktab.data.frame(friday87$fau, friday87$fau.blo, tabnames = friday87$tab.names) fri.kd <- lapply(1:10, function(x) dist.binary(fri.w[[x]], 10)) names(fri.kd) <- substr(friday87$tab.names, 1, 4) fri.kd <- kdist(fri.kd) fri.ktab <- kdist2ktab(kd = fri.kd) fri.sepan <- sepan(fri.ktab) plot(fri.sepan) tapply(fri.sepan$Eig, fri.sepan$TC[,1], sum) # the sum of the eigenvalues is constant and equal to 1, for each K tables fri.statis <- statis(fri.ktab, scan = FALSE, nf = 2) round(fri.statis$RV, dig = 2) fri.mfa <- mfa(fri.ktab, scan = FALSE, nf = 2) fri.mcoa <- mcoa(fri.ktab, scan = FALSE, nf = 2) apply(fri.statis$RV, 1, mean) fri.statis$RV.tabw plot(apply(fri.statis$RV, 1, mean), fri.statis$RV.tabw) plot(fri.statis$RV.tabw, fri.statis$RV.tabw)
data(friday87) fri.w <- ktab.data.frame(friday87$fau, friday87$fau.blo, tabnames = friday87$tab.names) fri.kd <- lapply(1:10, function(x) dist.binary(fri.w[[x]], 10)) names(fri.kd) <- substr(friday87$tab.names, 1, 4) fri.kd <- kdist(fri.kd) fri.ktab <- kdist2ktab(kd = fri.kd) fri.sepan <- sepan(fri.ktab) plot(fri.sepan) tapply(fri.sepan$Eig, fri.sepan$TC[,1], sum) # the sum of the eigenvalues is constant and equal to 1, for each K tables fri.statis <- statis(fri.ktab, scan = FALSE, nf = 2) round(fri.statis$RV, dig = 2) fri.mfa <- mfa(fri.ktab, scan = FALSE, nf = 2) fri.mcoa <- mcoa(fri.ktab, scan = FALSE, nf = 2) apply(fri.statis$RV, 1, mean) fri.statis$RV.tabw plot(apply(fri.statis$RV, 1, mean), fri.statis$RV.tabw) plot(fri.statis$RV.tabw, fri.statis$RV.tabw)
a way to obtain Euclidean distance matrices
kdisteuclid(obj, method = c("lingoes", "cailliez", "quasi"))
kdisteuclid(obj, method = c("lingoes", "cailliez", "quasi"))
obj |
an object of class |
method |
a method to convert a distance matrix in a Euclidean one |
returns an object of class kdist
with all distances Euclidean.
Daniel Chessel
Stéphane Dray [email protected]
Gower, J.C. and Legendre, P. (1986) Metric and Euclidean properties of dissimilarity coefficients. Journal of Classification, 3, 5–48.
Cailliez, F. (1983) The analytical solution of the additive constant problem. Psychometrika, 48, 305–310.
Lingoes, J.C. (1971) Somme boundary conditions for a monotone analysis of symmetric matrices. Psychometrika, 36, 195–203.
Legendre, P. and Anderson, M.J. (1999) Distance-based redundancy analysis: testing multispecies responses in multifactorial ecological experiments. Ecological Monographs, 69, 1–24.
Legendre, P., and L. Legendre. (1998) Numerical ecology, 2nd English edition edition. Elsevier Science BV, Amsterdam.
w <- c(0.8, 0.8, 0.377350269, 0.8, 0.377350269, 0.377350269) # see ref. w <- kdist(w) w1 <- c(kdisteuclid(kdist(w), "lingoes"), kdisteuclid(kdist(w), "cailliez"), kdisteuclid(kdist(w), "quasi")) print(w, print = TRUE) print(w1, print = TRUE) data(eurodist) par(mfrow = c(1, 3)) eu1 <- kdist(eurodist) # an object of class 'dist' plot(data.frame(unclass(c(eu1, kdisteuclid(eu1, "quasi")))), asp = 1) title(main = "Quasi") abline(0,1) plot(data.frame(unclass(c(eu1, kdisteuclid(eu1, "lingoes")))), asp = 1) title(main = "Lingoes") abline(0,1) plot(data.frame(unclass(c(eu1, kdisteuclid(eu1, "cailliez")))), asp = 1) title(main = "Cailliez") abline(0,1)
w <- c(0.8, 0.8, 0.377350269, 0.8, 0.377350269, 0.377350269) # see ref. w <- kdist(w) w1 <- c(kdisteuclid(kdist(w), "lingoes"), kdisteuclid(kdist(w), "cailliez"), kdisteuclid(kdist(w), "quasi")) print(w, print = TRUE) print(w1, print = TRUE) data(eurodist) par(mfrow = c(1, 3)) eu1 <- kdist(eurodist) # an object of class 'dist' plot(data.frame(unclass(c(eu1, kdisteuclid(eu1, "quasi")))), asp = 1) title(main = "Quasi") abline(0,1) plot(data.frame(unclass(c(eu1, kdisteuclid(eu1, "lingoes")))), asp = 1) title(main = "Lingoes") abline(0,1) plot(data.frame(unclass(c(eu1, kdisteuclid(eu1, "cailliez")))), asp = 1) title(main = "Cailliez") abline(0,1)
Methods for foucart
, mcoa
, mfa
, pta
, sepan
, sepan.coa
and statis
kplot(object, ...)
kplot(object, ...)
object |
an object used to select a method |
... |
further arguments passed to or from other methods |
methods(plot) methods(scatter) methods(kplot)
methods(plot) methods(scatter) methods(kplot)
performs high level plots of a Foucart's Correspondence Analysis,
using an object of class foucart
.
## S3 method for class 'foucart' kplot(object, xax = 1, yax = 2, mfrow = NULL, which.tab = 1:length(object$blo), clab.r = 1, clab.c = 1.25, csub = 2, possub = "bottomright", ...)
## S3 method for class 'foucart' kplot(object, xax = 1, yax = 2, mfrow = NULL, which.tab = 1:length(object$blo), clab.r = 1, clab.c = 1.25, csub = 2, possub = "bottomright", ...)
object |
an object of class |
xax , yax
|
the numbers of the x-axis and the y-axis |
mfrow |
a vector of the form 'c(nr,nc)', otherwise computed by as special own function |
which.tab |
vector of table numbers for analyzing |
clab.r |
a character size for the row labels |
clab.c |
a character size for the column labels |
csub |
a character size for the sub-titles used with |
possub |
a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", "bottomright") |
... |
further arguments passed to or from other methods |
data(bf88) fou1 <- foucart(bf88, scann = FALSE, nf = 3) if(adegraphicsLoaded()) { g <- kplot(fou1, row.plab.cex = 0, psub.cex = 2) } else { kplot(fou1, clab.c = 2, clab.r = 0, csub = 3) }
data(bf88) fou1 <- foucart(bf88, scann = FALSE, nf = 3) if(adegraphicsLoaded()) { g <- kplot(fou1, row.plab.cex = 0, psub.cex = 2) } else { kplot(fou1, clab.c = 2, clab.r = 0, csub = 3) }
performs high level plots of a Multiple Co-inertia Analysis,
using an object of class mcoa
.
## S3 method for class 'mcoa' kplot(object, xax = 1, yax = 2, which.tab = 1:nrow(object$cov2), mfrow = NULL, option = c("points", "axis", "columns"), clab = 1, cpoint = 2, csub = 2, possub = "bottomright",...)
## S3 method for class 'mcoa' kplot(object, xax = 1, yax = 2, which.tab = 1:nrow(object$cov2), mfrow = NULL, option = c("points", "axis", "columns"), clab = 1, cpoint = 2, csub = 2, possub = "bottomright",...)
object |
an object of class |
xax , yax
|
the numbers of the x-axis and the y-axis |
which.tab |
a numeric vector containing the numbers of the tables to analyse |
mfrow |
a vector of the form 'c(nr,nc)', otherwise computed by as special own function |
option |
a string of characters for the drawing option
|
clab |
a character size for the labels |
cpoint |
a character size for plotting the points, used with |
csub |
a character size for the sub-titles, used with |
possub |
a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", "bottomright") |
... |
further arguments passed to or from other methods |
Daniel Chessel
data(friday87) w1 <- data.frame(scale(friday87$fau, scal = FALSE)) w2 <- ktab.data.frame(w1, friday87$fau.blo, tabnames = friday87$tab.names) mcoa1 <- mcoa(w2, "lambda1", scan = FALSE) kplot(mcoa1, option = "axis") kplot(mcoa1) kplot(mcoa1, option = "columns")
data(friday87) w1 <- data.frame(scale(friday87$fau, scal = FALSE)) w2 <- ktab.data.frame(w1, friday87$fau.blo, tabnames = friday87$tab.names) mcoa1 <- mcoa(w2, "lambda1", scan = FALSE) kplot(mcoa1, option = "axis") kplot(mcoa1) kplot(mcoa1, option = "columns")
performs high level plots of a Multiple Factorial Analysis,
using an object of class mfa
.
## S3 method for class 'mfa' kplot(object, xax = 1, yax = 2, mfrow = NULL, which.tab = 1:length(object$blo), row.names = FALSE, col.names = TRUE, traject = FALSE, permute.row.col = FALSE, clab = 1, csub = 2, possub = "bottomright", ...)
## S3 method for class 'mfa' kplot(object, xax = 1, yax = 2, mfrow = NULL, which.tab = 1:length(object$blo), row.names = FALSE, col.names = TRUE, traject = FALSE, permute.row.col = FALSE, clab = 1, csub = 2, possub = "bottomright", ...)
object |
an object of class |
xax , yax
|
the numbers of the x-axis and the y-axis |
mfrow |
a vector of the form 'c(nr,nc'), otherwise computed by a special own function |
which.tab |
vector of the numbers of tables used for the analysis |
row.names |
a logical value indicating whether the row labels should be inserted |
col.names |
a logical value indicating whether the column labels should be inserted |
traject |
a logical value indicating whether the trajectories of the rows should be drawn in a natural order |
permute.row.col |
if TRUE, the rows are represented by vectors and columns by points, otherwise it is the opposite |
clab |
a character size for the labels |
csub |
a character size for the sub-titles, used with |
possub |
a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", "bottomright") |
... |
further arguments passed to or from other methods |
Daniel Chessel
data(friday87) w1 <- data.frame(scale(friday87$fau, scal = FALSE)) w2 <- ktab.data.frame(w1, friday87$fau.blo, tabnames = friday87$tab.names) mfa1 <- mfa(w2, scann = FALSE) kplot(mfa1)
data(friday87) w1 <- data.frame(scale(friday87$fau, scal = FALSE)) w2 <- ktab.data.frame(w1, friday87$fau.blo, tabnames = friday87$tab.names) mfa1 <- mfa(w2, scann = FALSE) kplot(mfa1)
performs high level plots of a Partial Triadic Analysis,
using an object of class pta
.
## S3 method for class 'pta' kplot(object, xax = 1, yax = 2, which.tab = 1:nrow(object$RV), mfrow = NULL, which.graph = 1:4, clab = 1, cpoint = 2, csub = 2, possub = "bottomright", ask = par("ask"), ...)
## S3 method for class 'pta' kplot(object, xax = 1, yax = 2, which.tab = 1:nrow(object$RV), mfrow = NULL, which.graph = 1:4, clab = 1, cpoint = 2, csub = 2, possub = "bottomright", ask = par("ask"), ...)
object |
an object of class |
xax , yax
|
the numbers of the x-axis and the y-axis |
which.tab |
a numeric vector containing the numbers of the tables to analyse |
mfrow |
parameter of the array of figures to be drawn, otherwise the graphs associated to a table are drawn on the same row |
which.graph |
an option for drawing, an integer between 1 and 4. For each table of which.tab, are drawn :
|
clab |
a character size for the labels |
cpoint |
a character size for plotting the points, used with |
csub |
a character size for the sub-titles, used with |
possub |
a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", "bottomright") |
ask |
a logical value indicating if the graphs requires several arrays of figures |
... |
further arguments passed to or from other methods |
Daniel Chessel
data(meaudret) wit1 <- wca(dudi.pca(meaudret$spe, scan = FALSE, scal = FALSE), meaudret$design$season, scan = FALSE) kta1 <- ktab.within(wit1, colnames = rep(c("S1", "S2", "S3", "S4", "S5"), 4)) kta2 <- t(kta1) pta1 <- pta(kta2, scann = FALSE) kplot(pta1) kplot(pta1, which.graph = 3)
data(meaudret) wit1 <- wca(dudi.pca(meaudret$spe, scan = FALSE, scal = FALSE), meaudret$design$season, scan = FALSE) kta1 <- ktab.within(wit1, colnames = rep(c("S1", "S2", "S3", "S4", "S5"), 4)) kta2 <- t(kta1) pta1 <- pta(kta2, scann = FALSE) kplot(pta1) kplot(pta1, which.graph = 3)
performs high level plots for Separed Analyses in a K-tables,
using an object of class sepan
.
## S3 method for class 'sepan' kplot(object, xax = 1, yax = 2, which.tab = 1:length(object$blo), mfrow = NULL, permute.row.col = FALSE, clab.row = 1, clab.col = 1.25, traject.row = FALSE, csub = 2, possub = "bottomright", show.eigen.value = TRUE,...) kplotsepan.coa(object, xax = 1, yax = 2, which.tab = 1:length(object$blo), mfrow = NULL, permute.row.col = FALSE, clab.row = 1, clab.col = 1.25, csub = 2, possub = "bottomright", show.eigen.value = TRUE, poseig = c("bottom", "top"), ...)
## S3 method for class 'sepan' kplot(object, xax = 1, yax = 2, which.tab = 1:length(object$blo), mfrow = NULL, permute.row.col = FALSE, clab.row = 1, clab.col = 1.25, traject.row = FALSE, csub = 2, possub = "bottomright", show.eigen.value = TRUE,...) kplotsepan.coa(object, xax = 1, yax = 2, which.tab = 1:length(object$blo), mfrow = NULL, permute.row.col = FALSE, clab.row = 1, clab.col = 1.25, csub = 2, possub = "bottomright", show.eigen.value = TRUE, poseig = c("bottom", "top"), ...)
object |
an object of class |
xax , yax
|
the numbers of the x-axis and the y-axis |
which.tab |
a numeric vector containing the numbers of the tables to analyse |
mfrow |
parameter for the array of figures to be drawn, otherwise use n2mfrow |
permute.row.col |
if TRUE the rows are represented by arrows and the columns by points, if FALSE it is the opposite |
clab.row |
a character size for the row labels |
clab.col |
a character size for the column labels |
traject.row |
a logical value indicating whether the trajectories between rows should be drawn in a natural order |
csub |
a character size for the sub-titles, used with |
possub |
a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", "bottomright") |
show.eigen.value |
a logical value indicating whether the eigenvalues bar plot should be drawn |
poseig |
if "top" the eigenvalues bar plot is upside, if "bottom", it is downside |
... |
further arguments passed to or from other methods |
kplot.sepan
superimposes the points for the rows and the arrows for the columns using an
adapted rescaling such as the scatter.dudi
.kplotsepan.coa
superimposes the row coordinates and the column coordinates with the same scale.
Daniel Chessel
data(escopage) w1 <- data.frame(scale(escopage$tab)) w1 <- ktab.data.frame(w1, escopage$blo, tabnames = escopage$tab.names) sep1 <- sepan(w1) if(adegraphicsLoaded()) { kplot(sep1, posieig = "none") } else { kplot(sep1, show = FALSE) } data(friday87) w2 <- data.frame(scale(friday87$fau, scal = FALSE)) w2 <- ktab.data.frame(w2, friday87$fau.blo, tabnames = friday87$tab.names) if(adegraphicsLoaded()) { kplot(sepan(w2), row.plabel.cex = 1.25, col.plab.cex = 0) } else { kplot(sepan(w2), clab.r = 1.25, clab.c = 0) } data(microsatt) w3 <- dudi.coa(data.frame(t(microsatt$tab)), scann = FALSE) loci.fac <- factor(rep(microsatt$loci.names, microsatt$loci.eff)) wit <- wca(w3, loci.fac, scann = FALSE) microsatt.ktab <- ktab.within(wit) if(adegraphicsLoaded()) { kplotsepan.coa(sepan(microsatt.ktab), posieig = "none", col.plab.cex = 0, row.plab.cex = 1.5) } else { kplotsepan.coa(sepan(microsatt.ktab), show = FALSE, clab.c = 0, mfrow = c(3,3), clab.r = 1.5) }
data(escopage) w1 <- data.frame(scale(escopage$tab)) w1 <- ktab.data.frame(w1, escopage$blo, tabnames = escopage$tab.names) sep1 <- sepan(w1) if(adegraphicsLoaded()) { kplot(sep1, posieig = "none") } else { kplot(sep1, show = FALSE) } data(friday87) w2 <- data.frame(scale(friday87$fau, scal = FALSE)) w2 <- ktab.data.frame(w2, friday87$fau.blo, tabnames = friday87$tab.names) if(adegraphicsLoaded()) { kplot(sepan(w2), row.plabel.cex = 1.25, col.plab.cex = 0) } else { kplot(sepan(w2), clab.r = 1.25, clab.c = 0) } data(microsatt) w3 <- dudi.coa(data.frame(t(microsatt$tab)), scann = FALSE) loci.fac <- factor(rep(microsatt$loci.names, microsatt$loci.eff)) wit <- wca(w3, loci.fac, scann = FALSE) microsatt.ktab <- ktab.within(wit) if(adegraphicsLoaded()) { kplotsepan.coa(sepan(microsatt.ktab), posieig = "none", col.plab.cex = 0, row.plab.cex = 1.5) } else { kplotsepan.coa(sepan(microsatt.ktab), show = FALSE, clab.c = 0, mfrow = c(3,3), clab.r = 1.5) }
performs high level plots for a STATIS analysis,
using an object of class statis
.
## S3 method for class 'statis' kplot(object, xax = 1, yax = 2, mfrow = NULL, which.tab = 1:length(object$tab.names), clab = 1.5, cpoi = 2, traject = FALSE, arrow = TRUE, class = NULL, unique.scale = FALSE, csub = 2, possub = "bottomright",...)
## S3 method for class 'statis' kplot(object, xax = 1, yax = 2, mfrow = NULL, which.tab = 1:length(object$tab.names), clab = 1.5, cpoi = 2, traject = FALSE, arrow = TRUE, class = NULL, unique.scale = FALSE, csub = 2, possub = "bottomright",...)
object |
an object of class |
xax , yax
|
the numbers of the x-axis and the y-axis |
mfrow |
parameter for the array of figures to be drawn |
which.tab |
a numeric vector containing the numbers of the tables to analyse |
clab |
a character size for the labels |
cpoi |
the size of points |
traject |
a logical value indicating whether the trajectories should be drawn in a natural order |
arrow |
a logical value indicating whether the column factorial diagrams should be plotted |
class |
if not NULL, a factor of length equal to the number of the total columns of the K-tables |
unique.scale |
if TRUE, all the arrays of figures have the same scale |
csub |
a character size for the labels of the arrays of figures used with |
possub |
a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", "bottomright") |
... |
further arguments passed to or from other methods |
Daniel Chessel
data(jv73) dudi1 <- dudi.pca(jv73$poi, scann = FALSE, scal = FALSE) wit1 <- wca(dudi1, jv73$fac.riv, scann = FALSE) kta1 <- ktab.within(wit1) statis1 <- statis(kta1, scann = FALSE) if(adegraphicsLoaded()) { g1 <- kplot(statis1, traj = TRUE, arrow = FALSE, plab.cex = 0, psub.cex = 2, ppoi.cex = 2) } else { kplot(statis1, traj = TRUE, arrow = FALSE, unique = TRUE, clab = 0, csub = 2, cpoi = 2) }
data(jv73) dudi1 <- dudi.pca(jv73$poi, scann = FALSE, scal = FALSE) wit1 <- wca(dudi1, jv73$fac.riv, scann = FALSE) kta1 <- ktab.within(wit1) statis1 <- statis(kta1, scann = FALSE) if(adegraphicsLoaded()) { g1 <- kplot(statis1, traj = TRUE, arrow = FALSE, plab.cex = 0, psub.cex = 2, ppoi.cex = 2) } else { kplot(statis1, traj = TRUE, arrow = FALSE, unique = TRUE, clab = 0, csub = 2, cpoi = 2) }
Plot, print and extract permutation tests. Objects of class 'krandtest'
are lists.
as.krandtest(sim, obs, alter = "greater", call = match.call(), names = colnames(sim), p.adjust.method = "none", output = c("light", "full")) ## S3 method for class 'krandtest' plot(x, mfrow = NULL, nclass = 10, main.title = x$names, ...) ## S3 method for class 'krandtest' print(x, ...) ## S3 method for class 'krandtest' x[i] ## S3 method for class 'krandtest' x[[i]]
as.krandtest(sim, obs, alter = "greater", call = match.call(), names = colnames(sim), p.adjust.method = "none", output = c("light", "full")) ## S3 method for class 'krandtest' plot(x, mfrow = NULL, nclass = 10, main.title = x$names, ...) ## S3 method for class 'krandtest' print(x, ...) ## S3 method for class 'krandtest' x[i] ## S3 method for class 'krandtest' x[[i]]
sim |
a matrix or data.frame of simulated values (repetitions as rows, number of tests as columns |
obs |
a numeric vector of observed values for each test |
alter |
a vector of character specifying the alternative hypothesis for each test. Each element must be one of "greater" (default), "less" or "two-sided". The length must be equal to the length of the vector obs, values are recycled if shorter. |
call |
a call order |
names |
a vector of names for tests |
p.adjust.method |
a string indicating a method for multiple adjustment, see |
output |
a character string specifying if all simulations should be stored ( |
x |
an object of class |
mfrow |
a vector of the form 'c(nr,nc)', otherwise computed by as special own function |
nclass |
a number of intervals for the histogram. Ignored if object output is |
main.title |
a string of character for the main title |
... |
further arguments passed to or from other methods |
i |
numeric indices specifying elements to extract |
plot.krandtest
draws the p simulated values histograms and the position of the observed value.
[.krandtest
returns a krandtest
object and
[[.krandtest
returns a randtest
object.
Daniel Chessel and Stéphane Dray [email protected]
wkrandtest <- as.krandtest(obs = c(0, 1.2, 2.4, 3.4, 5.4, 20.4), sim = matrix(rnorm(6*200), 200, 6)) wkrandtest plot(wkrandtest) wkrandtest[c(1, 4, 6)] wkrandtest[[1]]
wkrandtest <- as.krandtest(obs = c(0, 1.2, 2.4, 3.4, 5.4, 20.4), sim = matrix(rnorm(6*200), 200, 6)) wkrandtest plot(wkrandtest) wkrandtest[c(1, 4, 6)] wkrandtest[[1]]
an object of class ktab
is a list of data frames with the same row.names in common.
a list of class 'ktab' contains moreover :
: the vector of the numbers of columns for each table
: the vector of the row weightings in common for all tables
: the vector of the column weightings
: a data frame of two components to manage the parameter positions associated with the rows of tables
: a data frame of two components to manage the parameter positions associated with the columns of tables
: a data frame of two components to manage the parameter positions of 4 components associated to an array
## S3 method for class 'ktab' c(...) ## S3 method for class 'ktab' x[i,j,k] is.ktab(x) ## S3 method for class 'ktab' t(x) ## S3 method for class 'ktab' row.names(x) ## S3 method for class 'ktab' col.names(x) tab.names(x) col.names(x) ktab.util.names(x)
## S3 method for class 'ktab' c(...) ## S3 method for class 'ktab' x[i,j,k] is.ktab(x) ## S3 method for class 'ktab' t(x) ## S3 method for class 'ktab' row.names(x) ## S3 method for class 'ktab' col.names(x) tab.names(x) col.names(x) ktab.util.names(x)
x |
an object of the class |
... |
a sequence of objects of the class |
i , j , k
|
elements to extract (integer or empty): index of tables (i), rows (j) and columns (k) |
A 'ktab' object can be created with :
a list of data frame : ktab.list.df
a list of dudi
objects : ktab.list.dudi
a data.frame : ktab.data.frame
an object within
: ktab.within
a couple of ktab
s : ktab.match2ktabs
c.ktab
returns an object ktab
. It concatenates K-tables with the same rows in common. t.ktab
returns an object ktab
. It permutes each data frame into a K-tables. All tables have the same column names and the same column weightings (a data cube). "["
returns an object ktab
. It allows to select some arrays in a K-tables. is.ktab
returns TRUE if x is a K-tables. row.names
returns the vector of the row names common with all the tables of a K-tables and allowes to modifie them.col.names
returns the vector of the column names of a K-tables and allowes to modifie them.tab.names
returns the vector of the array names of a K-tables and allowes to modifie them.ktab.util.names
is a useful function.
Daniel Chessel
Anne-Béatrice Dufour [email protected]
Stéphane Dray [email protected]
data(friday87) wfri <- data.frame(scale(friday87$fau, scal = FALSE)) wfri <- ktab.data.frame(wfri, friday87$fau.blo) wfri[2:4, 1:5, 1:3] c(wfri[2:4], wfri[5]) data(meaudret) wit1 <- withinpca(meaudret$env, meaudret$design$season, scan = FALSE, scal = "partial") kta1 <- ktab.within(wit1, colnames = rep(c("S1","S2","S3","S4","S5"), 4)) kta2 <- t(kta1) if(adegraphicsLoaded()) { kplot(sepan(kta2), row.plab.cex = 1.5, col.plab.cex = 0.75) } else { kplot(sepan(kta2), clab.r = 1.5, clab.c = 0.75) }
data(friday87) wfri <- data.frame(scale(friday87$fau, scal = FALSE)) wfri <- ktab.data.frame(wfri, friday87$fau.blo) wfri[2:4, 1:5, 1:3] c(wfri[2:4], wfri[5]) data(meaudret) wit1 <- withinpca(meaudret$env, meaudret$design$season, scan = FALSE, scal = "partial") kta1 <- ktab.within(wit1, colnames = rep(c("S1","S2","S3","S4","S5"), 4)) kta2 <- t(kta1) if(adegraphicsLoaded()) { kplot(sepan(kta2), row.plab.cex = 1.5, col.plab.cex = 0.75) } else { kplot(sepan(kta2), clab.r = 1.5, clab.c = 0.75) }
creates K tables from a data frame.
ktab.data.frame(df, blocks, rownames = NULL, colnames = NULL, tabnames = NULL, w.row = rep(1, nrow(df)) / nrow(df), w.col = rep(1, ncol(df)))
ktab.data.frame(df, blocks, rownames = NULL, colnames = NULL, tabnames = NULL, w.row = rep(1, nrow(df)) / nrow(df), w.col = rep(1, ncol(df)))
df |
a data frame |
blocks |
an integer vector for which the sum must be the number of variables of df. Its length is the number of arrays of the K-tables |
rownames |
the row names of the K-tables (otherwise the row names of df) |
colnames |
the column names of the K-tables (otherwise the column names of df) |
tabnames |
the names of the arrays of the K-tables (otherwise "Ana1", "Ana2", ...) |
w.row |
a vector of the row weightings |
w.col |
a vector of the column weightings |
returns a list of class ktab
. See ktab
.
Daniel Chessel
Anne-Béatrice Dufour [email protected]
data(escopage) wescopage <- data.frame(scalewt(escopage$tab)) wescopage <- ktab.data.frame(wescopage, escopage$blo, tabnames = escopage$tab.names) plot(sepan(wescopage)) data(friday87) w <- data.frame(scale(friday87$fau, scal = FALSE)) w <- ktab.data.frame(w, friday87$fau.blo, tabnames = friday87$tab.names) kplot(sepan(w))
data(escopage) wescopage <- data.frame(scalewt(escopage$tab)) wescopage <- ktab.data.frame(wescopage, escopage$blo, tabnames = escopage$tab.names) plot(sepan(wescopage)) data(friday87) w <- data.frame(scale(friday87$fau, scal = FALSE)) w <- ktab.data.frame(w, friday87$fau.blo, tabnames = friday87$tab.names) kplot(sepan(w))
creates a list of class ktab
from a list of data frames
ktab.list.df(obj, rownames = NULL, colnames = NULL, tabnames = NULL, w.row = rep(1, nrow(obj[[1]])), w.col = lapply(obj, function(x) rep(1 / ncol(x), ncol(x))))
ktab.list.df(obj, rownames = NULL, colnames = NULL, tabnames = NULL, w.row = rep(1, nrow(obj[[1]])), w.col = lapply(obj, function(x) rep(1 / ncol(x), ncol(x))))
obj |
a list of data frame |
rownames |
the names of the K-tables rows (otherwise, the row names of the arrays) |
colnames |
the names of the K-tables columns (otherwise, the column names of the arrays) |
tabnames |
the names of the arrays of the K-tables (otherwise, the names of the obj if they exist, or else "Ana1", "Ana2", ...) |
w.row |
a vector of the row weightings in common with all the arrays |
w.col |
a list of the vector of the column weightings for each array |
Each element of the initial list have to possess the same names and row numbers
returns a list of class ktab
. See ktab
Daniel Chessel
Anne-Béatrice Dufour [email protected]
data(jv73) l0 <- split(jv73$morpho, jv73$fac.riv) l0 <- lapply(l0, function(x) data.frame(t(scalewt(x)))) kta <- ktab.list.df(l0) kplot(sepan(kta[c(2, 5, 7, 10)]), perm = TRUE)
data(jv73) l0 <- split(jv73$morpho, jv73$fac.riv) l0 <- lapply(l0, function(x) data.frame(t(scalewt(x)))) kta <- ktab.list.df(l0) kplot(sepan(kta[c(2, 5, 7, 10)]), perm = TRUE)
creates a list of class ktab
from a list of duality diagrams.
ktab.list.dudi(obj, rownames = NULL, colnames = NULL, tabnames = NULL)
ktab.list.dudi(obj, rownames = NULL, colnames = NULL, tabnames = NULL)
obj |
a list of objects of class 'dudi'. Each element of the list must have the same row names for |
rownames |
the row names of the K-tables (otherwise the row names of the |
colnames |
the column names of the K-tables (otherwise the column names of the |
tabnames |
the names of the arrays of the K-tables (otherwise the names of the |
returns a list of class ktab
. See ktab
Daniel Chessel
Anne-Béatrice Dufour [email protected]
data(euro123) pca1 <- dudi.pca(euro123$in78, scale = FALSE, scann = FALSE) pca2 <- dudi.pca(euro123$in86, scale = FALSE, scann = FALSE) pca3 <- dudi.pca(euro123$in97, scale = FALSE, scann = FALSE) ktabeuro <- ktab.list.dudi(list(pca1, pca2, pca3), tabnames = c("1978", "1986", "1997")) if(adegraphicsLoaded()) { kplot(sepan(ktabeuro)) } else { kplot(sepan(ktabeuro), mfr = c(2, 2), clab.c = 1.5) } data(meaudret) w1 <- split(meaudret$env,meaudret$design$season) ll <- lapply(w1, dudi.pca, scann = FALSE) kta <- ktab.list.dudi(ll, rownames <- paste("Site", 1:5, sep = "")) if(adegraphicsLoaded()) { kplot(sepan(kta), row.plab.cex = 1.5, col.plab.cex = 0.75) } else { kplot(sepan(kta), clab.r = 1.5, clab.c = 0.75) } data(jv73) w <- split(jv73$poi, jv73$fac.riv) wjv73poi <- lapply(w, dudi.pca, scal = FALSE, scan = FALSE) wjv73poi <- lapply(wjv73poi, t) wjv73poi <- ktab.list.dudi(wjv73poi) kplot(sepan(wjv73poi), permut = TRUE, traj = TRUE)
data(euro123) pca1 <- dudi.pca(euro123$in78, scale = FALSE, scann = FALSE) pca2 <- dudi.pca(euro123$in86, scale = FALSE, scann = FALSE) pca3 <- dudi.pca(euro123$in97, scale = FALSE, scann = FALSE) ktabeuro <- ktab.list.dudi(list(pca1, pca2, pca3), tabnames = c("1978", "1986", "1997")) if(adegraphicsLoaded()) { kplot(sepan(ktabeuro)) } else { kplot(sepan(ktabeuro), mfr = c(2, 2), clab.c = 1.5) } data(meaudret) w1 <- split(meaudret$env,meaudret$design$season) ll <- lapply(w1, dudi.pca, scann = FALSE) kta <- ktab.list.dudi(ll, rownames <- paste("Site", 1:5, sep = "")) if(adegraphicsLoaded()) { kplot(sepan(kta), row.plab.cex = 1.5, col.plab.cex = 0.75) } else { kplot(sepan(kta), clab.r = 1.5, clab.c = 0.75) } data(jv73) w <- split(jv73$poi, jv73$fac.riv) wjv73poi <- lapply(w, dudi.pca, scal = FALSE, scan = FALSE) wjv73poi <- lapply(wjv73poi, t) wjv73poi <- ktab.list.dudi(wjv73poi) kplot(sepan(wjv73poi), permut = TRUE, traj = TRUE)
Prepares the analysis of a series of paired ecological tables. Partial Triadic
Analysis (see pta
) can be used thereafter to perform the analysis of this k-table.
ktab.match2ktabs(KTX, KTY)
ktab.match2ktabs(KTX, KTY)
KTX |
an objet of class |
KTY |
an objet of class |
a list of class ktab
, subclass kcoinertia
. See ktab
IMPORTANT : KTX
and KTY
must have the same k-tables structure, the same number
of columns, and the same column weights.
Jean Thioulouse [email protected]
Thioulouse J., Simier M. and Chessel D. (2004). Simultaneous analysis of a sequence of paired ecological tables. Ecology 85, 272-283..
Simier, M., Blanc L., Pellegrin F., and Nandris D. (1999). Approche simultanée de K couples de tableaux : Application a l'étude des relations pathologie végétale - environnement. Revue de Statistique Appliquée, 47, 31-46.
data(meau) wit1 <- withinpca(meau$env, meau$design$season, scan = FALSE, scal = "total") pcaspe <- dudi.pca(meau$spe, scale = FALSE, scan = FALSE, nf = 2) wit2 <- wca(pcaspe, meau$design$season, scan = FALSE, nf = 2) kta1 <- ktab.within(wit1, colnames = rep(c("S1","S2","S3","S4","S5","S6"), 4)) kta2 <- ktab.within(wit2, colnames = rep(c("S1","S2","S3","S4","S5","S6"), 4)) kcoi <- ktab.match2ktabs(kta1, kta2) ptacoi <- pta(kcoi, scan = FALSE, nf = 2) plot(ptacoi) kplot(ptacoi)
data(meau) wit1 <- withinpca(meau$env, meau$design$season, scan = FALSE, scal = "total") pcaspe <- dudi.pca(meau$spe, scale = FALSE, scan = FALSE, nf = 2) wit2 <- wca(pcaspe, meau$design$season, scan = FALSE, nf = 2) kta1 <- ktab.within(wit1, colnames = rep(c("S1","S2","S3","S4","S5","S6"), 4)) kta2 <- ktab.within(wit2, colnames = rep(c("S1","S2","S3","S4","S5","S6"), 4)) kcoi <- ktab.match2ktabs(kta1, kta2) ptacoi <- pta(kcoi, scan = FALSE, nf = 2) plot(ptacoi) kplot(ptacoi)
performs the process to go from a Within Analysis to a K-tables.
ktab.within(dudiwit, rownames = NULL, colnames = NULL, tabnames = NULL)
ktab.within(dudiwit, rownames = NULL, colnames = NULL, tabnames = NULL)
dudiwit |
an objet of class |
rownames |
the row names of the K-tables (otherwise the row names of |
colnames |
the column names of the K-tables (otherwise the column names |
tabnames |
the names of the arrays of the K-tables (otherwise the levels of the factor which defines the within-classes) |
a list of class ktab
. See ktab
Daniel Chessel
Anne-Béatrice Dufour [email protected]
data(bacteria) w1 <- data.frame(t(bacteria$espcodon)) dudi1 <- dudi.coa(w1, scann = FALSE, nf = 4) wit1 <- wca(dudi1, bacteria$code, scannf = FALSE) kta1 <- ktab.within(wit1) plot(statis(kta1, scann = FALSE)) kta2 <- kta1[kta1$blo>3] kplot(mfa(kta2, scann = FALSE))
data(bacteria) w1 <- data.frame(t(bacteria$espcodon)) dudi1 <- dudi.coa(w1, scann = FALSE, nf = 4) wit1 <- wca(dudi1, bacteria$code, scannf = FALSE) kta1 <- ktab.within(wit1) plot(statis(kta1, scann = FALSE)) kta2 <- kta1[kta1$blo>3] kplot(mfa(kta2, scann = FALSE))
This data set gives meristic, genetic and morphological data frame for 306 trouts.
data(lascaux)
data(lascaux)
lascaux
is a list of 9 components.
is a factor returning the river where 306 trouts are captured
vector of characters : code of the 306 trouts
factor sex of the 306 trouts
data frame 306 trouts - 5 meristic variables
data frame of the total number of red and black points
factor of the genetic code of the 306 trouts
data frame 306 trouts 37 morphological variables
data frame 306 trouts 15 variables of coloring
data frame 306 trouts 15 factors (ornementation)
Lascaux, J.M. (1996) Analyse de la variabilité morphologique de la truite commune (Salmo trutta L.) dans les cours d'eau du bassin pyrénéen méditerranéen. Thèse de doctorat en sciences agronomiques, INP Toulouse.
See a data description at http://pbil.univ-lyon1.fr/R/pdf/pps022.pdf (in French).
data(lascaux) if(adegraphicsLoaded()) { g1 <- s1d.barchart(dudi.pca(lascaux$meris, scan = FALSE)$eig, psub.text = "Meristic", p1d.horizontal = FALSE, plot = FALSE) g2 <- s1d.barchart(dudi.pca(lascaux$colo, scan = FALSE)$eig, psub.text = "Coloration", p1d.horizontal = FALSE, plot = FALSE) g3 <- s1d.barchart(dudi.pca(na.omit(lascaux$morpho), scan = FALSE)$eig, psub.text = "Morphometric", p1d.horizontal = FALSE, plot = FALSE) g4 <- s1d.barchart(dudi.acm(na.omit(lascaux$orne), scan = FALSE)$eig, psub.text = "Ornemental", p1d.horizontal = FALSE, plot = FALSE) G <- ADEgS(c(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2,2)) barplot(dudi.pca(lascaux$meris, scan = FALSE)$eig) title(main = "Meristic") barplot(dudi.pca(lascaux$colo, scan = FALSE)$eig) title(main = "Coloration") barplot(dudi.pca(na.omit(lascaux$morpho), scan = FALSE)$eig) title(main = "Morphometric") barplot(dudi.acm(na.omit(lascaux$orne), scan = FALSE)$eig) title(main = "Ornemental") par(mfrow = c(1,1)) }
data(lascaux) if(adegraphicsLoaded()) { g1 <- s1d.barchart(dudi.pca(lascaux$meris, scan = FALSE)$eig, psub.text = "Meristic", p1d.horizontal = FALSE, plot = FALSE) g2 <- s1d.barchart(dudi.pca(lascaux$colo, scan = FALSE)$eig, psub.text = "Coloration", p1d.horizontal = FALSE, plot = FALSE) g3 <- s1d.barchart(dudi.pca(na.omit(lascaux$morpho), scan = FALSE)$eig, psub.text = "Morphometric", p1d.horizontal = FALSE, plot = FALSE) g4 <- s1d.barchart(dudi.acm(na.omit(lascaux$orne), scan = FALSE)$eig, psub.text = "Ornemental", p1d.horizontal = FALSE, plot = FALSE) G <- ADEgS(c(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2,2)) barplot(dudi.pca(lascaux$meris, scan = FALSE)$eig) title(main = "Meristic") barplot(dudi.pca(lascaux$colo, scan = FALSE)$eig) title(main = "Coloration") barplot(dudi.pca(na.omit(lascaux$morpho), scan = FALSE)$eig) title(main = "Morphometric") barplot(dudi.acm(na.omit(lascaux$orne), scan = FALSE)$eig) title(main = "Ornemental") par(mfrow = c(1,1)) }
transforms a distance matrix in a Euclidean one.
lingoes(distmat, print = FALSE, tol = 1e-07, cor.zero = TRUE)
lingoes(distmat, print = FALSE, tol = 1e-07, cor.zero = TRUE)
distmat |
an object of class |
print |
if TRUE, prints the eigenvalues of the matrix |
tol |
a tolerance threshold for zero |
cor.zero |
if TRUE, zero distances are not modified |
The function uses the smaller positive constant k which transforms the matrix of in an Euclidean one
returns an object of class dist
with a Euclidean distance
Daniel Chessel
Stéphane Dray [email protected]
Lingoes, J.C. (1971) Some boundary conditions for a monotone analysis of symmetric matrices. Psychometrika, 36, 195–203.
data(capitales) d0 <- capitales$dist is.euclid(d0) # FALSE d1 <- lingoes(d0, TRUE) # Lingoes constant = 2120982 is.euclid(d1) # TRUE plot(d0, d1) x0 <- sort(unclass(d0)) lines(x0, sqrt(x0^2 + 2 * 2120982), lwd = 3) is.euclid(sqrt(d0^2 + 2 * 2120981), tol = 1e-10) # FALSE is.euclid(sqrt(d0^2 + 2 * 2120982), tol = 1e-10) # FALSE is.euclid(sqrt(d0^2 + 2 * 2120983), tol = 1e-10) # TRUE the smaller constant
data(capitales) d0 <- capitales$dist is.euclid(d0) # FALSE d1 <- lingoes(d0, TRUE) # Lingoes constant = 2120982 is.euclid(d1) # TRUE plot(d0, d1) x0 <- sort(unclass(d0)) lines(x0, sqrt(x0^2 + 2 * 2120982), lwd = 3) is.euclid(sqrt(d0^2 + 2 * 2120981), tol = 1e-10) # FALSE is.euclid(sqrt(d0^2 + 2 * 2120982), tol = 1e-10) # FALSE is.euclid(sqrt(d0^2 + 2 * 2120983), tol = 1e-10) # TRUE the smaller constant
This data set describes the phylogeny of 18 lizards as reported by Bauwens and Díaz-Uriarte (1997). It also gives life-history traits corresponding to these 18 species.
data(lizards)
data(lizards)
lizards
is a list containing the 3 following objects :
is a data frame with 18 species and 8 traits.
is a character string giving the phylogenetic tree (hypothesized phylogenetic relationships based on immunological distances) in Newick format.
is a character string giving the phylogenetic tree (hypothesized phylogenetic relationships based on morphological characteristics) in Newick format.
Variables of lizards$traits
are the following ones :
mean.L (mean length (mm)), matur.L (length at maturity (mm)),
max.L (maximum length (mm)), hatch.L (hatchling length (mm)),
hatch.m (hatchling mass (g)), clutch.S (Clutch size),
age.mat (age at maturity (number of months of activity)),
clutch.F (clutch frequency).
Bauwens, D., and Díaz-Uriarte, R. (1997) Covariation of life-history traits in lacertid lizards: a comparative study. American Naturalist, 149, 91–111.
See a data description at http://pbil.univ-lyon1.fr/R/pdf/pps063.pdf (in French).
data(lizards) w <- data.frame(scalewt(log(lizards$traits))) par(mfrow = c(1,2)) wphy <- newick2phylog(lizards$hprA) table.phylog(w, wphy, csi = 3) wphy <- newick2phylog(lizards$hprB) table.phylog(w, wphy, csi = 3) par(mfrow = c(1,1))
data(lizards) w <- data.frame(scalewt(log(lizards$traits))) par(mfrow = c(1,2)) wphy <- newick2phylog(lizards$hprA) table.phylog(w, wphy, csi = 3) wphy <- newick2phylog(lizards$hprB) table.phylog(w, wphy, csi = 3) par(mfrow = c(1,1))
bca
Leave-one-out cross-validation for bca
.
## S3 method for class 'between' loocv(x, nax = 0, progress = FALSE, parallel = FALSE, ...) ## S3 method for class 'bcaloocv' print(x, ...) ## S3 method for class 'bcaloocv' plot(x, xax = 1, yax = 2, ...)
## S3 method for class 'between' loocv(x, nax = 0, progress = FALSE, parallel = FALSE, ...) ## S3 method for class 'bcaloocv' print(x, ...) ## S3 method for class 'bcaloocv' plot(x, xax = 1, yax = 2, ...)
x |
dudi of the |
nax |
list of axes for mean overlap index computation (0 = all axes) |
progress |
logical, TRUE = display a progress bar during computations |
parallel |
logical, TRUE = process cross-validation in parallel computing |
xax , yax
|
the numbers of the x-axis and the y-axis |
... |
further arguments passed to or from other methods |
This function returns a list containing the cross-validated coordinates of the rows (the rows of the original analysis, not the rows of the bca
). The dudi
on which the bca
was computed is redone after removing each row of the data table, one at a time. A bca
is done on this new dudi
and the coordinates of the missing row are computed by projection as supplementary element in the corresponding bca
. This is most useful in the case p >> n (many variables and few samples), where bca
graphs can show spurious groups (see Refs.)
For parallel computing (parallel argument = TRUE), the new dudi
, bca
and cross-validation computations are processed in parallel on all the available nodes of the computer processor(s).
A list with:
- XValCoord
:
the cross-validated row coordinates
- PRESS
:
the Predicted Residual Error Sum for each row
- PRESSTot
:
the sum of PRESS
for each bca
axis
- Oij_bga
:
the mean overlap index for BGA
- Oij_XVal
:
the mean overlap index for cross-validation
- DeltaOij
:
the spuriousness index
Jean Thioulouse
Thioulouse J, Renaud S, Dufour AB, Dray S. Overcoming the Spurious Groups Problem in Between-Group PCA. Evolutionary Biology (2021). (Accepted).
Cardini A, Polly D. Cross-validated Between Group PCA Scatterplots: A Solution to Spurious Group Separation ? Evolutionary Biology (2020) 47:85–95. doi:10.1007/s11692-020-09494-x
Cardini A, O'Higgins P, Rohlf J. Seeing Distinct Groups Where There are None: Spurious Patterns from Between-Group PCA. Evolutionary Biology (2019) 46:303-316. doi:10.1007/s11692-019-09487-5
Bookstein F. Pathologies of Between-Groups Principal Components Analysis in Geometric Morphometrics. Evolutionary Biology (2019) 46:271-302. doi:10.1007/s11692-019-09484-8
# Data = meaudret data(meaudret) pca1 <- dudi.pca(meaudret$env, scannf = FALSE, nf = 3) bca1 <- bca(pca1, meaudret$design$site, scannf = FALSE, nf = 3) pst1 <- paste0("Meaudret BGA randtest: p=", randtest(bca1)$pvalue, " ratio=", round(bca1$ratio, 2)) xbca1 <- loocv(bca1, progress = TRUE) if(adegraphicsLoaded()){ sc1 <- s.class(bca1$ls, meaudret$design$site, col = TRUE, psub.text = pst1, ellipseSize=0, chullSize=1, plot = FALSE) sc2 <- s.class(xbca1$XValCoord, meaudret$design$site, col = TRUE, psub.text = "Meaudret cross-validation", ellipseSize=0, chullSize=1, plot = FALSE) ADEgS(list(sc1, sc2)) } else { par(mfrow=c(2,2)) s.chull(dfxy = bca1$ls, fac = meaudret$design$site, cpoint = 1, col = hcl.colors(5, "Dark 2"), sub = pst1) s.class(bca1$ls, meaudret$design$site, col = hcl.colors(5, "Dark 2"), cellipse = 0, add.plot = TRUE) s.chull(dfxy = xbca1$XValCoord, fac = meaudret$design$site, cpoint = 1, col = hcl.colors(5, "Dark 2"), sub = "Meaudret cross-validation") s.class(xbca1$XValCoord, meaudret$design$site, col = hcl.colors(5, "Dark 2"), cellipse = 0, add.plot = TRUE) } ## Not run: # Data = rnorm() set.seed(9) fac1 <- as.factor(rep(1:3, each = 10)) tab <- as.data.frame(matrix(rnorm(10800), nrow = 30)) pca2 <- dudi.pca(tab, scannf = FALSE) bca2 <- bca(pca2, fac1, scannf = FALSE) pst2 <- paste0("rnorm spurious groups: p=", randtest(bca2)$pvalue, " ratio=", round(bca2$ratio, 2)) xbca2 <- loocv(bca2, progress = TRUE) if(adegraphicsLoaded()){ sc3 <- s.class(bca2$ls, fac1, col = TRUE, psub.text = pst2, ellipseSize=0, chullSize=1, xlim = c(-8, 8), ylim = c(-8, 8), plot = FALSE) sc4 <- s.class(xbca2$XValCoord, fac1, col = TRUE, psub.text = "rnorm cross-validation", ellipseSize=0, chullSize=1, xlim = c(-8, 8), ylim = c(-8, 8), plot = FALSE) ADEgS(list(sc3, sc4)) } else { par(mfrow=c(2,2)) s.chull(bca2$ls, fac1, optchull = 1, cpoint = 1, xlim = c(-8, 8), ylim = c(-8, 8), col = hcl.colors(3, "Dark 2"), sub = pst2) s.class(bca2$ls, fac1, xlim = c(-8, 8), ylim = c(-8, 8), col = hcl.colors(3, "Dark 2"), cellipse = 0, add.plot = TRUE) s.chull(xbca2$XValCoord, fac1, optchull = 1, cpoint = 1, xlim = c(-8, 8), ylim = c(-8, 8), col = hcl.colors(3, "Dark 2"), sub = "rnorm cross-validation") s.class(xbca2$XValCoord, fac1, xlim = c(-8, 8), ylim = c(-8, 8), col = hcl.colors(3, "Dark 2"), cellipse = 0, add.plot = TRUE) } ## End(Not run)
# Data = meaudret data(meaudret) pca1 <- dudi.pca(meaudret$env, scannf = FALSE, nf = 3) bca1 <- bca(pca1, meaudret$design$site, scannf = FALSE, nf = 3) pst1 <- paste0("Meaudret BGA randtest: p=", randtest(bca1)$pvalue, " ratio=", round(bca1$ratio, 2)) xbca1 <- loocv(bca1, progress = TRUE) if(adegraphicsLoaded()){ sc1 <- s.class(bca1$ls, meaudret$design$site, col = TRUE, psub.text = pst1, ellipseSize=0, chullSize=1, plot = FALSE) sc2 <- s.class(xbca1$XValCoord, meaudret$design$site, col = TRUE, psub.text = "Meaudret cross-validation", ellipseSize=0, chullSize=1, plot = FALSE) ADEgS(list(sc1, sc2)) } else { par(mfrow=c(2,2)) s.chull(dfxy = bca1$ls, fac = meaudret$design$site, cpoint = 1, col = hcl.colors(5, "Dark 2"), sub = pst1) s.class(bca1$ls, meaudret$design$site, col = hcl.colors(5, "Dark 2"), cellipse = 0, add.plot = TRUE) s.chull(dfxy = xbca1$XValCoord, fac = meaudret$design$site, cpoint = 1, col = hcl.colors(5, "Dark 2"), sub = "Meaudret cross-validation") s.class(xbca1$XValCoord, meaudret$design$site, col = hcl.colors(5, "Dark 2"), cellipse = 0, add.plot = TRUE) } ## Not run: # Data = rnorm() set.seed(9) fac1 <- as.factor(rep(1:3, each = 10)) tab <- as.data.frame(matrix(rnorm(10800), nrow = 30)) pca2 <- dudi.pca(tab, scannf = FALSE) bca2 <- bca(pca2, fac1, scannf = FALSE) pst2 <- paste0("rnorm spurious groups: p=", randtest(bca2)$pvalue, " ratio=", round(bca2$ratio, 2)) xbca2 <- loocv(bca2, progress = TRUE) if(adegraphicsLoaded()){ sc3 <- s.class(bca2$ls, fac1, col = TRUE, psub.text = pst2, ellipseSize=0, chullSize=1, xlim = c(-8, 8), ylim = c(-8, 8), plot = FALSE) sc4 <- s.class(xbca2$XValCoord, fac1, col = TRUE, psub.text = "rnorm cross-validation", ellipseSize=0, chullSize=1, xlim = c(-8, 8), ylim = c(-8, 8), plot = FALSE) ADEgS(list(sc3, sc4)) } else { par(mfrow=c(2,2)) s.chull(bca2$ls, fac1, optchull = 1, cpoint = 1, xlim = c(-8, 8), ylim = c(-8, 8), col = hcl.colors(3, "Dark 2"), sub = pst2) s.class(bca2$ls, fac1, xlim = c(-8, 8), ylim = c(-8, 8), col = hcl.colors(3, "Dark 2"), cellipse = 0, add.plot = TRUE) s.chull(xbca2$XValCoord, fac1, optchull = 1, cpoint = 1, xlim = c(-8, 8), ylim = c(-8, 8), col = hcl.colors(3, "Dark 2"), sub = "rnorm cross-validation") s.class(xbca2$XValCoord, fac1, xlim = c(-8, 8), ylim = c(-8, 8), col = hcl.colors(3, "Dark 2"), cellipse = 0, add.plot = TRUE) } ## End(Not run)
discrimin
analysis
Leave-one-out cross-validation to test the existence of groups in a discrimin
analysis.
## S3 method for class 'discrimin' loocv(x, nax = 0, progress = FALSE, ...) ## S3 method for class 'discloocv' print(x, ...) ## S3 method for class 'discloocv' plot(x, xax = 1, yax = 2, ...)
## S3 method for class 'discrimin' loocv(x, nax = 0, progress = FALSE, ...) ## S3 method for class 'discloocv' print(x, ...) ## S3 method for class 'discloocv' plot(x, xax = 1, yax = 2, ...)
x |
the |
nax |
list of axes for mean overlap index computation (0 = all axes) |
progress |
logical to display a progress bar during computations (see the |
xax , yax
|
the numbers of the x-axis and the y-axis |
... |
further arguments passed to or from other methods |
This function returns a list containing the cross-validated coordinates of the rows. The analysis on which the discrimin
was computed is redone after removing each row of the data table, one at a time. A discrimin
analysis is done on this new analysis and the coordinates of the missing row are computed by projection as supplementary element in the new discrimin
analysis. This can be useful to check that the groups evidenced by the discrimin
analysis are supported.
A list with:
- XValCoord
:
the cross-validated row coordinates
- PRESS
:
the Predicted Residual Error Sum for each row
- PRESSTot
:
the sum of PRESS
for each bca
axis
- Oij_disc
:
the mean overlap index for the discriminant analysis
- Oij_XVal
:
the mean overlap index for cross-validation
- DeltaOij
:
the spuriousness index
Jean Thioulouse
## Not run: # Data = skulls data(skulls) pcaskul <- dudi.pca(skulls, scan = FALSE) facskul <- gl(5,30) diskul <- discrimin(pcaskul, facskul, scan = FALSE) xdiskul <- loocv(diskul, progress = TRUE) oijdisc <- xdiskul$Oij_disc oijxval <- xdiskul$Oij_XVal Doij <- (oijxval - oijdisc)/0.5*100 pst1 <- paste0("Skulls discrimin randtest: p=", round(randtest(diskul)$pvalue, 4), ", Oij = ", round(oijdisc,2)) pst2 <- paste0("Skulls cross-validation: Oij = ", round(oijxval,2), ", dOij = ", round(Doij), "%") if (adegraphicsLoaded()) { sc1 <- s.class(diskul$li, facskul, col = TRUE, psub.text = pst1, ellipseSize=0, chullSize=1, plot = FALSE) sc2 <- s.class(xdiskul$XValCoord, facskul, col = TRUE, psub.text = pst2, ellipseSize=0, chullSize=1, plot = FALSE) ADEgS(list(sc1, sc2), layout=c(2,2)) } else { par(mfrow=c(2,2)) s.class(diskul$li, facskul, sub = pst1) s.class(xdiskul$XValCoord, facskul, sub = pst2) } data(chazeb) pcacz <- dudi.pca(chazeb$tab, scan = FALSE) discz <- discrimin(pcacz, chazeb$cla, scan = FALSE) xdiscz <- loocv(discz, progress = TRUE) oijdiscz <- xdiscz$Oij_disc oijxvalz <- xdiscz$Oij_XVal Doijz <- (oijxvalz - oijdiscz)/0.5*100 pst1 <- paste0("Chazeb discrimin randtest: p=", round(randtest(discz)$pvalue, 4), ", Oij = ", round(oijdiscz,2)) pst2 <- paste0("Chazeb cross-validation: Oij = ", round(oijxvalz,2), ", dOij = ", round(Doijz), "%") if (adegraphicsLoaded()) { tabi <- cbind(discz$li, pcacz$tab) gr1 <- s.class(tabi, xax=1, yax=2:7, chazeb$cla, col = TRUE, plot = FALSE) for (i in 1:6) gr1[[i]] <- update(gr1[[i]], psub.text = names(tabi)[i+1], plot = FALSE) pos1 <- gr1@positions pos1[,1] <- c(0, .3333, .6667, 0, .3333, .6667) pos1[,2] <- c(.6667, .6667, .6667, .3333, .3333, .3333) pos1[,3] <- c(.3333, .6667, 1, .3333, .6667, 1) pos1[,4] <- c(1, 1, 1, .6667, .6667, .6667) gr1@positions <- pos1 sc1 <- s1d.gauss(discz$li, chazeb$cla, col = TRUE, psub.text = pst1, plot = FALSE) sc2 <- s1d.gauss(xdiscz$XValCoord, chazeb$cla, col = TRUE, psub.text = pst2, plot = FALSE) ADEgS(list(gr1[[1]], gr1[[2]], gr1[[3]], gr1[[4]], gr1[[5]], gr1[[6]], sc1, sc2)) } else { dev.new() sco.gauss(discz$li[,1], as.data.frame(chazeb$cla), sub = pst1) dev.new() sco.gauss(xdiscz$XValCoord[,1], as.data.frame(chazeb$cla), sub = pst2) } ## End(Not run)
## Not run: # Data = skulls data(skulls) pcaskul <- dudi.pca(skulls, scan = FALSE) facskul <- gl(5,30) diskul <- discrimin(pcaskul, facskul, scan = FALSE) xdiskul <- loocv(diskul, progress = TRUE) oijdisc <- xdiskul$Oij_disc oijxval <- xdiskul$Oij_XVal Doij <- (oijxval - oijdisc)/0.5*100 pst1 <- paste0("Skulls discrimin randtest: p=", round(randtest(diskul)$pvalue, 4), ", Oij = ", round(oijdisc,2)) pst2 <- paste0("Skulls cross-validation: Oij = ", round(oijxval,2), ", dOij = ", round(Doij), "%") if (adegraphicsLoaded()) { sc1 <- s.class(diskul$li, facskul, col = TRUE, psub.text = pst1, ellipseSize=0, chullSize=1, plot = FALSE) sc2 <- s.class(xdiskul$XValCoord, facskul, col = TRUE, psub.text = pst2, ellipseSize=0, chullSize=1, plot = FALSE) ADEgS(list(sc1, sc2), layout=c(2,2)) } else { par(mfrow=c(2,2)) s.class(diskul$li, facskul, sub = pst1) s.class(xdiskul$XValCoord, facskul, sub = pst2) } data(chazeb) pcacz <- dudi.pca(chazeb$tab, scan = FALSE) discz <- discrimin(pcacz, chazeb$cla, scan = FALSE) xdiscz <- loocv(discz, progress = TRUE) oijdiscz <- xdiscz$Oij_disc oijxvalz <- xdiscz$Oij_XVal Doijz <- (oijxvalz - oijdiscz)/0.5*100 pst1 <- paste0("Chazeb discrimin randtest: p=", round(randtest(discz)$pvalue, 4), ", Oij = ", round(oijdiscz,2)) pst2 <- paste0("Chazeb cross-validation: Oij = ", round(oijxvalz,2), ", dOij = ", round(Doijz), "%") if (adegraphicsLoaded()) { tabi <- cbind(discz$li, pcacz$tab) gr1 <- s.class(tabi, xax=1, yax=2:7, chazeb$cla, col = TRUE, plot = FALSE) for (i in 1:6) gr1[[i]] <- update(gr1[[i]], psub.text = names(tabi)[i+1], plot = FALSE) pos1 <- gr1@positions pos1[,1] <- c(0, .3333, .6667, 0, .3333, .6667) pos1[,2] <- c(.6667, .6667, .6667, .3333, .3333, .3333) pos1[,3] <- c(.3333, .6667, 1, .3333, .6667, 1) pos1[,4] <- c(1, 1, 1, .6667, .6667, .6667) gr1@positions <- pos1 sc1 <- s1d.gauss(discz$li, chazeb$cla, col = TRUE, psub.text = pst1, plot = FALSE) sc2 <- s1d.gauss(xdiscz$XValCoord, chazeb$cla, col = TRUE, psub.text = pst2, plot = FALSE) ADEgS(list(gr1[[1]], gr1[[2]], gr1[[3]], gr1[[4]], gr1[[5]], gr1[[6]], sc1, sc2)) } else { dev.new() sco.gauss(discz$li[,1], as.data.frame(chazeb$cla), sub = pst1) dev.new() sco.gauss(xdiscz$XValCoord[,1], as.data.frame(chazeb$cla), sub = pst2) } ## End(Not run)
dudi
Leave-one-out cross-validation to check the dispersion of row coordinates in a dudi
.
## S3 method for class 'dudi' loocv(x, progress = FALSE, ...)
## S3 method for class 'dudi' loocv(x, progress = FALSE, ...)
x |
the dudi of the |
progress |
logical to display a progress bar during computations (see the |
... |
further arguments passed to or from other methods |
This function does a cross-validation of the row coordinates of a dudi. Each row is removed from the table one at a time, and its coordinates are computed by projection of this row in the analysis of the table with the removed row. This can be used to check the sensitivity of an analysis to outliers. The cross-validated and original coordinates can be compared with the s.match
function (see example).
A list with:
- XValCoord
:
the cross-validated row coordinates
- PRESS
:
the Predicted Residual Error Sum for each row
- PRESSTot
:
the sum of PRESS
for each bca
axis
Jean Thioulouse
loocv.between, loocv.discrimin, suprow, s.match
data(meaudret) envpca <- dudi.pca(meaudret$env, scannf = FALSE, nf = 3) xvpca <- loocv(envpca) s.match(envpca$li, xvpca$XValCoord)
data(meaudret) envpca <- dudi.pca(meaudret$env, scannf = FALSE, nf = 3) xvpca <- loocv(envpca) s.match(envpca$li, xvpca$XValCoord)
This data set gives the landmarks of a macaca at the ages of 0.9 and 5.77 years.
data(macaca)
data(macaca)
macaca
is a list of 2 components.
is a data frame with 72 points and 2 coordinates.
is a data frame with 72 points and 2 coordinates.
Olshan, A.F., Siegel, A.F. and Swindler, D.R. (1982) Robust and least-squares orthogonal mapping: Methods for the study of cephalofacial form and growth. American Journal of Physical Anthropology, 59, 131–137.
data(macaca) pro1 <- procuste(macaca$xy1, macaca$xy2, scal = FALSE) pro2 <- procuste(macaca$xy1, macaca$xy2) if(adegraphicsLoaded()) { g1 <- s.match(macaca$xy1, macaca$xy2, plab.cex = 0, plot = FALSE) g2 <- s.match(pro1$tabX, pro1$rotY, plab.cex = 0.7, plot = FALSE) g3 <- s.match(pro1$tabY, pro1$rotX, plab.cex = 0.7, plot = FALSE) g4 <- s.match(pro2$tabY, pro2$rotX, plab.cex = 0.7, plot = FALSE) G <- ADEgS(c(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2,2)) s.match(macaca$xy1, macaca$xy2, clab = 0) s.match(pro1$tabX, pro1$rotY, clab = 0.7) s.match(pro1$tabY, pro1$rotX, clab = 0.7) s.match(pro2$tabY, pro2$rotX, clab = 0.7) par(mfrow = c(1,1)) }
data(macaca) pro1 <- procuste(macaca$xy1, macaca$xy2, scal = FALSE) pro2 <- procuste(macaca$xy1, macaca$xy2) if(adegraphicsLoaded()) { g1 <- s.match(macaca$xy1, macaca$xy2, plab.cex = 0, plot = FALSE) g2 <- s.match(pro1$tabX, pro1$rotY, plab.cex = 0.7, plot = FALSE) g3 <- s.match(pro1$tabY, pro1$rotX, plab.cex = 0.7, plot = FALSE) g4 <- s.match(pro2$tabY, pro2$rotX, plab.cex = 0.7, plot = FALSE) G <- ADEgS(c(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2,2)) s.match(macaca$xy1, macaca$xy2, clab = 0) s.match(pro1$tabX, pro1$rotY, clab = 0.7) s.match(pro1$tabY, pro1$rotX, clab = 0.7) s.match(pro2$tabY, pro2$rotX, clab = 0.7) par(mfrow = c(1,1)) }
The macon
data frame has 8 rows-wines and 25 columns-tasters.
Each column is a classification of 8 wines (Beaujolais, France).
data(macon)
data(macon)
Foire Nationale des Vins de France, Mâcon, 1985
data(macon) s.corcircle(dudi.pca(macon, scan = FALSE)$co)
data(macon) s.corcircle(dudi.pca(macon, scan = FALSE)$co)
A total of 38 sites were surveyed along 800 km of the Loire River yielding 40 species of Trichoptera and Coleoptera sampled from riffle habitats. The river was divided into three regions according to geology: granitic highlands (Region#1), limestone lowlands (Region#2) and granitic lowlands (Region#3). This data set has been collected for analyzing changes in macroinvertebrate assemblages along the course of a large river. Four criterias are given here: variation in 1/ species composition and relative abundance, 2/ taxonomic composition, 3/ Body Sizes, 4/ Feeding habits.
data(macroloire)
data(macroloire)
macroloire
is a list of 5 components.
is a data frame containing the abundance of each species in each station.
is a data frame describes two traits : the maximal sizes and feeding habits for each species. Each trait is divided into categories. The maximal size achieved by the species is divided into four length categories: <= 5mm ; >5-10mm ; >10-20mm ; >20-40mm. Feeding habits comprise seven categories: engulfers, shredders, scrapers, deposit-feeders, active filter-feeders, passive filter-feeders and piercers, in this order. The affinity of each species to each trait category is quantified using a fuzzy coding approach. A score is assigned to each species for describing its affinity for a given trait category from "0" which indicates no affinity to "3" which indicates high affinity. These affinities are further transformed into percentage per trait per species.
is a data frame with species and 3 factors: Genus, Family and Order. It is a data frame of class "taxo": the variables are factors giving nested classifications.
is a data frame giving for each station, its name (variable "SamplingSite"), its distance from the source (km, variable "Distance"), its altitude (m, variable "Altitude"), its position regarding the dams [1: before the first dam; 2: after the first dam; 3: after the second dam] (variable "Dam"), its position in one of the three regions defined according to geology: granitic highlands, limestone lowlands and granitic lowlands (variable "Morphoregion"), presence of confluence (variable "Confluence")
is a data frame containing the latin names of the species.
Ivol, J.M., Guinand, B., Richoux, P. and Tachet, H. (1997) Longitudinal changes in
Trichoptera and Coleoptera assemblages and environmental conditions in the Loire
River (France). Archiv for Hydrobiologie, 138, 525–557.
Pavoine S. and Doledec S. (2005) The apportionment of quadratic entropy: a useful alternative for partitioning diversity in ecological data. Environmental and Ecological Statistics, 12, 125–138.
data(macroloire) apqe.Equi <- apqe(macroloire$fau, , macroloire$morphoregions) apqe.Equi #test.Equi <- randtest.apqe(apqe.Equi, method = "aggregated", 99) #plot(test.Equi) ## Not run: m.phy <- taxo2phylog(macroloire$taxo) apqe.Tax <- apqe(macroloire$fau, m.phy$Wdist, macroloire$morphoregions) apqe.Tax #test.Tax <- randtest.apqe(apqe.Tax, method = "aggregated", 99) #plot(test.Tax) dSize <- sqrt(dist.prop(macroloire$traits[ ,1:4], method = 2)) apqe.Size <- apqe(macroloire$fau, dSize, macroloire$morphoregions) apqe.Size #test.Size <- randtest.apqe(apqe.Size, method = "aggregated", 99) #plot(test.Size) dFeed <- sqrt(dist.prop(macroloire$traits[ ,-(1:4)], method = 2)) apqe.Feed <- apqe(macroloire$fau, dFeed, macroloire$morphoregions) apqe.Feed #test.Feed <- randtest.apqe(apqe.Feed, method = "aggregated", 99) #plot(test.Size) ## End(Not run)
data(macroloire) apqe.Equi <- apqe(macroloire$fau, , macroloire$morphoregions) apqe.Equi #test.Equi <- randtest.apqe(apqe.Equi, method = "aggregated", 99) #plot(test.Equi) ## Not run: m.phy <- taxo2phylog(macroloire$taxo) apqe.Tax <- apqe(macroloire$fau, m.phy$Wdist, macroloire$morphoregions) apqe.Tax #test.Tax <- randtest.apqe(apqe.Tax, method = "aggregated", 99) #plot(test.Tax) dSize <- sqrt(dist.prop(macroloire$traits[ ,1:4], method = 2)) apqe.Size <- apqe(macroloire$fau, dSize, macroloire$morphoregions) apqe.Size #test.Size <- randtest.apqe(apqe.Size, method = "aggregated", 99) #plot(test.Size) dFeed <- sqrt(dist.prop(macroloire$traits[ ,-(1:4)], method = 2)) apqe.Feed <- apqe(macroloire$fau, dFeed, macroloire$morphoregions) apqe.Feed #test.Feed <- randtest.apqe(apqe.Feed, method = "aggregated", 99) #plot(test.Size) ## End(Not run)
This data set gives environmental and spatial informations about species and sites.
data(mafragh)
data(mafragh)
mafragh
is a list with the following components:
the coordinates of 97 sites
a data frame with 97 sites and 56 species
a data frame with 97 sites and 11 environmental variables
a factor classifying the 97 sites in 7 classes
a data frame of class area
a character providing the phylogeny as a newick object
a list of data frame. Each data frame provides the value of biological traits for plant species
the neighbourhood graph of the 97 Mafragh sites (an object of class nb
)
the map of the 97 Mafragh sites (an object of the class SpatialPolygons
of sp
)
a data frame with 56 rows (species) and 2 columns (names)
the contour of the Magragh map (an object of the class SpatialPolygons
of sp
)
de Bélair, Gérard and Bencheikh-Lehocine, Mahmoud (1987) Composition et déterminisme de la végétation d'une plaine côtière marécageuse : La Mafragh (Annaba, Algérie). Bulletin d'Ecologie, 18(4), 393–407.
Pavoine, S., Vela, E., Gachet, S., de Bélair, G. and Bonsall, M. B. (2011) Linking patterns in phylogeny, traits, abiotic variables and space: a novel approach to linking environmental filtering and plant community assembly. Journal of Ecology, 99, 165–175. doi:10.1111/j.1365-2745.2010.01743.x
See a data description at http://pbil.univ-lyon1.fr/R/pdf/pps053.pdf (in French).
data(mafragh) coa1 <- dudi.coa(mafragh$flo, scan = FALSE) pca1 <- dudi.pca(mafragh$xy, scan = FALSE) if(adegraphicsLoaded()) { g1 <- s.label(mafragh$xy, nb = mafragh$nb, psub.text = "Samples & Neighbourhood graph", plot = FALSE) g2 <- s.value(mafragh$xy, coa1$li[, 1], psub.text = "Axis 1 - COA", plot = FALSE) g3 <- s.value(mafragh$xy, pca1$li[, 1], psub.text = "Axis 1 - PCA", plot = FALSE) g4 <- s.class(pca1$li, mafragh$partition, psub.text = "Plane 1-2 - PCA", plot = FALSE) g5 <- s.class(coa1$li, mafragh$partition, psub.text = "Plane 1-2 - COA", plot = FALSE) g6 <- s.class(mafragh$xy, mafragh$partition, chullSize = 1, ellipseSize = 0, starSize = 0, ppoints.cex = 0, plot = FALSE) G <- ADEgS(c(g1, g2, g3, g4, g5, g6), layout = c(3, 2)) } else { par(mfrow = c(3, 2)) s.value(mafragh$xy, coa1$li[, 1], sub = "Axis 1 - COA") s.value(mafragh$xy, pca1$li[, 1], sub = "Axis 1 - PCA") s.class(pca1$li, mafragh$partition, sub = "Plane 1-2 - PCA") s.class(coa1$li, mafragh$partition, sub = "Plane 1-2 - COA") s.chull(mafragh$xy, mafragh$partition, optchull = 1) par(mfrow = c(1, 1)) } ## Not run: link1 <- area2link(mafragh$area) neig1 <- neig(mat01 = 1*(link1 > 0)) nb1 <- neig2nb(neig1) if(adegraphicsLoaded()) { if(requireNamespace("sp", quietly = TRUE)) { g7 <- s.label(mafragh$xy, Sp = mafragh$Spatial, pSp.col = "white", plot = FALSE) g8 <- s.label(mafragh$xy, Sp = mafragh$Spatial, pSp.col = "white", nb = nb1, plab.cex = 0, pnb.node.cex = 0, ppoints.cex = 0, plot = FALSE) G <- ADEgS(c(g7, g8), layout = c(2, 1)) } } else { par(mfrow = c(2, 1)) area.plot(mafragh$area, center = mafragh$xy, clab = 0.75) area.plot(mafragh$area, center = mafragh$xy, graph = neig1) par(mfrow = c(1, 1)) } if(requireNamespace("spdep", quietly = TRUE) & requireNamespace("adespatial", quietly = TRUE)) { lw1 <- apply(link1, 1, function(x) x[x > 0]) listw1 <- spdep::nb2listw(nb1, lw1) coa1 <- dudi.coa(mafragh$flo, scan = FALSE, nf = 4) ms1 <- adespatial::multispati(coa1, listw1, scan = FALSE, nfp = 2, nfn = 0) summary(ms1) if(adegraphicsLoaded()) { if(requireNamespace("lattice", quietly = TRUE)) { g9 <- s1d.barchart(coa1$eig, p1d.hori = FALSE, plot = FALSE) g10 <- s1d.barchart(ms1$eig, p1d.hori = FALSE, plot = FALSE) g11 <- s.corcircle(ms1$as, plot = FALSE) g12 <- lattice::xyplot(ms1$li[, 1] ~ coa1$li[, 1]) G <- ADEgS(list(g9, g10, g11, g12), layout = c(2, 2)) } } else { par(mfrow = c(2, 2)) barplot(coa1$eig) barplot(ms1$eig) s.corcircle(ms1$as) plot(coa1$li[, 1], ms1$li[, 1]) par(mfrow = c(1, 1)) } } ## End(Not run)
data(mafragh) coa1 <- dudi.coa(mafragh$flo, scan = FALSE) pca1 <- dudi.pca(mafragh$xy, scan = FALSE) if(adegraphicsLoaded()) { g1 <- s.label(mafragh$xy, nb = mafragh$nb, psub.text = "Samples & Neighbourhood graph", plot = FALSE) g2 <- s.value(mafragh$xy, coa1$li[, 1], psub.text = "Axis 1 - COA", plot = FALSE) g3 <- s.value(mafragh$xy, pca1$li[, 1], psub.text = "Axis 1 - PCA", plot = FALSE) g4 <- s.class(pca1$li, mafragh$partition, psub.text = "Plane 1-2 - PCA", plot = FALSE) g5 <- s.class(coa1$li, mafragh$partition, psub.text = "Plane 1-2 - COA", plot = FALSE) g6 <- s.class(mafragh$xy, mafragh$partition, chullSize = 1, ellipseSize = 0, starSize = 0, ppoints.cex = 0, plot = FALSE) G <- ADEgS(c(g1, g2, g3, g4, g5, g6), layout = c(3, 2)) } else { par(mfrow = c(3, 2)) s.value(mafragh$xy, coa1$li[, 1], sub = "Axis 1 - COA") s.value(mafragh$xy, pca1$li[, 1], sub = "Axis 1 - PCA") s.class(pca1$li, mafragh$partition, sub = "Plane 1-2 - PCA") s.class(coa1$li, mafragh$partition, sub = "Plane 1-2 - COA") s.chull(mafragh$xy, mafragh$partition, optchull = 1) par(mfrow = c(1, 1)) } ## Not run: link1 <- area2link(mafragh$area) neig1 <- neig(mat01 = 1*(link1 > 0)) nb1 <- neig2nb(neig1) if(adegraphicsLoaded()) { if(requireNamespace("sp", quietly = TRUE)) { g7 <- s.label(mafragh$xy, Sp = mafragh$Spatial, pSp.col = "white", plot = FALSE) g8 <- s.label(mafragh$xy, Sp = mafragh$Spatial, pSp.col = "white", nb = nb1, plab.cex = 0, pnb.node.cex = 0, ppoints.cex = 0, plot = FALSE) G <- ADEgS(c(g7, g8), layout = c(2, 1)) } } else { par(mfrow = c(2, 1)) area.plot(mafragh$area, center = mafragh$xy, clab = 0.75) area.plot(mafragh$area, center = mafragh$xy, graph = neig1) par(mfrow = c(1, 1)) } if(requireNamespace("spdep", quietly = TRUE) & requireNamespace("adespatial", quietly = TRUE)) { lw1 <- apply(link1, 1, function(x) x[x > 0]) listw1 <- spdep::nb2listw(nb1, lw1) coa1 <- dudi.coa(mafragh$flo, scan = FALSE, nf = 4) ms1 <- adespatial::multispati(coa1, listw1, scan = FALSE, nfp = 2, nfn = 0) summary(ms1) if(adegraphicsLoaded()) { if(requireNamespace("lattice", quietly = TRUE)) { g9 <- s1d.barchart(coa1$eig, p1d.hori = FALSE, plot = FALSE) g10 <- s1d.barchart(ms1$eig, p1d.hori = FALSE, plot = FALSE) g11 <- s.corcircle(ms1$as, plot = FALSE) g12 <- lattice::xyplot(ms1$li[, 1] ~ coa1$li[, 1]) G <- ADEgS(list(g9, g10, g11, g12), layout = c(2, 2)) } } else { par(mfrow = c(2, 2)) barplot(coa1$eig) barplot(ms1$eig) s.corcircle(ms1$as) plot(coa1$li[, 1], ms1$li[, 1]) par(mfrow = c(1, 1)) } } ## End(Not run)
Performs a Mantel test between two distance matrices.
mantel.randtest(m1, m2, nrepet = 999, ...)
mantel.randtest(m1, m2, nrepet = 999, ...)
m1 |
an object of class |
m2 |
an object of class |
nrepet |
the number of permutations |
... |
further arguments passed to or from other methods |
an object of class randtest
(randomization tests)
Jean Thioulouse [email protected]
Mantel, N. (1967) The detection of disease clustering and a generalized regression approach. Cancer Research, 27, 209–220.
data(yanomama) gen <- quasieuclid(as.dist(yanomama$gen)) geo <- quasieuclid(as.dist(yanomama$geo)) plot(r1 <- mantel.randtest(geo,gen), main = "Mantel's test") r1
data(yanomama) gen <- quasieuclid(as.dist(yanomama$gen)) geo <- quasieuclid(as.dist(yanomama$geo)) plot(r1 <- mantel.randtest(geo,gen), main = "Mantel's test") r1
Performs a Mantel test between two distance matrices.
mantel.rtest(m1, m2, nrepet = 99, ...)
mantel.rtest(m1, m2, nrepet = 99, ...)
m1 |
an object of class |
m2 |
an object of class |
nrepet |
the number of permutations |
... |
further arguments passed to or from other methods |
an object of class rtest
(randomization tests)
Daniel Chessel
Stéphane Dray [email protected]
Mantel, N. (1967) The detection of disease clustering and a generalized regression approach. Cancer Research, 27, 209–220.
data(yanomama) gen <- quasieuclid(as.dist(yanomama$gen)) geo <- quasieuclid(as.dist(yanomama$geo)) plot(r1 <- mantel.rtest(geo,gen), main = "Mantel's test") r1
data(yanomama) gen <- quasieuclid(as.dist(yanomama$gen)) geo <- quasieuclid(as.dist(yanomama$geo)) plot(r1 <- mantel.rtest(geo,gen), main = "Mantel's test") r1
This data set describes the phylogeny of 17 flowers as reported by Ackerly and Donoghue (1998). It also gives 31 traits corresponding to these 17 species.
data(maples)
data(maples)
tithonia
is a list containing the 2 following objects :
is a character string giving the phylogenetic tree in Newick format.
is a data frame with 17 species and 31 traits
Ackerly, D. D. and Donoghue, M.J. (1998) Leaf size, sapling allometry, and Corner's rules: phylogeny and correlated evolution in Maples (Acer). American Naturalist, 152, 767–791.
data(maples) phy <- newick2phylog(maples$tre) dom <- maples$tab$Dom bif <- maples$tab$Bif if (requireNamespace("adephylo", quietly = TRUE) & requireNamespace("ape", quietly = TRUE)) { phylo <- ape::read.tree(text = maples$tre) adephylo::orthogram(dom, tre = phylo) adephylo::orthogram(bif, tre = phylo) par(mfrow = c(1, 2)) dotchart.phylog(phy, dom) dotchart.phylog(phy, bif, clabel.nodes = 0.7) par(mfrow = c(1, 1)) plot(bif, dom, pch = 20) abline(lm(dom~bif)) summary(lm(dom~bif)) cor.test(bif, dom) pic.bif <- ape::pic(bif, phylo) pic.dom <- ape::pic(dom, phylo) cor.test(pic.bif, pic.dom) }
data(maples) phy <- newick2phylog(maples$tre) dom <- maples$tab$Dom bif <- maples$tab$Bif if (requireNamespace("adephylo", quietly = TRUE) & requireNamespace("ape", quietly = TRUE)) { phylo <- ape::read.tree(text = maples$tre) adephylo::orthogram(dom, tre = phylo) adephylo::orthogram(bif, tre = phylo) par(mfrow = c(1, 2)) dotchart.phylog(phy, dom) dotchart.phylog(phy, bif, clabel.nodes = 0.7) par(mfrow = c(1, 1)) plot(bif, dom, pch = 20) abline(lm(dom~bif)) summary(lm(dom~bif)) cor.test(bif, dom) pic.bif <- ape::pic(bif, phylo) pic.dom <- ape::pic(dom, phylo) cor.test(pic.bif, pic.dom) }
This array contains the socio-professionnal repartitions of 5850 couples.
data(mariages)
data(mariages)
The mariages
data frame has 9 rows and 9 columns.
The rows represent the wife's socio-professionnal category and the columns the husband's socio-professionnal category (1982).
Codes for rows and columns are identical : agri (Farmers), ouva (Farm workers), pat (Company directors (commerce and industry)), sup (Liberal profession, executives and higher intellectual professions), moy (Intermediate professions), emp (Other white-collar workers), ouv (Manual workers), serv (Domestic staff), aut (other workers).
Vallet, L.A. (1986) Activité professionnelle de la femme mariée et détermination de la position sociale de la famille. Un test empirique : la France entre 1962 et 1982. Revue Française de Sociologie, 27, 656–696.
data(mariages) w <- dudi.coa(mariages, scan = FALSE, nf = 3) if(adegraphicsLoaded()) { g1 <- scatter(w, met = 1, posi = "bottomleft", plot = FALSE) g2 <- scatter(w, met = 2, posi = "bottomleft", plot = FALSE) g3 <- scatter(w, met = 3, posi = "bottomleft", plot = FALSE) ## g4 <- score(w, 3) G <- ADEgS(list(g1, g2, g3), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) scatter(w, met = 1, posi = "bottom") scatter(w, met = 2, posi = "bottom") scatter(w, met = 3, posi = "bottom") score(w, 3) par(mfrow = c(1, 1)) }
data(mariages) w <- dudi.coa(mariages, scan = FALSE, nf = 3) if(adegraphicsLoaded()) { g1 <- scatter(w, met = 1, posi = "bottomleft", plot = FALSE) g2 <- scatter(w, met = 2, posi = "bottomleft", plot = FALSE) g3 <- scatter(w, met = 3, posi = "bottomleft", plot = FALSE) ## g4 <- score(w, 3) G <- ADEgS(list(g1, g2, g3), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) scatter(w, met = 1, posi = "bottom") scatter(w, met = 2, posi = "bottom") scatter(w, met = 3, posi = "bottom") score(w, 3) par(mfrow = c(1, 1)) }
Function to perform a multiblock redundancy analysis of several explanatory blocks , defined as an object of class
ktab
, to explain a dependent dataset $Y$, defined as an object of class dudi
mbpcaiv(dudiY, ktabX, scale = TRUE, option = c("uniform", "none"), scannf = TRUE, nf = 2)
mbpcaiv(dudiY, ktabX, scale = TRUE, option = c("uniform", "none"), scannf = TRUE, nf = 2)
dudiY |
an object of class |
ktabX |
an object of class |
scale |
logical value indicating whether the explanatory variables should be standardized |
option |
an option for the block weighting. If |
scannf |
logical value indicating whether the eigenvalues bar plot should be displayed |
nf |
integer indicating the number of kept dimensions |
A list containing the following components is returned:
call |
the matching call |
tabY |
data frame of dependent variables centered, eventually scaled (if scale=TRUE) and weighted (if option="uniform") |
tabX |
data frame of explanatory variables centered, eventually scaled (if scale=TRUE) and weighted (if option="uniform") |
TL , TC
|
data frame useful to manage graphical outputs |
nf |
numeric value indicating the number of kept dimensions |
lw |
numeric vector of row weights |
X.cw |
numeric vector of column weighs for the explanalatory dataset |
blo |
vector of the numbers of variables in each explanatory dataset |
rank |
maximum rank of the analysis |
eig |
numeric vector containing the eigenvalues |
lX |
matrix of the global components associated with the whole explanatory dataset (scores of the individuals) |
lY |
matrix of the components associated with the dependent dataset |
Yc1 |
matrix of the variable loadings associated with the dependent dataset |
Tli |
matrix containing the partial components associated with each explanatory dataset |
Tl1 |
matrix containing the normalized partial components associated with each explanatory dataset |
Tfa |
matrix containing the partial loadings associated with each explanatory dataset |
cov2 |
squared covariance between lY and Tl1 |
Yco |
matrix of the regression coefficients of the dependent dataset onto the global components |
faX |
matrix of the regression coefficients of the whole explanatory dataset onto the global components |
XYcoef |
list of matrices of the regression coefficients of the whole explanatory dataset onto the dependent dataset |
bip |
block importances for a given dimension |
bipc |
cumulated block importances for a given number of dimensions |
vip |
variable importances for a given dimension |
vipc |
cumulated variable importances for a given number of dimensions |
Stéphanie Bougeard ([email protected]) and Stéphane Dray ([email protected])
Bougeard, S., Qannari, E.M. and Rose, N. (2011) Multiblock Redundancy Analysis: interpretation tools and application in epidemiology. Journal of Chemometrics, 23, 1-9
Bougeard, S. and Dray S. (2018) Supervised Multiblock Analysis in R with the ade4 Package. Journal of Statistical Software, 86 (1), 1-17. doi:10.18637/jss.v086.i01
mbpls
, testdim.multiblock
, randboot.multiblock
data(chickenk) Mortality <- chickenk[[1]] dudiY.chick <- dudi.pca(Mortality, center = TRUE, scale = TRUE, scannf = FALSE) ktabX.chick <- ktab.list.df(chickenk[2:5]) resmbpcaiv.chick <- mbpcaiv(dudiY.chick, ktabX.chick, scale = TRUE, option = "uniform", scannf = FALSE) summary(resmbpcaiv.chick) if(adegraphicsLoaded()) plot(resmbpcaiv.chick)
data(chickenk) Mortality <- chickenk[[1]] dudiY.chick <- dudi.pca(Mortality, center = TRUE, scale = TRUE, scannf = FALSE) ktabX.chick <- ktab.list.df(chickenk[2:5]) resmbpcaiv.chick <- mbpcaiv(dudiY.chick, ktabX.chick, scale = TRUE, option = "uniform", scannf = FALSE) summary(resmbpcaiv.chick) if(adegraphicsLoaded()) plot(resmbpcaiv.chick)
Function to perform a multiblock partial least squares (PLS) of several explanatory blocks defined as an object of class
ktab
, to explain a dependent dataset $Y$ defined as an object of class dudi
mbpls(dudiY, ktabX, scale = TRUE, option = c("uniform", "none"), scannf = TRUE, nf = 2)
mbpls(dudiY, ktabX, scale = TRUE, option = c("uniform", "none"), scannf = TRUE, nf = 2)
dudiY |
an object of class |
ktabX |
an object of class |
scale |
logical value indicating whether the explanatory variables should be standardized |
option |
an option for the block weighting. If |
scannf |
logical value indicating whether the eigenvalues bar plot should be displayed |
nf |
integer indicating the number of kept dimensions |
A list containing the following components is returned:
call |
the matching call |
tabY |
data frame of dependent variables centered, eventually scaled (if scale=TRUE) and weighted (if option="uniform") |
tabX |
data frame of explanatory variables centered, eventually scaled (if scale=TRUE) and weighted (if option="uniform") |
TL , TC
|
data frame useful to manage graphical outputs |
nf |
numeric value indicating the number of kept dimensions |
lw |
numeric vector of row weights |
X.cw |
numeric vector of column weighs for the explanalatory dataset |
blo |
vector of the numbers of variables in each explanatory dataset |
rank |
maximum rank of the analysis |
eig |
numeric vector containing the eigenvalues |
lX |
matrix of the global components associated with the whole explanatory dataset (scores of the individuals) |
lY |
matrix of the components associated with the dependent dataset |
Yc1 |
matrix of the variable loadings associated with the dependent dataset |
cov2 |
squared covariance between lY and TlX |
Tc1 |
matrix containing the partial loadings associated with each explanatory dataset (unit norm) |
TlX |
matrix containing the partial components associated with each explanatory dataset |
faX |
matrix of the regression coefficients of the whole explanatory dataset onto the global components |
XYcoef |
list of matrices of the regression coefficients of the whole explanatory dataset onto the dependent dataset |
bip |
block importances for a given dimension |
bipc |
cumulated block importances for a given number of dimensions |
vip |
variable importances for a given dimension |
vipc |
cumulated variable importances for a given number of dimensions |
Stéphanie Bougeard ([email protected]) and Stéphane Dray ([email protected])
Bougeard, S., Qannari, E.M., Lupo, C. and Hanafi, M. (2011). From multiblock partial least squares to multiblock redundancy analysis. A continuum approach. Informatica, 22(1), 11-26
Bougeard, S. and Dray S. (2018) Supervised Multiblock Analysis in R with the ade4 Package. Journal of Statistical Software, 86 (1), 1-17. doi:10.18637/jss.v086.i01
mbpls
, testdim.multiblock
,
randboot.multiblock
data(chickenk) Mortality <- chickenk[[1]] dudiY.chick <- dudi.pca(Mortality, center = TRUE, scale = TRUE, scannf = FALSE) ktabX.chick <- ktab.list.df(chickenk[2:5]) resmbpls.chick <- mbpls(dudiY.chick, ktabX.chick, scale = TRUE, option = "uniform", scannf = FALSE) summary(resmbpls.chick) if(adegraphicsLoaded()) plot(resmbpls.chick)
data(chickenk) Mortality <- chickenk[[1]] dudiY.chick <- dudi.pca(Mortality, center = TRUE, scale = TRUE, scannf = FALSE) ktabX.chick <- ktab.list.df(chickenk[2:5]) resmbpls.chick <- mbpls(dudiY.chick, ktabX.chick, scale = TRUE, option = "uniform", scannf = FALSE) summary(resmbpls.chick) if(adegraphicsLoaded()) plot(resmbpls.chick)
performs a multiple CO-inertia analysis,
using an object of class ktab
.
mcoa(X, option = c("inertia", "lambda1", "uniform", "internal"), scannf = TRUE, nf = 3, tol = 1e-07) ## S3 method for class 'mcoa' print(x, ...) ## S3 method for class 'mcoa' summary(object, ...) ## S3 method for class 'mcoa' plot(x, xax = 1, yax = 2, eig.bottom = TRUE, ...)
mcoa(X, option = c("inertia", "lambda1", "uniform", "internal"), scannf = TRUE, nf = 3, tol = 1e-07) ## S3 method for class 'mcoa' print(x, ...) ## S3 method for class 'mcoa' summary(object, ...) ## S3 method for class 'mcoa' plot(x, xax = 1, yax = 2, eig.bottom = TRUE, ...)
X |
an object of class |
option |
a string of characters for the weightings of the arrays options :
|
scannf |
a logical value indicating whether the eigenvalues bar plot should be displayed |
nf |
if scannf FALSE, an integer indicating the number of kept axes |
tol |
a tolerance threshold, an eigenvalue is considered positive if it is larger than |
x , object
|
an object of class 'mcoa' |
... |
further arguments passed to or from other methods |
xax , yax
|
the numbers of the x-axis and the y-axis |
eig.bottom |
a logical value indicating whether the eigenvalues bar plot should be added |
mcoa returns a list of class 'mcoa' containing :
pseudoeig |
a numeric vector with the all pseudo eigenvalues |
call |
the call-up order |
nf |
a numeric value indicating the number of kept axes |
SynVar |
a data frame with the synthetic scores |
axis |
a data frame with the co-inertia axes |
Tli |
a data frame with the co-inertia coordinates |
Tl1 |
a data frame with the co-inertia normed scores |
Tax |
a data frame with the inertia axes onto co-inertia axis |
Tco |
a data frame with the column coordinates onto synthetic scores |
TL |
a data frame with the factors for Tli Tl1 |
TC |
a data frame with the factors for Tco |
T4 |
a data frame with the factors for Tax |
lambda |
a data frame with the all eigenvalues (computed on the separate analyses) |
cov2 |
a numeric vector with the all pseudo eigenvalues (synthetic analysis) |
Daniel Chessel
Anne-Béatrice Dufour [email protected]
Chessel, D. and Hanafi, M. (1996) Analyses de la co-inertie de K nuages de points, Revue de Statistique Appliquée, 44, 35–60.
data(friday87) w1 <- data.frame(scale(friday87$fau, scal = FALSE)) w2 <- ktab.data.frame(w1, friday87$fau.blo, tabnames = friday87$tab.names) mcoa1 <- mcoa(w2, "lambda1", scan = FALSE) mcoa1 summary(mcoa1) plot(mcoa1)
data(friday87) w1 <- data.frame(scale(friday87$fau, scal = FALSE)) w2 <- ktab.data.frame(w1, friday87$fau.blo, tabnames = friday87$tab.names) mcoa1 <- mcoa(w2, "lambda1", scan = FALSE) mcoa1 summary(mcoa1) plot(mcoa1)
The DPCoA analysis (see dpcoa
) has been developed by Pavoine et al. (2004).
It has been used in genetics for describing inter-population nucleotide
diversity. However, this procedure can only be used with one locus. In order to measure
and describe nucleotide diversity with more than one locus, we developed three versions of
multiple DPCoA by using three ordination methods: multiple co-inertia analysis, STATIS, and
multiple factorial analysis.
The multiple DPCoA allows the impact of various loci in the
measurement and description of diversity to be quantified and described. This method is general enough to handle a large variety
of data sets. It complements existing methods such as the analysis of molecular variance or other
analyses based on linkage disequilibrium measures, and is very useful to study the impact of various
loci on the measurement of diversity.
mdpcoa(msamples, mdistances = NULL, method = c("mcoa", "statis", "mfa"), option = c("inertia", "lambda1", "uniform", "internal"), scannf = TRUE, nf = 3, full = TRUE, nfsep = NULL, tol = 1e-07) kplotX.mdpcoa(object, xax = 1, yax = 2, mfrow = NULL, which.tab = 1:length(object$nX), includepop = FALSE, clab = 0.7, cpoi = 0.7, unique.scale = FALSE, csub = 2, possub = "bottomright") prep.mdpcoa(dnaobj, pop, model, ...)
mdpcoa(msamples, mdistances = NULL, method = c("mcoa", "statis", "mfa"), option = c("inertia", "lambda1", "uniform", "internal"), scannf = TRUE, nf = 3, full = TRUE, nfsep = NULL, tol = 1e-07) kplotX.mdpcoa(object, xax = 1, yax = 2, mfrow = NULL, which.tab = 1:length(object$nX), includepop = FALSE, clab = 0.7, cpoi = 0.7, unique.scale = FALSE, csub = 2, possub = "bottomright") prep.mdpcoa(dnaobj, pop, model, ...)
msamples |
A list of data frames with the populations as columns, alleles as rows and abundances as entries. All the tables should have equal numbers of columns (populations). Each table corresponds to a locus; |
mdistances |
A list of objects of class 'dist', corresponding to the distances among alleles. The order of the loci should be the same in msamples as in mdistances; |
method |
One of the three possibilities: "mcoa", "statis", or "mfa". If a vector is given, only its first value is considered; |
option |
One of the four possibilities for normalizing the population coordinates over the loci: "inertia", "lambda1", "uniform", or "internal". These options are used with MCoA and MFA only; |
scannf |
a logical value indicating whether the eigenvalues bar plots should be displayed; |
nf |
if scannf is FALSE, an integer indicating the number of kept axes for the multiple analysis; |
full |
a logical value indicating whether all the axes should be kept in the separated analyses (one analysis, DPCoA, per locus); |
nfsep |
if full is FALSE, a vector indicating the number of kept axes for each of the separated analyses; |
tol |
a tolerance threshold for null eigenvalues (a value less than tol times the first one is considered as null); |
object |
an object of class 'mdpcoa'; |
xax |
the number of the x-axis; |
yax |
the number of the y-axis; |
mfrow |
a vector of the form 'c(nr,nc)', otherwise computed by as special own function 'n2mfrow'; |
which.tab |
a numeric vector containing the numbers of the loci to analyse; |
includepop |
a logical indicating if the populations must be displayed. In that case, the alleles are displayed by points and the populations by labels; |
clab |
a character size for the labels; |
cpoi |
a character size for plotting the points, used with 'par("cex")'*cpoint. If zero, no points are drawn; |
unique.scale |
if TRUE, all the arrays of figures have the same scale; |
csub |
a character size for the labels of the arrays of figures used with 'par("cex")*csub'; |
possub |
a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", "bottomright"); |
dnaobj |
a list of dna sequences that can be obtained with the function |
pop |
a factor that gives the name of the population to which each sequence belongs; |
model |
a vector giving the model to be applied for the calculations of the distances for each locus. One model should be attributed to each locus, given that the loci are in alphabetical order. The models can take the following values: "raw", "JC69", "K80" (the default), "F81", "K81", "F84", "BH87", "T92", "TN93", "GG95", "logdet", or "paralin". See the help documentation for the function "dist.dna" of ape for a describtion of the models. |
... |
|
An object obtained by the function mdpcoa has two classes. The first one is "mdpcoa" and the second is either "mcoa", or "statis", or "mfa", depending on the method chosen. Consequently, other functions already available in ade4 for displaying graphical results can be used: With MCoA, - plot.mcoa: this function displays (1) the differences among the populations according to each locus and the compromise, (2) the projection of the principal axes of the individual analyses onto the synthetic variables, (3) the projection of the principal axes of the individual analyses onto the co-inertia axes, (4) the squared vectorial covariance among the coinertia scores and the synthetic variables; - kplot.mcoa: this function divides previous displays (figures 1, 2, or 3 described in plot.mcoa) by giving one plot per locus.
With STATIS, - plot.statis: this function displays (1) the scores of each locus according to the two first eigenvectors of the matrix Rv, (2) the scatter diagram of the differences among populations according to the compromise, (3) the weight attributed to each locus in abscissa and the vectorial covariance among each individual analysis with the notations in the main text of the paper) and the compromise analysis in ordinates, (4) the covariance between the principal component inertia axes of each locus and the axes of the compromise space; - kplot.statis: this function displays for each locus the projection of the principal axes onto the compromise space.
With MFA, - plot.mfa: this function displays (1) the differences among the populations according to each locus and the compromise, (2) the projection of the principal axes of the individual analyses onto the compromise, (3) the covariance between the principal component inertia axes of each locus and the axes of the compromise space, (4) for each axis of the compromise, the amount of inertia conserved by the projection of the individual analyses onto the common space. - kplot.mfa: this function displays for each locus the projection of the principal axes and populations onto the compromise space.
The functions provide the following results:
dist.ktab |
returns an object of class |
Sandrine Pavoine [email protected]
Pavoine, S. and Bailly, X. (2007) New analysis for consistency among markers in the study of genetic diversity:
development and application to the description of bacterial diversity. BMC Evolutionary Biology, 7, e156.
Pavoine, S., Dufour, A.B. and Chessel, D. (2004) From dissimilarities among species to dissimilarities among communities: a double principal coordinate analysis. Journal of Theoretical Biology, 228, 523–537.
# The functions used below require the package ape data(rhizobium) if (requireNamespace("ape", quietly = TRUE)) { dat <- prep.mdpcoa(rhizobium[[1]], rhizobium[[2]], model = c("F84", "F84", "F84", "F81"), pairwise.deletion = TRUE) sam <- dat$sam dis <- dat$dis # The distances should be Euclidean. # Several transformations exist to render a distance object Euclidean # (see functions cailliez, lingoes and quasieuclid in the ade4 package). # Here we use the quasieuclid function. dis <- lapply(dis, quasieuclid) mdpcoa1 <- mdpcoa(sam, dis, scannf = FALSE, nf = 2) # Reference analysis plot(mdpcoa1) # Differences between the loci kplot(mdpcoa1) # Alleles projected on the population maps. kplotX.mdpcoa(mdpcoa1) }
# The functions used below require the package ape data(rhizobium) if (requireNamespace("ape", quietly = TRUE)) { dat <- prep.mdpcoa(rhizobium[[1]], rhizobium[[2]], model = c("F84", "F84", "F84", "F81"), pairwise.deletion = TRUE) sam <- dat$sam dis <- dat$dis # The distances should be Euclidean. # Several transformations exist to render a distance object Euclidean # (see functions cailliez, lingoes and quasieuclid in the ade4 package). # Here we use the quasieuclid function. dis <- lapply(dis, quasieuclid) mdpcoa1 <- mdpcoa(sam, dis, scannf = FALSE, nf = 2) # Reference analysis plot(mdpcoa1) # Differences between the loci kplot(mdpcoa1) # Alleles projected on the population maps. kplotX.mdpcoa(mdpcoa1) }
This data set contains information about sites, environmental variables and Ephemeroptera Species.
data(meau)
data(meau)
meau
is a list of 3 components.
is a data frame with 24 sites and 10 physicochemical variables.
is a data frame with 24 sites and 13 Ephemeroptera Species.
is a data frame with 24 sites and 2 factors.
season
: is a factor with 4 levels = seasons.
site
: is a factor with 6 levels = sites.
Data set equivalents to meaudret
, except that one site (6) along the Bourne (a Meaudret affluent) and
one physico chemical variable - the oxygen concentration were added.
Pegaz-Maucet, D. (1980) Impact d'une perturbation d'origine organique sur la dérive des macro-invertébrés benthiques d'un cours d'eau. Comparaison avec le benthos. Thèse de 3ème cycle, Université Lyon 1, 130 p.
Thioulouse, J., Simier, M. and Chessel, D. (2004) Simultaneous analysis of a sequence of paired ecological tables. Ecology, 85, 1, 272–283.
data(meau) pca1 <- dudi.pca(meau$env, scan = FALSE, nf = 4) pca2 <- bca(pca1, meau$design$season, scan = FALSE, nf = 2) if(adegraphicsLoaded()) { g1 <- s.class(pca1$li, meau$design$season, psub.text = "Principal Component Analysis", plot = FALSE) g2 <- s.class(pca2$ls, meau$design$season, psub.text = "Between seasons Principal Component Analysis", plot = FALSE) g3 <- s.corcircle(pca1$co, plot = FALSE) g4 <- s.corcircle(pca2$as, plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) s.class(pca1$li, meau$design$season, sub = "Principal Component Analysis") s.class(pca2$ls, meau$design$season, sub = "Between seasons Principal Component Analysis") s.corcircle(pca1$co) s.corcircle(pca2$as) par(mfrow = c(1, 1)) }
data(meau) pca1 <- dudi.pca(meau$env, scan = FALSE, nf = 4) pca2 <- bca(pca1, meau$design$season, scan = FALSE, nf = 2) if(adegraphicsLoaded()) { g1 <- s.class(pca1$li, meau$design$season, psub.text = "Principal Component Analysis", plot = FALSE) g2 <- s.class(pca2$ls, meau$design$season, psub.text = "Between seasons Principal Component Analysis", plot = FALSE) g3 <- s.corcircle(pca1$co, plot = FALSE) g4 <- s.corcircle(pca2$as, plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) s.class(pca1$li, meau$design$season, sub = "Principal Component Analysis") s.class(pca2$ls, meau$design$season, sub = "Between seasons Principal Component Analysis") s.corcircle(pca1$co) s.corcircle(pca2$as) par(mfrow = c(1, 1)) }
This data set contains information about sites, environmental variables and Ephemeroptera Species.
data(meaudret)
data(meaudret)
meaudret
is a list of 4 components.
is a data frame with 20 sites and 9 variables.
is a data frame with 20 sites and 13 Ephemeroptera Species.
is a data frame with 20 sites and 2 factors.
season
is a factor with 4 levels = seasons.
site
is a factor with 5 levels = sites along the Meaudret river.
is a character vector containing the names of the 13 species.
Data set equivalents to meau
: site (6) on the Bourne (a Meaudret affluent) and
oxygen concentration were removed.
Pegaz-Maucet, D. (1980) Impact d'une perturbation d'origine organique sur la dérive des macro-invertébrés benthiques d'un cours d'eau. Comparaison avec le benthos. Thèse de 3ème cycle, Université Lyon 1, 130 p.
Thioulouse, J., Simier, M. and Chessel, D. (2004) Simultaneous analysis of a sequence of paired ecological tables. Ecology, 85, 1, 272–283.
data(meaudret) pca1 <- dudi.pca(meaudret$env, scan = FALSE, nf = 4) pca2 <- bca(pca1, meaudret$design$season, scan = FALSE, nf = 2) if(adegraphicsLoaded()) { g1 <- s.class(pca1$li, meaudret$design$season, psub.text = "Principal Component Analysis", plot = FALSE) g2 <- s.class(pca2$ls, meaudret$design$season, psub.text = "Between dates Principal Component Analysis", plot = FALSE) g3 <- s.corcircle(pca1$co, plot = FALSE) g4 <- s.corcircle(pca2$as, plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) s.class(pca1$li, meaudret$design$season, sub = "Principal Component Analysis") s.class(pca2$ls, meaudret$design$season, sub = "Between dates Principal Component Analysis") s.corcircle(pca1$co) s.corcircle(pca2$as) par(mfrow = c(1, 1)) }
data(meaudret) pca1 <- dudi.pca(meaudret$env, scan = FALSE, nf = 4) pca2 <- bca(pca1, meaudret$design$season, scan = FALSE, nf = 2) if(adegraphicsLoaded()) { g1 <- s.class(pca1$li, meaudret$design$season, psub.text = "Principal Component Analysis", plot = FALSE) g2 <- s.class(pca2$ls, meaudret$design$season, psub.text = "Between dates Principal Component Analysis", plot = FALSE) g3 <- s.corcircle(pca1$co, plot = FALSE) g4 <- s.corcircle(pca2$as, plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) s.class(pca1$li, meaudret$design$season, sub = "Principal Component Analysis") s.class(pca2$ls, meaudret$design$season, sub = "Between dates Principal Component Analysis") s.corcircle(pca1$co) s.corcircle(pca2$as) par(mfrow = c(1, 1)) }
performs a multiple factorial analysis,
using an object of class ktab
.
mfa(X, option = c("lambda1", "inertia", "uniform", "internal"), scannf = TRUE, nf = 3) ## S3 method for class 'mfa' plot(x, xax = 1, yax = 2, option.plot = 1:4, ...) ## S3 method for class 'mfa' print(x, ...) ## S3 method for class 'mfa' summary(object, ...)
mfa(X, option = c("lambda1", "inertia", "uniform", "internal"), scannf = TRUE, nf = 3) ## S3 method for class 'mfa' plot(x, xax = 1, yax = 2, option.plot = 1:4, ...) ## S3 method for class 'mfa' print(x, ...) ## S3 method for class 'mfa' summary(object, ...)
X |
K-tables, an object of class |
option |
a string of characters for the weighting of arrays options :
|
scannf |
a logical value indicating whether the eigenvalues bar plot should be displayed |
nf |
if scannf FALSE, an integer indicating the number of kept axes |
x , object
|
an object of class 'mfa' |
xax , yax
|
the numbers of the x-axis and the y-axis |
option.plot |
an integer between 1 and 4, otherwise the 4 components of the plot are displayed |
... |
further arguments passed to or from other methods |
Returns a list including :
tab |
a data frame with the modified array |
rank |
a vector of ranks for the analyses |
eig |
a numeric vector with the all eigenvalues |
li |
a data frame with the coordinates of rows |
TL |
a data frame with the factors associated to the rows (indicators of table) |
co |
a data frame with the coordinates of columns |
TC |
a data frame with the factors associated to the columns (indicators of table) |
blo |
a vector indicating the number of variables for each table |
lisup |
a data frame with the projections of normalized scores of rows for each table |
link |
a data frame containing the projected inertia and the links between the arrays and the reference array |
Daniel Chessel
Anne-Béatrice Dufour [email protected]
Escofier, B. and Pagès, J. (1994) Multiple factor analysis (AFMULT package), Computational Statistics and Data Analysis, 18, 121–140.
data(friday87) w1 <- data.frame(scale(friday87$fau, scal = FALSE)) w2 <- ktab.data.frame(w1, friday87$fau.blo, tabnames = friday87$tab.names) mfa1 <- mfa(w2, scann = FALSE) mfa1 plot(mfa1) data(escopage) w <- data.frame(scale(escopage$tab)) w <- ktab.data.frame(w, escopage$blo, tabnames = escopage$tab.names) plot(mfa(w, scann = FALSE))
data(friday87) w1 <- data.frame(scale(friday87$fau, scal = FALSE)) w2 <- ktab.data.frame(w1, friday87$fau.blo, tabnames = friday87$tab.names) mfa1 <- mfa(w2, scann = FALSE) mfa1 plot(mfa1) data(escopage) w <- data.frame(scale(escopage$tab)) w <- ktab.data.frame(w, escopage$blo, tabnames = escopage$tab.names) plot(mfa(w, scann = FALSE))
This data set gives genetic relationships between cattle breeds with microsatellites.
data(microsatt)
data(microsatt)
microsatt
is a list of 4 components.
contains the allelic frequencies for 18 cattle breeds (Taurine or Zebu,French or African) and 9 microsatellites.
is a vector of the names of loci.
is a vector of the number of alleles per locus.
is a vector of the names of alleles.
Extract of data prepared by D. Laloë [email protected] from data used in:
Moazami-Goudarzi, K., D. Laloë, J. P. Furet, and F. Grosclaude (1997) Analysis of genetic relationships between 10 cattle breeds with 17 microsatellites. Animal Genetics, 28, 338–345.
Souvenir Zafindrajaona, P.,Zeuh V. ,Moazami-Goudarzi K., Laloë D., Bourzat D., Idriss A., and Grosclaude F. (1999) Etude du statut phylogénétique du bovin Kouri du lac Tchad à l'aide de marqueurs moléculaires. Revue d'Elevage et de Médecine Vétérinaire des pays Tropicaux, 55, 155–162.
Moazami-Goudarzi, K., Belemsaga D. M. A., Ceriotti G., Laloë D. , Fagbohoun F., Kouagou N. T., Sidibé I., Codjia V., Crimella M. C., Grosclaude F. and Touré S. M. (2001)
Caractérisation de la race bovine Somba à l'aide de marqueurs moléculaires.
Revue d'Elevage et de Médecine Vétérinaire des pays Tropicaux, 54, 1–10.
See a data description at http://pbil.univ-lyon1.fr/R/pdf/pps055.pdf (in French).
## Not run: data(microsatt) fac <- factor(rep(microsatt$loci.names, microsatt$loci.eff)) w <- dudi.coa(data.frame(t(microsatt$tab)), scann = FALSE) wit <- wca(w, fac, scann = FALSE) microsatt.ktab <- ktab.within(wit) plot(sepan(microsatt.ktab)) # 9 separated correspondence analyses plot(mcoa(microsatt.ktab, scan = FALSE)) plot(mfa(microsatt.ktab, scan = FALSE)) plot(statis(microsatt.ktab, scan = FALSE)) ## End(Not run)
## Not run: data(microsatt) fac <- factor(rep(microsatt$loci.names, microsatt$loci.eff)) w <- dudi.coa(data.frame(t(microsatt$tab)), scann = FALSE) wit <- wca(w, fac, scann = FALSE) microsatt.ktab <- ktab.within(wit) plot(sepan(microsatt.ktab)) # 9 separated correspondence analyses plot(mcoa(microsatt.ktab, scan = FALSE)) plot(mfa(microsatt.ktab, scan = FALSE)) plot(statis(microsatt.ktab, scan = FALSE)) ## End(Not run)
This data set describes the phylogeny of 49 teleos fishes as reported by Rochet et al. (2000). It also gives life-history traits corresponding to these 49 species.
data(mjrochet)
data(mjrochet)
mjrochet
is a list containing the 2 following objects :
is a character string giving the phylogenetic tree in Newick format.
is a data frame with 49 rows and 7 traits.
Variables of mjrochet$tab
are the following ones : tm (age at maturity (years)),
lm (length at maturity (cm)), l05 (length at 5 per cent survival (cm)),
t05 (time to 5 per cent survival (years)), fb (slope of the log-log fecundity-length relationship),
fm (fecundity the year of maturity), egg (volume of eggs ()).
Data taken from:
Summary of data - Clupeiformes : http://www.ifremer.fr/maerha/clupe.html
Summary of data - Argentiniformes : http://www.ifremer.fr/maerha/argentin.html
Summary of data - Salmoniformes : http://www.ifremer.fr/maerha/salmon.html
Summary of data - Gadiformes : http://www.ifremer.fr/maerha/gadi.html
Summary of data - Lophiiformes : http://www.ifremer.fr/maerha/loph.html
Summary of data - Atheriniformes : http://www.ifremer.fr/maerha/ather.html
Summary of data - Perciformes : http://www.ifremer.fr/maerha/perci.html
Summary of data - Pleuronectiformes : http://www.ifremer.fr/maerha/pleuro.html
Summary of data - Scorpaeniformes : http://www.ifremer.fr/maerha/scorpa.html
Phylogenetic tree : http://www.ifremer.fr/maerha/life_history.html
Rochet, M. J., Cornillon, P-A., Sabatier, R. and Pontier, D. (2000) Comparative analysis of phylogenic and fishing effects in life history patterns of teleos fishes. Oïkos, 91, 255–270.
data(mjrochet) mjrochet.phy <- newick2phylog(mjrochet$tre) tab <- log((mjrochet$tab)) tab0 <- data.frame(scalewt(tab)) table.phylog(tab0, mjrochet.phy, csi = 2, clabel.r = 0.75) if (requireNamespace("adephylo", quietly = TRUE)) { adephylo::orthogram(tab0[,1], ortho = mjrochet.phy$Bscores) }
data(mjrochet) mjrochet.phy <- newick2phylog(mjrochet$tre) tab <- log((mjrochet$tab)) tab0 <- data.frame(scalewt(tab)) table.phylog(tab0, mjrochet.phy, csi = 2, clabel.r = 0.75) if (requireNamespace("adephylo", quietly = TRUE)) { adephylo::orthogram(tab0[,1], ortho = mjrochet.phy$Bscores) }
The function mld
performs an additive decomposition of the input vector x
onto sub-spaces associated
to an orthonormal orthobasis. The sub-spaces are defined by levels of the input factor level
.
The function haar2level
builds the factor level
such that the multi level decomposition corresponds exactly to a multiresolution analysis performed with the haar basis.
mld(x, orthobas, level, na.action = c("fail", "mean"), plot = TRUE, dfxy = NULL, phylog = NULL, ...) haar2level(x)
mld(x, orthobas, level, na.action = c("fail", "mean"), plot = TRUE, dfxy = NULL, phylog = NULL, ...) haar2level(x)
x |
is a vector or a time serie containing the data to be decomposed. This must be a dyadic length vector (power of 2) for the function |
orthobas |
is a data frame containing the vectors of the orthonormal basis. |
level |
is a factor which levels define the sub-spaces on which the function |
na.action |
if 'fail' stops the execution of the current expression when |
plot |
if TRUE plot |
dfxy |
is a data frame with two coordinates. |
phylog |
is an object of class |
... |
further arguments passed to or from other methods. |
A data frame with the components resulting from the decomposition.
Sébastien Ollier [email protected]
Mallat, S. G. (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, 7, 674–693.
Percival, D. B. and Walden, A. T. (2000) Wavelet Methods for Time Series Analysis, Cambridge University Press.
gridrowcol
, orthobasis
,
orthogram
, mra
for multiresolution analysis with various families of wavelets
## Not run: # decomposition of a time serie data(co2) x <- log(co2) orthobas <- orthobasis.line(length(x)) level<-rep("D", 467) level[1:3]<-rep("A", 3) level[c(77,78,79,81)]<-rep("B", 4) level[156]<-"C" level<-as.factor(level) res <- mld(x, orthobas, level) sum(scale(x, scale = FALSE) - apply(res, 1, sum)) ## End(Not run) # decomposition of a biological trait on a phylogeny data(palm) vfruit<-palm$traits$vfruit vfruit<-scalewt(vfruit) palm.phy<-newick2phylog(palm$tre) level <- rep("F", 65) level[c(4, 21, 3, 6, 13)] <- LETTERS[1:5] level <- as.factor(level) res <- mld(as.vector(vfruit), palm.phy$Bscores, level, phylog = palm.phy, clabel.nod = 0.7, f.phylog=0.8, csize = 2, clabel.row = 0.7, clabel.col = 0.7)
## Not run: # decomposition of a time serie data(co2) x <- log(co2) orthobas <- orthobasis.line(length(x)) level<-rep("D", 467) level[1:3]<-rep("A", 3) level[c(77,78,79,81)]<-rep("B", 4) level[156]<-"C" level<-as.factor(level) res <- mld(x, orthobas, level) sum(scale(x, scale = FALSE) - apply(res, 1, sum)) ## End(Not run) # decomposition of a biological trait on a phylogeny data(palm) vfruit<-palm$traits$vfruit vfruit<-scalewt(vfruit) palm.phy<-newick2phylog(palm$tre) level <- rep("F", 65) level[c(4, 21, 3, 6, 13)] <- LETTERS[1:5] level <- as.factor(level) res <- mld(as.vector(vfruit), palm.phy$Bscores, level, phylog = palm.phy, clabel.nod = 0.7, f.phylog=0.8, csize = 2, clabel.row = 0.7, clabel.col = 0.7)
This data set gives the abundance of 32 mollusk species in 163 samples. For each sample, 4 informations are known : the sampling sites, the seasons, the sampler types and the time of exposure.
data(mollusc)
data(mollusc)
mollusc
is a list of 2 objects.
is a data frame with 163 samples and 32 mollusk species (abundance).
contains the 163 samples and 4 variables.
Richardot-Coulet, M., Chessel D. and Bournaud M. (1986) Typological value of the benthos of old beds of a large river. Methodological approach. Archiv fùr Hydrobiologie, 107, 363–383.
data(mollusc) coa1 <- dudi.coa(log(mollusc$fau + 1), scannf = FALSE, nf = 3) if(adegraphicsLoaded()) { g1 <- s.class(coa1$li, mollusc$plan$site, ellipseSize = 0, starSize = 0, chullSize = 1, xax = 2, yax = 3, plot = FALSE) g2 <- s.class(coa1$li, mollusc$plan$season, ellipseSize = 0, starSize = 0, chullSize = 1, xax = 2, yax = 3, plot = FALSE) g3 <- s.class(coa1$li, mollusc$plan$method, ellipseSize = 0, starSize = 0, chullSize = 1, xax = 2, yax = 3, plot = FALSE) g4 <- s.class(coa1$li, mollusc$plan$duration, ellipseSize = 0, starSize = 0, chullSize = 1, xax = 2, yax = 3, plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) s.chull(coa1$li, mollusc$plan$site, 2, 3, opt = 1, cpoi = 1) s.chull(coa1$li, mollusc$plan$season, 2, 3, opt = 1, cpoi = 1) s.chull(coa1$li, mollusc$plan$method, 2, 3, opt = 1, cpoi = 1) s.chull(coa1$li, mollusc$plan$duration, 2, 3, opt = 1, cpoi = 1) par(mfrow = c(1, 1)) }
data(mollusc) coa1 <- dudi.coa(log(mollusc$fau + 1), scannf = FALSE, nf = 3) if(adegraphicsLoaded()) { g1 <- s.class(coa1$li, mollusc$plan$site, ellipseSize = 0, starSize = 0, chullSize = 1, xax = 2, yax = 3, plot = FALSE) g2 <- s.class(coa1$li, mollusc$plan$season, ellipseSize = 0, starSize = 0, chullSize = 1, xax = 2, yax = 3, plot = FALSE) g3 <- s.class(coa1$li, mollusc$plan$method, ellipseSize = 0, starSize = 0, chullSize = 1, xax = 2, yax = 3, plot = FALSE) g4 <- s.class(coa1$li, mollusc$plan$duration, ellipseSize = 0, starSize = 0, chullSize = 1, xax = 2, yax = 3, plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) s.chull(coa1$li, mollusc$plan$site, 2, 3, opt = 1, cpoi = 1) s.chull(coa1$li, mollusc$plan$season, 2, 3, opt = 1, cpoi = 1) s.chull(coa1$li, mollusc$plan$method, 2, 3, opt = 1, cpoi = 1) s.chull(coa1$li, mollusc$plan$duration, 2, 3, opt = 1, cpoi = 1) par(mfrow = c(1, 1)) }
The monde84
data frame gives five demographic variables
for 48 countries in the world.
data(monde84)
data(monde84)
This data frame contains the following columns:
pib: Gross Domestic Product
croipop: Growth of the population
morta: Infant Mortality
anal: Literacy Rate
scol: Percentage of children in full-time education
Geze, F. and Coll., eds. (1984) L'état du Monde 1984 : annuaire économique et géopolitique mondial. La Découverte, Paris.
data(monde84) X <- cbind.data.frame(lpib = log(monde84$pib), monde84$croipop) Y <- cbind.data.frame(lmorta = log(monde84$morta), lanal = log(monde84$anal + 1), rscol = sqrt(100 - monde84$scol)) pcaY <- dudi.pca(Y, scan = FALSE) pcaiv1 <- pcaiv(pcaY, X0 <- scale(X), scan = FALSE) sum(cor(pcaiv1$l1[,1], Y0 <- scale(Y))^2) pcaiv1$eig[1] #the same
data(monde84) X <- cbind.data.frame(lpib = log(monde84$pib), monde84$croipop) Y <- cbind.data.frame(lmorta = log(monde84$morta), lanal = log(monde84$anal + 1), rscol = sqrt(100 - monde84$scol)) pcaY <- dudi.pca(Y, scan = FALSE) pcaiv1 <- pcaiv(pcaY, X0 <- scale(X), scan = FALSE) sum(cor(pcaiv1$l1[,1], Y0 <- scale(Y))^2) pcaiv1$eig[1] #the same
This data set gives a morphological description of 153 athletes split in five different sports.
data(morphosport)
data(morphosport)
morphosport
is a list of 2 objects.
is a data frame with 153 athletes and 5 variables.
is a factor with 6 items
Variables of morphosport$tab
are the following ones: dbi (biacromial diameter (cm)),
tde (height (cm)), tas (distance from the buttocks to the top of the head (cm)),
lms (length of the upper limbs (cm)), poids (weigth (kg)).
The levels of morphosport$sport
are: athl (athletics), foot (football),
hand (handball), judo, nata (swimming), voll (volleyball).
Mimouni , N. (1996) Contribution de méthodes biométriques à l'analyse de la morphotypologie des sportifs. Thèse de doctorat. Université Lyon 1.
data(morphosport) plot(discrimin(dudi.pca(morphosport$tab, scan = FALSE), morphosport$sport, scan = FALSE))
data(morphosport) plot(discrimin(dudi.pca(morphosport$tab, scan = FALSE), morphosport$sport, scan = FALSE))
Minimal Spanning Tree
mstree(xdist, ngmax = 1)
mstree(xdist, ngmax = 1)
xdist |
an object of class |
ngmax |
a component number (default=1). Select 1 for getting classical MST. To add n supplementary edges k times: select k+1. |
returns an object of class neig
Daniel Chessel
data(mafragh) maf.coa <- dudi.coa(mafragh$flo, scan = FALSE) maf.mst <- ade4::mstree(dist.dudi(maf.coa), 1) if(adegraphicsLoaded()) { g0 <- s.label(maf.coa$li, plab.cex = 0, ppoints.cex = 2, nb = neig2nb(maf.mst)) } else { s.label(maf.coa$li, clab = 0, cpoi = 2, neig = maf.mst, cnei = 1) } xy <- data.frame(x = runif(20), y = runif(20)) if(adegraphicsLoaded()) { g1 <- s.label(xy, xlim = c(0, 1), ylim = c(0, 1), nb = neig2nb(ade4::mstree(dist.quant(xy, 1), 1)), plot = FALSE) g2 <- s.label(xy, xlim = c(0, 1), ylim = c(0, 1), nb = neig2nb(ade4::mstree(dist.quant(xy, 1), 2)), plot = FALSE) g3 <- s.label(xy, xlim = c(0, 1), ylim = c(0, 1), nb = neig2nb(ade4::mstree(dist.quant(xy, 1), 3)), plot = FALSE) g4 <- s.label(xy, xlim = c(0, 1), ylim = c(0, 1), nb = neig2nb(ade4::mstree(dist.quant(xy, 1), 4)), plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) for(k in 1:4) { neig <- mstree(dist.quant(xy, 1), k) s.label(xy, xlim = c(0, 1), ylim = c(0, 1), addax = FALSE, neig = neig) } }
data(mafragh) maf.coa <- dudi.coa(mafragh$flo, scan = FALSE) maf.mst <- ade4::mstree(dist.dudi(maf.coa), 1) if(adegraphicsLoaded()) { g0 <- s.label(maf.coa$li, plab.cex = 0, ppoints.cex = 2, nb = neig2nb(maf.mst)) } else { s.label(maf.coa$li, clab = 0, cpoi = 2, neig = maf.mst, cnei = 1) } xy <- data.frame(x = runif(20), y = runif(20)) if(adegraphicsLoaded()) { g1 <- s.label(xy, xlim = c(0, 1), ylim = c(0, 1), nb = neig2nb(ade4::mstree(dist.quant(xy, 1), 1)), plot = FALSE) g2 <- s.label(xy, xlim = c(0, 1), ylim = c(0, 1), nb = neig2nb(ade4::mstree(dist.quant(xy, 1), 2)), plot = FALSE) g3 <- s.label(xy, xlim = c(0, 1), ylim = c(0, 1), nb = neig2nb(ade4::mstree(dist.quant(xy, 1), 3)), plot = FALSE) g4 <- s.label(xy, xlim = c(0, 1), ylim = c(0, 1), nb = neig2nb(ade4::mstree(dist.quant(xy, 1), 4)), plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) for(k in 1:4) { neig <- mstree(dist.quant(xy, 1), k) s.label(xy, xlim = c(0, 1), ylim = c(0, 1), addax = FALSE, neig = neig) } }
Generic methods print and summary for mulitblock objects
## S3 method for class 'multiblock' summary(object, ...) ## S3 method for class 'multiblock' print(x, ...)
## S3 method for class 'multiblock' summary(object, ...) ## S3 method for class 'multiblock' print(x, ...)
object |
|
x |
|
... |
other arguments to be passed to methods |
Stéphanie Bougeard ([email protected]) and Stéphane Dray ([email protected])
Bougeard, S. and Dray S. (2018) Supervised Multiblock Analysis in R with the ade4 Package. Journal of Statistical Software, 86 (1), 1-17. doi:10.18637/jss.v086.i01
neig
creates objects of class neig
with :
a list of edges
a binary square matrix
a list of vectors of neighbours
an integer (linear and circular graphs)
a data frame of polygons (area)
scores.neig returns the eigenvectors of neighbouring,
orthonormalized scores (null average, unit variance 1/n and null covariances) of maximal autocorrelation.
nb2neig returns an object of class neig
using an object of class nb
in the library 'spdep'
neig2nb returns an object of class nb
using an object of class neig
neig2mat returns the incidence matrix between edges (1 = neighbour ; 0 = no neighbour)
neig.util.GtoL and neig.util.LtoG are utilities.
neig(list = NULL, mat01 = NULL, edges = NULL, n.line = NULL, n.circle = NULL, area = NULL) scores.neig (obj) ## S3 method for class 'neig' print(x, ...) ## S3 method for class 'neig' summary(object, ...) nb2neig (nb) neig2nb (neig) neig2mat (neig)
neig(list = NULL, mat01 = NULL, edges = NULL, n.line = NULL, n.circle = NULL, area = NULL) scores.neig (obj) ## S3 method for class 'neig' print(x, ...) ## S3 method for class 'neig' summary(object, ...) nb2neig (nb) neig2nb (neig) neig2mat (neig)
list |
a list which each component gives the number of neighbours |
mat01 |
a symmetric square matrix of 0-1 values |
edges |
a matrix of 2 columns with integer values giving a list of edges |
n.line |
the number of points for a linear plot |
n.circle |
the number of points for a circular plot |
area |
a data frame containing a polygon set (see area.plot) |
nb |
an object of class 'nb' |
neig , x , obj , object
|
an object of class 'neig' |
... |
further arguments passed to or from other methods |
Daniel Chessel
Thioulouse, J., D. Chessel, and S. Champely. 1995. Multivariate analysis of spatial patterns: a unified approach to local and global structures. Environmental and Ecological Statistics, 2, 1–14.
if(!adegraphicsLoaded()) { if(requireNamespace("deldir", quietly = TRUE)) { data(mafragh) par(mfrow = c(2, 1)) provi <- deldir::deldir(mafragh$xy) provi.neig <- neig(edges = as.matrix(provi$delsgs[, 5:6])) s.label(mafragh$xy, neig = provi.neig, inc = FALSE, addax = FALSE, clab = 0, cnei = 2) dist <- apply(provi.neig, 1, function(x) sqrt(sum((mafragh$xy[x[1], ] - mafragh$xy[x[2], ]) ^ 2))) #hist(dist, nclass = 50) mafragh.neig <- neig(edges = provi.neig[dist < 50, ]) s.label(mafragh$xy, neig = mafragh.neig, inc = FALSE, addax = FALSE, clab = 0, cnei = 2) par(mfrow = c(1, 1)) data(irishdata) irish.neig <- neig(area = irishdata$area) summary(irish.neig) print(irish.neig) s.label(irishdata$xy, neig = irish.neig, cneig = 3, area = irishdata$area, clab = 0.8, inc = FALSE) irish.scores <- scores.neig(irish.neig) par(mfrow = c(2, 3)) for(i in 1:6) s.value(irishdata$xy, irish.scores[, i], inc = FALSE, grid = FALSE, addax = FALSE, neig = irish.neig, csi = 2, cleg = 0, sub = paste("Eigenvector ",i), csub = 2) par(mfrow = c(1, 1)) a.neig <- neig(n.circle = 16) a.scores <- scores.neig(a.neig) xy <- cbind.data.frame(cos((1:16) * pi / 8), sin((1:16) * pi / 8)) par(mfrow = c(4, 4)) for(i in 1:15) s.value(xy, a.scores[, i], neig = a.neig, csi = 3, cleg = 0) par(mfrow = c(1, 1)) a.neig <- neig(n.line = 28) a.scores <- scores.neig(a.neig) par(mfrow = c(7, 4)) par(mar = c(1.1, 2.1, 0.1, 0.1)) for(i in 1:27) barplot(a.scores[, i], col = grey(0.8)) par(mfrow = c(1, 1)) } if(requireNamespace("spdep", quietly = TRUE)) { data(mafragh) maf.rel <- spdep::relativeneigh(as.matrix(mafragh$xy)) maf.rel <- spdep::graph2nb(maf.rel) s.label(mafragh$xy, neig = neig(list = maf.rel), inc = FALSE, clab = 0, addax = FALSE, cne = 1, cpo = 2) par(mfrow = c(2, 2)) w <- matrix(runif(100), 50, 2) x.gab <- spdep::gabrielneigh(w) x.gab <- spdep::graph2nb(x.gab) s.label(data.frame(w), neig = neig(list = x.gab), inc = FALSE, clab = 0, addax = FALSE, cne = 1, cpo = 2, sub = "relative") x.rel <- spdep::relativeneigh(w) x.rel <- spdep::graph2nb(x.rel) s.label(data.frame(w), neig = neig(list = x.rel), inc = FALSE, clab = 0, addax = FALSE, cne = 1, cpo = 2, sub = "Gabriel") k1 <- spdep::knn2nb(spdep::knearneigh(w)) s.label(data.frame(w), neig = neig(list = k1), inc = FALSE, clab = 0, addax = FALSE, cne = 1, cpo = 2, sub = "k nearest neighbours") all.linked <- max(unlist(spdep::nbdists(k1, w))) z <- spdep::dnearneigh(w, 0, all.linked) s.label(data.frame(w), neig = neig(list = z), inc = FALSE, clab = 0, addax = FALSE, cne = 1, cpo = 2, sub = "Neighbourhood contiguity by distance") par(mfrow = c(1, 1)) } }
if(!adegraphicsLoaded()) { if(requireNamespace("deldir", quietly = TRUE)) { data(mafragh) par(mfrow = c(2, 1)) provi <- deldir::deldir(mafragh$xy) provi.neig <- neig(edges = as.matrix(provi$delsgs[, 5:6])) s.label(mafragh$xy, neig = provi.neig, inc = FALSE, addax = FALSE, clab = 0, cnei = 2) dist <- apply(provi.neig, 1, function(x) sqrt(sum((mafragh$xy[x[1], ] - mafragh$xy[x[2], ]) ^ 2))) #hist(dist, nclass = 50) mafragh.neig <- neig(edges = provi.neig[dist < 50, ]) s.label(mafragh$xy, neig = mafragh.neig, inc = FALSE, addax = FALSE, clab = 0, cnei = 2) par(mfrow = c(1, 1)) data(irishdata) irish.neig <- neig(area = irishdata$area) summary(irish.neig) print(irish.neig) s.label(irishdata$xy, neig = irish.neig, cneig = 3, area = irishdata$area, clab = 0.8, inc = FALSE) irish.scores <- scores.neig(irish.neig) par(mfrow = c(2, 3)) for(i in 1:6) s.value(irishdata$xy, irish.scores[, i], inc = FALSE, grid = FALSE, addax = FALSE, neig = irish.neig, csi = 2, cleg = 0, sub = paste("Eigenvector ",i), csub = 2) par(mfrow = c(1, 1)) a.neig <- neig(n.circle = 16) a.scores <- scores.neig(a.neig) xy <- cbind.data.frame(cos((1:16) * pi / 8), sin((1:16) * pi / 8)) par(mfrow = c(4, 4)) for(i in 1:15) s.value(xy, a.scores[, i], neig = a.neig, csi = 3, cleg = 0) par(mfrow = c(1, 1)) a.neig <- neig(n.line = 28) a.scores <- scores.neig(a.neig) par(mfrow = c(7, 4)) par(mar = c(1.1, 2.1, 0.1, 0.1)) for(i in 1:27) barplot(a.scores[, i], col = grey(0.8)) par(mfrow = c(1, 1)) } if(requireNamespace("spdep", quietly = TRUE)) { data(mafragh) maf.rel <- spdep::relativeneigh(as.matrix(mafragh$xy)) maf.rel <- spdep::graph2nb(maf.rel) s.label(mafragh$xy, neig = neig(list = maf.rel), inc = FALSE, clab = 0, addax = FALSE, cne = 1, cpo = 2) par(mfrow = c(2, 2)) w <- matrix(runif(100), 50, 2) x.gab <- spdep::gabrielneigh(w) x.gab <- spdep::graph2nb(x.gab) s.label(data.frame(w), neig = neig(list = x.gab), inc = FALSE, clab = 0, addax = FALSE, cne = 1, cpo = 2, sub = "relative") x.rel <- spdep::relativeneigh(w) x.rel <- spdep::graph2nb(x.rel) s.label(data.frame(w), neig = neig(list = x.rel), inc = FALSE, clab = 0, addax = FALSE, cne = 1, cpo = 2, sub = "Gabriel") k1 <- spdep::knn2nb(spdep::knearneigh(w)) s.label(data.frame(w), neig = neig(list = k1), inc = FALSE, clab = 0, addax = FALSE, cne = 1, cpo = 2, sub = "k nearest neighbours") all.linked <- max(unlist(spdep::nbdists(k1, w))) z <- spdep::dnearneigh(w, 0, all.linked) s.label(data.frame(w), neig = neig(list = z), inc = FALSE, clab = 0, addax = FALSE, cne = 1, cpo = 2, sub = "Neighbourhood contiguity by distance") par(mfrow = c(1, 1)) } }
This data set contains various exemples of phylogenetic trees in Newick format.
data(newick.eg)
data(newick.eg)
newick.eg
is a list containing 14 character strings in Newick format.
Trees 1 to 7 were obtained from the phylip software.
Trees 8 and 9 were obtained by Clémentine Carpentier-Gimaret.
Tree 10 was obtained from Treezilla Data Sets .
Trees 11 and 12 are taken from Bauwens and Díaz-Uriarte (1997).
Tree 13 is taken from Cheverud and Dow (1985).
Tree 13 is taken from Martins and Hansen (1997).
Bauwens, D. and Díaz-Uriarte, R. (1997) Covariation of life-history traits in lacertid lizards: a comparative study. American Naturalist, 149, 91–111.
Cheverud, J. and Dow, M.M. (1985) An autocorrelation analysis of genetic variation due to lineal fission in social groups of rhesus macaques. American Journal of Physical Anthropology, 67, 113–122.
Martins, E. P. and Hansen, T.F. (1997) Phylogenies and the comparative method: a general approach to incorporating phylogenetic information into the analysis of interspecific data. American Naturalist, 149, 646–667.
data(newick.eg) newick2phylog(newick.eg[[11]]) radial.phylog(newick2phylog(newick.eg[[7]]), circ = 1, clabel.l = 0.75)
data(newick.eg) newick2phylog(newick.eg[[11]]) radial.phylog(newick2phylog(newick.eg[[7]]), circ = 1, clabel.l = 0.75)
The first three functions ensure to create object of class phylog
from either a character string in Newick format (newick2phylog
) or an object of class 'hclust'
(hclust2phylog
) or a taxonomy (taxo2phylog
).
The function newick2phylog.addtools
is an internal function called by newick2phylog
, hclust2phylog
and taxo2phylog
when newick2phylog.addtools
= TRUE. It adds some items in 'phylog'
objects.
newick2phylog(x.tre, add.tools = TRUE, call = match.call()) hclust2phylog(hc, add.tools = TRUE) taxo2phylog(taxo, add.tools = FALSE, root="Root", abbrev=TRUE) newick2phylog.addtools(res, tol = 1e-07)
newick2phylog(x.tre, add.tools = TRUE, call = match.call()) hclust2phylog(hc, add.tools = TRUE) taxo2phylog(taxo, add.tools = FALSE, root="Root", abbrev=TRUE) newick2phylog.addtools(res, tol = 1e-07)
x.tre |
a character string corresponding to a phylogenetic tree in Newick format (see the phylip software) |
add.tools |
if TRUE, executes the function |
call |
call |
hc |
an object of class |
taxo |
an object of class |
res |
an object of class |
tol |
used in case 3 of |
root |
a character string for the root of the tree |
abbrev |
logical : if TRUE levels are abbreviated by column and two characters are added before |
Return object of class phylog
.
Daniel Chessel
Sébastien Ollier [email protected]
w <- "((((,,),,(,)),),(,));" w.phy <- newick2phylog(w) print(w.phy) plot(w.phy) ## Not run: # newick2phylog data(newick.eg) radial.phylog(newick2phylog(newick.eg[[8]], FALSE), cnode = 1, clabel.l = 0.8) w <- NULL w[1] <- "(,((((((((((((((((,,(,(,))),),(((,(,)),(,)),),(,(,)),(,)),(((((" w[2] <- ",(,)),),),(,)),((((,((,),((,(,)),))),(,)),(,(,),,((,),(,)),)),(" w[3] <- "(((((,),),(,(,))),),(,)),(((,),),)))),((,,((,),)),(,)),((,),(,)" w[4] <- ")),(((((((((,,),),,),),((,),)),(,),((,),)),),(((((,),),),((,),)" w[5] <- "),(((,(,(,(,)))),(,)),(((,),(((((((,),),),,),(,)),(,)),)),((,)" w[6] <- ",))))),(,((,),(,)),((,(,)),)))),((((,(,(,))),((,(,)),,((,(,)),)" w[7] <- ",)),(((,),),(((,),),))),((,),))),((((((((((,,,,(,)),),((,),)),(" w[8] <- ",(,))),(((((((((,(,)),(,)),((((,((,),(,(,(,))))),((,),(,(,))))," w[9] <- "),((,),))),(((((((((,(,)),((,),(,))),),),),(((,((,),)),),((,((," w[10] <- "),)),)),(,)),(,(,(,)))),((((,(,)),(,)),(((,),(,)),(,),,(,))),(," w[11] <- "))),(,,,))),((((,),),),(((,(,(,))),((,),)),(,)))),(,)),),(,((,(" w[12] <- ",)),),(((,),),))),),(((,),),(,),(,(,))),(((,),(,)),((,),(,))))," w[13] <- "(((,),((,),)),(((((,,,,,),(,)),(,)),(,((,),))),))),(,(((((,((((" w[14] <- ",(,)),),),)),),((,((,),((,((,),(,))),))),)),((((,),(((,),(,(,))" w[15] <- "),)),),)),((,),)))),(((,((,,((,),)),)),),((,),))),((,),(,))),((" w[16] <- ",),)),(((((,),((,(,)),(((,(,)),(,(((,),),))),))),(,),,),),),,(," w[17] <- ")),((((,),,),),((,,,),((,),((,),))))),((((((,(,)),,(,)),,(,),(," w[18] <- "),),(((((,(,(,),)),(((,),,),(,))),),),),,,((,),)),),)),(((((,)," w[19] <- "(,(,)),),((,((,),),,),)),(((((((,),((((,,,),(,(,))),(((,(,)),)," w[20] <- "(,))),)),),),),(,)),),),((,),))),((,),)),(((((((((((,),),((((((" w[21] <- ",),),((,),)),(,)),),)),(,)),),((((((,),),(((,),),)),(,)),),(,))" w[22] <- ",),),),),(,)),),((,),(,),,,)),(,(,(,)))),),(,)),),);" phy1 <- newick2phylog(w,FALSE) phy1 radial.phylog(phy1, clabel.l = 0, circle = 2.2, clea = 0.5, cnod = 0.5) data(newick.eg) radial.phylog(newick2phylog(newick.eg[[8]], FALSE), cnode = 1, clabel.l = 0.8) # hclust2phylog data(USArrests) hc <- hclust(dist(USArrests), "ave") par(mfrow = c(1,2)) plot(hc, hang = -1) phy <- hclust2phylog(hc) plot(phy, clabel.l = 0.75, clabel.n = 0.6, f = 0.75) par(mfrow = c(1,1)) row.names(USArrests) names(phy$leaves) #WARNING not the same for two reasons row.names(USArrests) <- gsub(" ","_",row.names(USArrests)) row.names(USArrests) names(phy$leaves) #WARNING not the same for one reason USArrests <- USArrests[names(phy$leaves),] row.names(USArrests) names(phy$leaves) #the same table.phylog(data.frame(scalewt(USArrests)), phy, csi = 2.5, clabel.r = 0.75, f = 0.7) #taxo2phylog data(taxo.eg) tax <- as.taxo(taxo.eg[[1]]) tax.phy <- taxo2phylog(as.taxo(taxo.eg[[1]])) par(mfrow = c(1,2)) plot(tax.phy, clabel.l = 1.25, clabel.n = 1.25, f = 0.75) plot(taxo2phylog(as.taxo(taxo.eg[[1]][sample(15),])), clabel.l = 1.25, clabel.n = 1.25, f = 0.75) par(mfrow=c(1,1)) plot(taxo2phylog(as.taxo(taxo.eg[[2]])), clabel.l = 1, clabel.n = 0.75, f = 0.65) ## End(Not run)
w <- "((((,,),,(,)),),(,));" w.phy <- newick2phylog(w) print(w.phy) plot(w.phy) ## Not run: # newick2phylog data(newick.eg) radial.phylog(newick2phylog(newick.eg[[8]], FALSE), cnode = 1, clabel.l = 0.8) w <- NULL w[1] <- "(,((((((((((((((((,,(,(,))),),(((,(,)),(,)),),(,(,)),(,)),(((((" w[2] <- ",(,)),),),(,)),((((,((,),((,(,)),))),(,)),(,(,),,((,),(,)),)),(" w[3] <- "(((((,),),(,(,))),),(,)),(((,),),)))),((,,((,),)),(,)),((,),(,)" w[4] <- ")),(((((((((,,),),,),),((,),)),(,),((,),)),),(((((,),),),((,),)" w[5] <- "),(((,(,(,(,)))),(,)),(((,),(((((((,),),),,),(,)),(,)),)),((,)" w[6] <- ",))))),(,((,),(,)),((,(,)),)))),((((,(,(,))),((,(,)),,((,(,)),)" w[7] <- ",)),(((,),),(((,),),))),((,),))),((((((((((,,,,(,)),),((,),)),(" w[8] <- ",(,))),(((((((((,(,)),(,)),((((,((,),(,(,(,))))),((,),(,(,))))," w[9] <- "),((,),))),(((((((((,(,)),((,),(,))),),),),(((,((,),)),),((,((," w[10] <- "),)),)),(,)),(,(,(,)))),((((,(,)),(,)),(((,),(,)),(,),,(,))),(," w[11] <- "))),(,,,))),((((,),),),(((,(,(,))),((,),)),(,)))),(,)),),(,((,(" w[12] <- ",)),),(((,),),))),),(((,),),(,),(,(,))),(((,),(,)),((,),(,))))," w[13] <- "(((,),((,),)),(((((,,,,,),(,)),(,)),(,((,),))),))),(,(((((,((((" w[14] <- ",(,)),),),)),),((,((,),((,((,),(,))),))),)),((((,),(((,),(,(,))" w[15] <- "),)),),)),((,),)))),(((,((,,((,),)),)),),((,),))),((,),(,))),((" w[16] <- ",),)),(((((,),((,(,)),(((,(,)),(,(((,),),))),))),(,),,),),),,(," w[17] <- ")),((((,),,),),((,,,),((,),((,),))))),((((((,(,)),,(,)),,(,),(," w[18] <- "),),(((((,(,(,),)),(((,),,),(,))),),),),,,((,),)),),)),(((((,)," w[19] <- "(,(,)),),((,((,),),,),)),(((((((,),((((,,,),(,(,))),(((,(,)),)," w[20] <- "(,))),)),),),),(,)),),),((,),))),((,),)),(((((((((((,),),((((((" w[21] <- ",),),((,),)),(,)),),)),(,)),),((((((,),),(((,),),)),(,)),),(,))" w[22] <- ",),),),),(,)),),((,),(,),,,)),(,(,(,)))),),(,)),),);" phy1 <- newick2phylog(w,FALSE) phy1 radial.phylog(phy1, clabel.l = 0, circle = 2.2, clea = 0.5, cnod = 0.5) data(newick.eg) radial.phylog(newick2phylog(newick.eg[[8]], FALSE), cnode = 1, clabel.l = 0.8) # hclust2phylog data(USArrests) hc <- hclust(dist(USArrests), "ave") par(mfrow = c(1,2)) plot(hc, hang = -1) phy <- hclust2phylog(hc) plot(phy, clabel.l = 0.75, clabel.n = 0.6, f = 0.75) par(mfrow = c(1,1)) row.names(USArrests) names(phy$leaves) #WARNING not the same for two reasons row.names(USArrests) <- gsub(" ","_",row.names(USArrests)) row.names(USArrests) names(phy$leaves) #WARNING not the same for one reason USArrests <- USArrests[names(phy$leaves),] row.names(USArrests) names(phy$leaves) #the same table.phylog(data.frame(scalewt(USArrests)), phy, csi = 2.5, clabel.r = 0.75, f = 0.7) #taxo2phylog data(taxo.eg) tax <- as.taxo(taxo.eg[[1]]) tax.phy <- taxo2phylog(as.taxo(taxo.eg[[1]])) par(mfrow = c(1,2)) plot(tax.phy, clabel.l = 1.25, clabel.n = 1.25, f = 0.75) plot(taxo2phylog(as.taxo(taxo.eg[[1]][sample(15),])), clabel.l = 1.25, clabel.n = 1.25, f = 0.75) par(mfrow=c(1,1)) plot(taxo2phylog(as.taxo(taxo.eg[[2]])), clabel.l = 1, clabel.n = 0.75, f = 0.65) ## End(Not run)
performs a special multivariate analysis for ecological data.
niche(dudiX, Y, scannf = TRUE, nf = 2) ## S3 method for class 'niche' print(x, ...) ## S3 method for class 'niche' plot(x, xax = 1, yax = 2, ...) niche.param(x) ## S3 method for class 'niche' rtest(xtest,nrepet=99, ...)
niche(dudiX, Y, scannf = TRUE, nf = 2) ## S3 method for class 'niche' print(x, ...) ## S3 method for class 'niche' plot(x, xax = 1, yax = 2, ...) niche.param(x) ## S3 method for class 'niche' rtest(xtest,nrepet=99, ...)
dudiX |
a duality diagram providing from a function |
Y |
a data frame sites-species according to |
scannf |
a logical value indicating whether the eigenvalues bar plot should be displayed |
nf |
if scannf FALSE, an integer indicating the number of kept axes |
x |
an object of class |
... |
further arguments passed to or from other methods |
xax , yax
|
the numbers of the x-axis and the y-axis |
xtest |
an object of class |
nrepet |
the number of permutations for the testing procedure |
Returns a list of the class niche
(sub-class of dudi
) containing :
rank |
an integer indicating the rank of the studied matrix |
nf |
an integer indicating the number of kept axes |
RV |
a numeric value indicating the RV coefficient |
eig |
a numeric vector with the all eigenvalues |
lw |
a data frame with the row weigths (crossed array) |
tab |
a data frame with the crossed array (averaging species/sites) |
li |
a data frame with the species coordinates |
l1 |
a data frame with the species normed scores |
co |
a data frame with the variable coordinates |
c1 |
a data frame with the variable normed scores |
ls |
a data frame with the site coordinates |
as |
a data frame with the axis upon niche axis |
Daniel Chessel
Anne-Béatrice Dufour [email protected]
Stéphane Dray [email protected]
Dolédec, S., Chessel, D. and Gimaret, C. (2000) Niche separation in community analysis: a new method. Ecology, 81, 2914–1927.
data(doubs) dudi1 <- dudi.pca(doubs$env, scale = TRUE, scan = FALSE, nf = 3) nic1 <- niche(dudi1, doubs$fish, scann = FALSE) if(adegraphicsLoaded()) { g1 <- s.traject(dudi1$li, plab.cex = 0, plot = FALSE) g2 <- s.traject(nic1$ls, plab.cex = 0, plot = FALSE) g3 <- s.corcircle(nic1$as, plot = FALSE) g4 <- s.arrow(nic1$c1, plot = FALSE) G1 <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) glist <- list() for(i in 1:ncol(doubs$fish)) glist[[i]] <- s.distri(nic1$ls, dfdistri = doubs$fish[, i], psub.text = names(doubs$fish)[i], plot = FALSE, storeData = TRUE) G2 <- ADEgS(glist, layout = c(5, 6)) G3 <- s.arrow(nic1$li, plab.cex = 0.7) } else { par(mfrow = c(2, 2)) s.traject(dudi1$li, clab = 0) s.traject(nic1$ls, clab = 0) s.corcircle(nic1$as) s.arrow(nic1$c1) par(mfrow = c(5, 6)) for (i in 1:27) s.distri(nic1$ls, as.data.frame(doubs$fish[,i]), csub = 2, sub = names(doubs$fish)[i]) par(mfrow = c(1, 1)) s.arrow(nic1$li, clab = 0.7) } data(trichometeo) pca1 <- dudi.pca(trichometeo$meteo, scan = FALSE) nic1 <- niche(pca1, log(trichometeo$fau + 1), scan = FALSE) plot(nic1) niche.param(nic1) rtest(nic1,19) data(rpjdl) plot(niche(dudi.pca(rpjdl$mil, scan = FALSE), rpjdl$fau, scan = FALSE))
data(doubs) dudi1 <- dudi.pca(doubs$env, scale = TRUE, scan = FALSE, nf = 3) nic1 <- niche(dudi1, doubs$fish, scann = FALSE) if(adegraphicsLoaded()) { g1 <- s.traject(dudi1$li, plab.cex = 0, plot = FALSE) g2 <- s.traject(nic1$ls, plab.cex = 0, plot = FALSE) g3 <- s.corcircle(nic1$as, plot = FALSE) g4 <- s.arrow(nic1$c1, plot = FALSE) G1 <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) glist <- list() for(i in 1:ncol(doubs$fish)) glist[[i]] <- s.distri(nic1$ls, dfdistri = doubs$fish[, i], psub.text = names(doubs$fish)[i], plot = FALSE, storeData = TRUE) G2 <- ADEgS(glist, layout = c(5, 6)) G3 <- s.arrow(nic1$li, plab.cex = 0.7) } else { par(mfrow = c(2, 2)) s.traject(dudi1$li, clab = 0) s.traject(nic1$ls, clab = 0) s.corcircle(nic1$as) s.arrow(nic1$c1) par(mfrow = c(5, 6)) for (i in 1:27) s.distri(nic1$ls, as.data.frame(doubs$fish[,i]), csub = 2, sub = names(doubs$fish)[i]) par(mfrow = c(1, 1)) s.arrow(nic1$li, clab = 0.7) } data(trichometeo) pca1 <- dudi.pca(trichometeo$meteo, scan = FALSE) nic1 <- niche(pca1, log(trichometeo$fau + 1), scan = FALSE) plot(nic1) niche.param(nic1) rtest(nic1,19) data(rpjdl) plot(niche(dudi.pca(rpjdl$mil, scan = FALSE), rpjdl$fau, scan = FALSE))
This function performs NIPALS algorithm, i.e. a principal component analysis of a data table that can contain missing values.
nipals(df, nf = 2, rec = FALSE, niter = 100, tol = 1e-09) ## S3 method for class 'nipals' scatter(x, xax = 1, yax = 2, clab.row = 0.75, clab.col = 1, posieig = "top", sub = NULL, ...) ## S3 method for class 'nipals' print(x, ...)
nipals(df, nf = 2, rec = FALSE, niter = 100, tol = 1e-09) ## S3 method for class 'nipals' scatter(x, xax = 1, yax = 2, clab.row = 0.75, clab.col = 1, posieig = "top", sub = NULL, ...) ## S3 method for class 'nipals' print(x, ...)
df |
a data frame that can contain missing values |
nf |
an integer, the number of axes to keep |
rec |
a logical that specify if the functions must perform the
reconstitution of the data using the |
niter |
an integer, the maximum number of iterations |
tol |
a real, the tolerance used in the iterative algorithm |
x |
an object of class |
xax |
the column number for the x-axis |
yax |
the column number for the y-axis |
clab.row |
a character size for the rows |
clab.col |
a character size for the columns |
posieig |
if "top" the eigenvalues bar plot is upside, if "bottom" it is downside, if "none" no plot |
sub |
a string of characters to be inserted as legend |
... |
further arguments passed to or from other methods |
Data are scaled (mean 0 and variance 1) prior to the analysis.
Returns a list of classes nipals
:
tab |
the scaled data frame |
eig |
the pseudoeigenvalues |
rank |
the rank of the analyzed matrice |
nf |
the number of factors |
c1 |
the column normed scores |
co |
the column coordinates |
li |
the row coordinates |
call |
the call function |
nb |
the number of iterations for each axis |
rec |
a data frame obtained by the reconstitution of the scaled
data using the |
Stéphane Dray [email protected]
Wold, H. (1966) Estimation of principal
components and related models by iterative least squares. In
P. Krishnaiah, editors.Multivariate
Analysis, Academic Press, 391–420.
Wold, S., Esbensen, K. and Geladi, P. (1987) Principal component analysis Chemometrics and Intelligent Laboratory Systems, 2, 37–52.
data(doubs) ## nipals is equivalent to dudi.pca when there are no NA acp1 <- dudi.pca(doubs$env, scannf = FALSE, nf = 2) nip1 <- nipals(doubs$env) if(adegraphicsLoaded()) { if(requireNamespace("lattice", quietly = TRUE)) { g1 <- s1d.barchart(acp1$eig, psub.text = "dudi.pca", p1d.horizontal = FALSE, plot = FALSE) g2 <- s1d.barchart(nip1$eig, psub.text = "nipals", p1d.horizontal = FALSE, plot = FALSE) g3 <- lattice::xyplot(nip1$c1[, 1] ~ acp1$c1[, 1], main = "col scores", xlab = "dudi.pca", ylab = "nipals") g4 <- lattice::xyplot(nip1$li[, 1] ~ acp1$li[, 1], main = "row scores", xlab = "dudi.pca", ylab = "nipals") G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } } else { par(mfrow = c(2, 2)) barplot(acp1$eig, main = "dudi.pca") barplot(nip1$eig, main = "nipals") plot(acp1$c1[, 1], nip1$c1[, 1], main = "col scores", xlab = "dudi.pca", ylab = "nipals") plot(acp1$li[, 1], nip1$li[, 1], main = "row scores", xlab = "dudi.pca", ylab = "nipals") } ## Not run: ## with NAs: doubs$env[1, 1] <- NA nip2 <- nipals(doubs$env) cor(nip1$li, nip2$li) nip1$eig nip2$eig ## End(Not run)
data(doubs) ## nipals is equivalent to dudi.pca when there are no NA acp1 <- dudi.pca(doubs$env, scannf = FALSE, nf = 2) nip1 <- nipals(doubs$env) if(adegraphicsLoaded()) { if(requireNamespace("lattice", quietly = TRUE)) { g1 <- s1d.barchart(acp1$eig, psub.text = "dudi.pca", p1d.horizontal = FALSE, plot = FALSE) g2 <- s1d.barchart(nip1$eig, psub.text = "nipals", p1d.horizontal = FALSE, plot = FALSE) g3 <- lattice::xyplot(nip1$c1[, 1] ~ acp1$c1[, 1], main = "col scores", xlab = "dudi.pca", ylab = "nipals") g4 <- lattice::xyplot(nip1$li[, 1] ~ acp1$li[, 1], main = "row scores", xlab = "dudi.pca", ylab = "nipals") G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } } else { par(mfrow = c(2, 2)) barplot(acp1$eig, main = "dudi.pca") barplot(nip1$eig, main = "nipals") plot(acp1$c1[, 1], nip1$c1[, 1], main = "col scores", xlab = "dudi.pca", ylab = "nipals") plot(acp1$li[, 1], nip1$li[, 1], main = "row scores", xlab = "dudi.pca", ylab = "nipals") } ## Not run: ## with NAs: doubs$env[1, 1] <- NA nip2 <- nipals(doubs$env) cor(nip1$li, nip2$li) nip1$eig nip2$eig ## End(Not run)
This data set describes the phylogeny of 36 bacteria as reported by Perrière and Gouy (1996). It also gives the GC rate corresponding to these 36 species.
data(njplot)
data(njplot)
njplot
is a list containing the 2 following objects:
is a character string giving the fission tree in Newick format.
is a numeric vector that gives the CG rate of the 36 species.
Data were obtained by Manolo Gouy [email protected]
Perrière, G. and Gouy, M. (1996) WWW-Query : an on-line retrieval system for biological sequence banks. Biochimie, 78, 364–369.
data(njplot) njplot.phy <- newick2phylog(njplot$tre) par(mfrow = c(2,1)) tauxcg0 <- njplot$tauxcg - mean(njplot$tauxcg) symbols.phylog(njplot.phy, squares = tauxcg0) symbols.phylog(njplot.phy, circles = tauxcg0) par(mfrow = c(1,1))
data(njplot) njplot.phy <- newick2phylog(njplot$tre) par(mfrow = c(2,1)) tauxcg0 <- njplot$tauxcg - mean(njplot$tauxcg) symbols.phylog(njplot.phy, squares = tauxcg0) symbols.phylog(njplot.phy, circles = tauxcg0) par(mfrow = c(1,1))
This data set gives the performances of 33 men's decathlon at the Olympic Games (1988).
data(olympic)
data(olympic)
olympic
is a list of 2 components.
is a data frame with 33 rows and 10 columns events of the decathlon: 100 meters (100), long jump (long), shotput (poid), high jump (haut), 400 meters (400), 110-meter hurdles (110), discus throw (disq), pole vault (perc), javelin (jave) and 1500 meters (1500).
is a vector of the final points scores of the competition.
Example 357 in:
Hand, D.J., Daly, F., Lunn, A.D., McConway, K.J. and Ostrowski, E. (1994)
A handbook of small data sets, Chapman & Hall, London. 458 p.
Lunn, A. D. and McNeil, D.R. (1991) Computer-Interactive Data Analysis, Wiley, New York
data(olympic) pca1 <- dudi.pca(olympic$tab, scan = FALSE) if(adegraphicsLoaded()) { if(requireNamespace("lattice", quietly = TRUE)) { g1 <- s1d.barchart(pca1$eig, p1d.hori = FALSE, plot = FALSE) g2 <- s.corcircle(pca1$co, plot = FALSE) g3 <- lattice::xyplot(pca1$l1[, 1] ~ olympic$score, type = c("p", "r")) g41 <- s.label(pca1$l1, plab.cex = 0.5, plot = FALSE) g42 <- s.arrow(2 * pca1$co, plot = FALSE) g4 <- superpose(g41, g42) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } } else { par(mfrow = c(2, 2)) barplot(pca1$eig) s.corcircle(pca1$co) plot(olympic$score, pca1$l1[, 1]) abline(lm(pca1$l1[, 1] ~ olympic$score)) s.label(pca1$l1, clab = 0.5) s.arrow(2 * pca1$co, add.p = TRUE) par(mfrow = c(1, 1)) }
data(olympic) pca1 <- dudi.pca(olympic$tab, scan = FALSE) if(adegraphicsLoaded()) { if(requireNamespace("lattice", quietly = TRUE)) { g1 <- s1d.barchart(pca1$eig, p1d.hori = FALSE, plot = FALSE) g2 <- s.corcircle(pca1$co, plot = FALSE) g3 <- lattice::xyplot(pca1$l1[, 1] ~ olympic$score, type = c("p", "r")) g41 <- s.label(pca1$l1, plab.cex = 0.5, plot = FALSE) g42 <- s.arrow(2 * pca1$co, plot = FALSE) g4 <- superpose(g41, g42) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } } else { par(mfrow = c(2, 2)) barplot(pca1$eig) s.corcircle(pca1$co) plot(olympic$score, pca1$l1[, 1]) abline(lm(pca1$l1[, 1] ~ olympic$score)) s.label(pca1$l1, clab = 0.5) s.arrow(2 * pca1$co, add.p = TRUE) par(mfrow = c(1, 1)) }
This data set contains informations about environmental control and spatial structure in ecological communities of Oribatid mites.
data(oribatid)
data(oribatid)
oribatid
is a list containing the following objects :
: a data frame with 70 rows (sites) and 35 columns (Oribatid species)
: a data frame with 70 rows (sites) and 5 columns (environmental variables)
: a data frame that contains spatial coordinates of the 70 sites
Variables of oribatid$envir
are the following ones :
substrate: a factor with seven levels that describes the nature of the substratum
shrubs: a factor with three levels that describes the absence/presence of shrubs
topo: a factor with two levels that describes the microtopography
density: substratum density ()
water: water content of the substratum ()
Data prepared by P. Legendre [email protected] and D. Borcard [email protected]
Borcard, D., and Legendre, P. (1994) Environmental control and spatial structure in ecological communities: an example using Oribatid mites (Acari Oribatei). Environmental and Ecological Statistics, 1, 37–61.
Borcard, D., Legendre, P., and Drapeau, P. (1992) Partialling out the spatial component of ecological variation. Ecology, 73, 1045–1055.
See a data description at http://pbil.univ-lyon1.fr/R/pdf/pps039.pdf (in French).
data(oribatid) ori.xy <- oribatid$xy[, c(2, 1)] names(ori.xy) <- c("x","y") plot(ori.xy,pch = 20, cex = 2, asp = 1) if(requireNamespace("deldir", quietly = TRUE) & requireNamespace("spdep", quietly = TRUE)) { plot(deldir::deldir(ori.xy), add = TRUE) if(adegraphicsLoaded()) { s.label(ori.xy, nb = spdep::knn2nb(spdep::knearneigh(as.matrix(ori.xy), 3)), plab.cex = 0) } else { s.label(ori.xy, add.p = TRUE, clab = 0, neig = nb2neig(spdep::knn2nb(spdep::knearneigh(as.matrix(ori.xy), 3)))) } }
data(oribatid) ori.xy <- oribatid$xy[, c(2, 1)] names(ori.xy) <- c("x","y") plot(ori.xy,pch = 20, cex = 2, asp = 1) if(requireNamespace("deldir", quietly = TRUE) & requireNamespace("spdep", quietly = TRUE)) { plot(deldir::deldir(ori.xy), add = TRUE) if(adegraphicsLoaded()) { s.label(ori.xy, nb = spdep::knn2nb(spdep::knearneigh(as.matrix(ori.xy), 3)), plab.cex = 0) } else { s.label(ori.xy, add.p = TRUE, clab = 0, neig = nb2neig(spdep::knn2nb(spdep::knearneigh(as.matrix(ori.xy), 3)))) } }
computes originality values for species from an ultrametric phylogenetic tree.
originality(phyl, method = 5)
originality(phyl, method = 5)
phyl |
an object of class phylog |
method |
a vector containing integers between 1 and 7. |
1 = Vane-Wright et al.'s (1991) node-counting index 2 = May's (1990) branch-counting index 3 = Nixon and Wheeler's (1991) unweighted index, based on the sum of units in binary values 4 = Nixon and Wheeler's (1991) weighted index 5 = QE-based index 6 = Isaac et al. (2007) ED index 7 = Redding et al. (2006) Equal-split index
Returns a data frame with species in rows, and the selected indices of originality in columns. Indices are expressed as percentages.
Sandrine Pavoine [email protected]
Isaac, N.J.B., Turvey, S.T., Collen, B., Waterman, C. and Baillie, J.E.M. (2007) Mammals on the EDGE: conservation priorities based on threat and phylogeny. PloS ONE, 2, e–296.
Redding, D. and Mooers, A. (2006) Incorporating evolutionary measures into conservation prioritization. Conservation Biology, 20, 1670–1678.
Pavoine, S., Ollier, S. and Dufour, A.-B. (2005) Is the originality of a species measurable? Ecology Letters, 8, 579–586.
Vane-Wright, R.I., Humphries, C.J. and Williams, P.H. (1991). What to protect? Systematics and the agony of choice. Biological Conservation, 55, 235–254.
May, R.M. (1990). Taxonomy as destiny. Nature, 347, 129–130.
Nixon, K.C. and Wheeler, Q.D. (1992). Measures of phylogenetic diversity. In: Extinction and Phylogeny (eds. Novacek, M.J. and Wheeler, Q.D.), 216–234, Columbia University Press, New York.
data(carni70) carni70.phy <- newick2phylog(carni70$tre) ori.tab <- originality(carni70.phy, 1:7) names(ori.tab) dotchart.phylog(carni70.phy, ori.tab, scaling = FALSE, yjoining = 0, ranging = FALSE, cleaves = 0, ceti = 0.5, csub = 0.7, cdot = 0.5)
data(carni70) carni70.phy <- newick2phylog(carni70$tre) ori.tab <- originality(carni70.phy, 1:7) names(ori.tab) dotchart.phylog(carni70.phy, ori.tab, scaling = FALSE, yjoining = 0, ranging = FALSE, cleaves = 0, ceti = 0.5, csub = 0.7, cdot = 0.5)
These functions returns object of class 'orthobasis'
that
contains data frame defining an orthonormal basis.
orthobasic.neig
returns the eigen vectors of the matrix N-M where M is the symmetric n by n matrix of the between-sites neighbouring graph and N is the diagonal matrix of neighbour numbers. orthobasis.line
returns the analytical solution for the linear neighbouring graph. orthobasic.circ
returns the analytical solution for the circular neighbouring graph. orthobsic.mat
returns the eigen vectors of the general link matrix M. orthobasis.haar
returns wavelet haar basis.
orthobasis.neig(neig) orthobasis.line(n) orthobasis.circ(n) orthobasis.mat(mat, cnw=TRUE) orthobasis.haar(n) ## S3 method for class 'orthobasis' print(x,..., nr = 6, nc = 4) ## S3 method for class 'orthobasis' plot(x,...) ## S3 method for class 'orthobasis' summary(object,...) is.orthobasis(x)
orthobasis.neig(neig) orthobasis.line(n) orthobasis.circ(n) orthobasis.mat(mat, cnw=TRUE) orthobasis.haar(n) ## S3 method for class 'orthobasis' print(x,..., nr = 6, nc = 4) ## S3 method for class 'orthobasis' plot(x,...) ## S3 method for class 'orthobasis' summary(object,...) is.orthobasis(x)
neig |
is an object of class |
n |
is an integer that defines length of vectors |
mat |
is a n by n phylogenetic or spatial link matrix |
cnw |
if TRUE, the matrix of the neighbouring graph is modified to give Constant Neighbouring Weights |
x , object
|
is an object of class |
nr , nc
|
the number of rows and columns to be printed |
... |
: further arguments passed to or from other methods |
All the functions return an object of class orthobasis
containing a data frame.
This data frame defines an orthonormal basis with various attributes:
names |
names of the vectors |
row.names |
row names of the data frame |
class |
class |
values |
optional associated eigenvalues |
weights |
weights for the rows |
call |
: call |
the function orthobasis.haar
uses function wavelet.filter
from package waveslim.
Sébastien Ollier [email protected]
Daniel Chessel
Misiti, M., Misiti, Y., Oppenheim, G. and Poggi, J.M. (1993) Analyse de signaux classiques par décomposition en ondelettes. Revue de Statistique Appliquée, 41, 5–32.
Cornillon, P.A. (1998) Prise en compte de proximités en analyse factorielle et comparative. Thèse, Ecole Nationale Supérieure Agronomique, Montpellier.
gridrowcol
that defines an orthobasis for square grid,
phylog
that defines an orthobasis for phylogenetic tree,
orthogram
and mld
# a 2D spatial orthobasis w <- gridrowcol(8, 8) if(adegraphicsLoaded()) { g1 <- s.value(w$xy, w$orthobasis[, 1:16], pleg.drawKey = FALSE, pgri.text.cex = 0, ylim = c(0, 10), porigin.include = FALSE, paxes.draw = FALSE) g2 <- s1d.barchart(attr(w$orthobasis, "values"), p1d.horizontal = FALSE, labels = names(attr(w$orthobasis, "values")), plabels.cex = 0.7) } else { par(mfrow = c(4, 4)) for(k in 1:16) s.value(w$xy, w$orthobasis[, k], cleg = 0, csi = 2, incl = FALSE, addax = FALSE, sub = k, csub = 4, ylim = c(0, 10), cgri = 0) par(mfrow = c(1, 1)) barplot(attr(w$orthobasis, "values")) } # Haar 1D orthobasis w <- orthobasis.haar(32) par(mfrow = c(8, 4)) par(mar = c(0.1, 0.1, 0.1, 0.1)) for (k in 1:31) { plot(w[, k], type = "S", xlab = "", ylab = "", xaxt = "n", yaxt = "n", xaxs = "i", yaxs = "i", ylim = c(-4.5, 4.5)) points(w[, k], type = "p", pch = 20, cex = 1.5) } # a 1D orthobasis w <- orthobasis.line(n = 33) par(mfrow = c(8, 4)) par(mar = c(0.1, 0.1, 0.1, 0.1)) for (k in 1:32) { plot(w[, k], type = "l", xlab = "", ylab = "", xaxt = "n", yaxt = "n", xaxs = "i", yaxs = "i", ylim = c(-1.5, 1.5)) points(w[, k], type = "p", pch = 20, cex = 1.5) } if(adegraphicsLoaded()) { s1d.barchart(attr(w, "values"), p1d.horizontal = FALSE, labels = names(attr(w, "values")), plab.cex = 0.7) } else { par(mfrow = c(1, 1)) barplot(attr(w, "values")) } w <- orthobasis.circ(n = 26) #par(mfrow = c(5, 5)) #par(mar = c(0.1, 0.1, 0.1, 0.1)) # for (k in 1:25) # dotcircle(w[, k], xlim = c(-1.5, 1.5), cleg = 0) par(mfrow = c(1, 1)) #barplot(attr(w, "values")) ## Not run: # a spatial orthobasis data(mafragh) w <- orthobasis.neig(neig2nb(mafragh$nb)) if(adegraphicsLoaded()) { s.value(mafragh$xy, w[, 1:8], plegend.drawKey = FALSE) s1d.barchart(attr(w, "values"), p1d.horizontal = FALSE) } else { par(mfrow = c(4, 2)) for(k in 1:8) s.value(mafragh$xy, w[, k], cleg = 0, sub = as.character(k), csub = 3) par(mfrow = c(1, 1)) barplot(attr(w, "values")) } # a phylogenetic orthobasis data(njplot) phy <- newick2phylog(njplot$tre) wA <- phy$Ascores wW <- phy$Wscores table.phylog(phylog = phy, wA, clabel.row = 0, clabel.col = 0.5) table.phylog(phylog = phy, wW, clabel.row = 0, clabel.col = 0.5) ## End(Not run)
# a 2D spatial orthobasis w <- gridrowcol(8, 8) if(adegraphicsLoaded()) { g1 <- s.value(w$xy, w$orthobasis[, 1:16], pleg.drawKey = FALSE, pgri.text.cex = 0, ylim = c(0, 10), porigin.include = FALSE, paxes.draw = FALSE) g2 <- s1d.barchart(attr(w$orthobasis, "values"), p1d.horizontal = FALSE, labels = names(attr(w$orthobasis, "values")), plabels.cex = 0.7) } else { par(mfrow = c(4, 4)) for(k in 1:16) s.value(w$xy, w$orthobasis[, k], cleg = 0, csi = 2, incl = FALSE, addax = FALSE, sub = k, csub = 4, ylim = c(0, 10), cgri = 0) par(mfrow = c(1, 1)) barplot(attr(w$orthobasis, "values")) } # Haar 1D orthobasis w <- orthobasis.haar(32) par(mfrow = c(8, 4)) par(mar = c(0.1, 0.1, 0.1, 0.1)) for (k in 1:31) { plot(w[, k], type = "S", xlab = "", ylab = "", xaxt = "n", yaxt = "n", xaxs = "i", yaxs = "i", ylim = c(-4.5, 4.5)) points(w[, k], type = "p", pch = 20, cex = 1.5) } # a 1D orthobasis w <- orthobasis.line(n = 33) par(mfrow = c(8, 4)) par(mar = c(0.1, 0.1, 0.1, 0.1)) for (k in 1:32) { plot(w[, k], type = "l", xlab = "", ylab = "", xaxt = "n", yaxt = "n", xaxs = "i", yaxs = "i", ylim = c(-1.5, 1.5)) points(w[, k], type = "p", pch = 20, cex = 1.5) } if(adegraphicsLoaded()) { s1d.barchart(attr(w, "values"), p1d.horizontal = FALSE, labels = names(attr(w, "values")), plab.cex = 0.7) } else { par(mfrow = c(1, 1)) barplot(attr(w, "values")) } w <- orthobasis.circ(n = 26) #par(mfrow = c(5, 5)) #par(mar = c(0.1, 0.1, 0.1, 0.1)) # for (k in 1:25) # dotcircle(w[, k], xlim = c(-1.5, 1.5), cleg = 0) par(mfrow = c(1, 1)) #barplot(attr(w, "values")) ## Not run: # a spatial orthobasis data(mafragh) w <- orthobasis.neig(neig2nb(mafragh$nb)) if(adegraphicsLoaded()) { s.value(mafragh$xy, w[, 1:8], plegend.drawKey = FALSE) s1d.barchart(attr(w, "values"), p1d.horizontal = FALSE) } else { par(mfrow = c(4, 2)) for(k in 1:8) s.value(mafragh$xy, w[, k], cleg = 0, sub = as.character(k), csub = 3) par(mfrow = c(1, 1)) barplot(attr(w, "values")) } # a phylogenetic orthobasis data(njplot) phy <- newick2phylog(njplot$tre) wA <- phy$Ascores wW <- phy$Wscores table.phylog(phylog = phy, wA, clabel.row = 0, clabel.col = 0.5) table.phylog(phylog = phy, wW, clabel.row = 0, clabel.col = 0.5) ## End(Not run)
The ours
(bears) data frame has 38 rows, areas of the "Inventaire National Forestier", and 10 columns.
data(ours)
data(ours)
This data frame contains the following columns:
altit: importance of the altitudinal area inhabited by bears, a factor with levels:
1
less than 50% of the area between 800 and 2000 meters
2
between 50 and 70%
3
more than 70%
deniv: importance of the average variation in level by square of 50 km2, a factor with levels:
1
less than 700m
2
between 700 and 900 m
3
more than 900 m
cloiso: partitioning of the massif, a factor with levels:
1
a great valley or a ridge isolates at least a quarter of the massif
2
less than a quarter of the massif is isolated
3
the massif has no split
domain: importance of the national forests on contact with the massif, a factor with levels:
1
less than 400 km2
2
between 400 and 1000 km2
3
more than 1000 km2
boise: rate of afforestation, a factor with levels:
1
less than 30%
2
between 30 and 50%
3
more than 50%
hetra: importance of plantations and mixed forests, a factor with levels:
1
less than 5%
2
between 5 and 10%
3
more than 10% of the massif
favor: importance of favorable forests, plantations, mixed forests, fir plantations, a factor with levels:
1
less than 5%
2
between 5 and 10%
3
more than 10% of the massif
inexp: importance of unworked forests, a factor with levels:
1
less than 4%
2
between 4 and 8%
3
more than 8% of the total area
citat: presence of the bear before its disappearance, a factor with levels:
1
no quotation since 1840
2
1 to 3 quotations before 1900 and none after
3
4 quotations before 1900 and none after
4
at least 4 quotations before 1900 and at least 1 quotation between 1900 and 1940
depart: district, a factor with levels:
AHP
Alpes-de-Haute-Provence
AM
Alpes-Maritimes
D
Drôme
HP
Hautes-Alpes
HS
Haute-Savoie
I
Isère
S
Savoie
Erome, G. (1989) L'ours brun dans les Alpes françaises. Historique de sa disparition. Centre Ornithologique Rhône-Alpes, Villeurbanne. 120 p.
data(ours) if(adegraphicsLoaded()) { s1d.boxplot(dudi.acm(ours, scan = FALSE)$l1[, 1], ours) } else { boxplot(dudi.acm(ours, scan = FALSE)) }
data(ours) if(adegraphicsLoaded()) { s1d.boxplot(dudi.acm(ours, scan = FALSE)$l1[, 1], ours) } else { boxplot(dudi.acm(ours, scan = FALSE)) }
This data set describes the phylogeny of 66 amazonian palm trees. It also gives 7 traits corresponding to these 66 species.
data(palm)
data(palm)
palm
is a list containing the 2 following objects:
is a character string giving the phylogenetic tree in Newick format.
is a data frame with 66 species (rows) and 7 traits (columns).
Variables of palm$traits
are the following ones:
rord: specific richness with five ordered levels
h: height in meter (squared transform)
dqual: diameter at breast height in centimeter with five levels sout : subterranean
, d1(0, 5 cm)
, d2(5, 15 cm)
, d3(15, 30 cm)
and d4(30, 100 cm)
vfruit: fruit volume in (logged transform)
vgrain: seed volume in (logged transform)
aire: spatial distribution area ()
alti: maximum altitude in meter (logged transform)
This data set was obtained by Clémentine Gimaret-Carpentier.
## Not run: data(palm) palm.phy <- newick2phylog(palm$tre) radial.phylog(palm.phy,clabel.l=1.25) if (requireNamespace("adephylo", quietly = TRUE) & requireNamespace("ape", quietly = TRUE)) { tre <- ape::read.tree(text = palm$tre) adephylo::orthogram(palm$traits[, 4], tre) } dotchart.phylog(palm.phy,palm$traits[,4], clabel.l = 1, labels.n = palm.phy$Blabels, clabel.n = 0.75) w <- cbind.data.frame(palm.phy$Bscores[,c(3,4,6,13,21)], scalewt((palm$traits[,4]))) names(w)[6] <- names(palm$traits[4]) table.phylog(w, palm.phy, clabel.r = 0.75, f = 0.5) gearymoran(palm.phy$Amat, palm$traits[,-c(1,3)]) ## End(Not run)
## Not run: data(palm) palm.phy <- newick2phylog(palm$tre) radial.phylog(palm.phy,clabel.l=1.25) if (requireNamespace("adephylo", quietly = TRUE) & requireNamespace("ape", quietly = TRUE)) { tre <- ape::read.tree(text = palm$tre) adephylo::orthogram(palm$traits[, 4], tre) } dotchart.phylog(palm.phy,palm$traits[,4], clabel.l = 1, labels.n = palm.phy$Blabels, clabel.n = 0.75) w <- cbind.data.frame(palm.phy$Bscores[,c(3,4,6,13,21)], scalewt((palm$traits[,4]))) names(w)[6] <- names(palm$traits[4]) table.phylog(w, palm.phy, clabel.r = 0.75, f = 0.5) gearymoran(palm.phy$Amat, palm$traits[,-c(1,3)]) ## End(Not run)
This data set describes the taxonomy of 39 carnivora. It also gives life-history traits corresponding to these 39 species.
data(pap)
data(pap)
pap
is a list containing the 2 following objects :
is a data frame with 39 species and 3 columns.
is a data frame with 39 species and 4 traits.
Variables of pap$tab
are the following ones : genre (genus with 30 levels),
famille (family with 6 levels), superfamille (superfamily with 2 levels).
Variables of pap$tab
are Group Size, Body Weight, Brain Weight, Litter Size.
Data taken from the phylogenetic autocorrelation package
data(pap) taxo <- taxo2phylog(as.taxo(pap$taxo)) table.phylog(as.data.frame(scalewt(pap$tab)), taxo, csi = 2, clabel.nod = 0.6, f.phylog = 0.6)
data(pap) taxo <- taxo2phylog(as.taxo(pap$taxo)) table.phylog(as.data.frame(scalewt(pap$tab)), taxo, csi = 2, clabel.nod = 0.6, f.phylog = 0.6)
performs a principal component analysis with respect to instrumental variables.
pcaiv(dudi, df, scannf = TRUE, nf = 2) ## S3 method for class 'pcaiv' plot(x, xax = 1, yax = 2, ...) ## S3 method for class 'pcaiv' print(x, ...) ## S3 method for class 'pcaiv' summary(object, ...)
pcaiv(dudi, df, scannf = TRUE, nf = 2) ## S3 method for class 'pcaiv' plot(x, xax = 1, yax = 2, ...) ## S3 method for class 'pcaiv' print(x, ...) ## S3 method for class 'pcaiv' summary(object, ...)
dudi |
a duality diagram, object of class |
df |
a data frame with the same rows |
scannf |
a logical value indicating whether the eigenvalues bar plot should be displayed |
nf |
if scannf FALSE, an integer indicating the number of kept axes |
x , object
|
an object of class |
xax |
the column number for the x-axis |
yax |
the column number for the y-axis |
... |
further arguments passed to or from other methods |
returns an object of class pcaiv
, sub-class of class dudi
tab |
a data frame with the modified array (projected variables) |
cw |
a numeric vector with the column weigths (from |
lw |
a numeric vector with the row weigths (from |
eig |
a vector with the all eigenvalues |
rank |
an integer indicating the rank of the studied matrix |
nf |
an integer indicating the number of kept axes |
c1 |
a data frame with the Pseudo Principal Axes (PPA) |
li |
a data frame |
co |
a data frame with the inner products between the CPC and Y |
l1 |
data frame with the Constraint Principal Components (CPC) |
call |
the matched call |
X |
a data frame with the explanatory variables |
Y |
a data frame with the dependant variables |
ls |
a data frame with the projections of lines of |
param |
a table containing information about contributions of the analyses : absolute (1) and cumulative (2) contributions of the decomposition of inertia of the dudi object, absolute (3) and cumulative (4) variances of the projections, the ration (5) between the cumulative variances of the projections (4) and the cumulative contributions (2), the square coefficient of correlation (6) and the eigenvalues of the pcaiv (7) |
as |
a data frame with the Principal axes of |
fa |
a data frame with the loadings (Constraint Principal Components as linear combinations of X |
cor |
a data frame with the correlations between the CPC and X |
Daniel Chessel
Anne-Béatrice Dufour [email protected]
Stéphane Dray [email protected]
Rao, C. R. (1964) The use and interpretation of principal component analysis in applied research. Sankhya, A 26, 329–359.
Obadia, J. (1978) L'analyse en composantes explicatives. Revue de Statistique Appliquee, 24, 5–28.
Lebreton, J. D., Sabatier, R., Banco G. and Bacou A. M. (1991)
Principal component and correspondence analyses with respect to instrumental variables :
an overview of their role in studies of structure-activity and species- environment relationships.
In J. Devillers and W. Karcher, editors. Applied Multivariate Analysis in SAR and Environmental Studies,
Kluwer Academic Publishers, 85–114.
Ter Braak, C. J. F. (1986) Canonical correspondence analysis : a new eigenvector technique for multivariate direct gradient analysis. Ecology, 67, 1167–1179.
Ter Braak, C. J. F. (1987) The analysis of vegetation-environment relationships by canonical correspondence analysis. Vegetatio, 69, 69–77.
Chessel, D., Lebreton J. D. and Yoccoz N. (1987) Propriétés de l'analyse canonique des correspondances. Une utilisation en hydrobiologie. Revue de Statistique Appliquée, 35, 55–72.
# example for the pcaiv data(rhone) pca1 <- dudi.pca(rhone$tab, scan = FALSE, nf = 3) iv1 <- pcaiv(pca1, rhone$disch, scan = FALSE) summary(iv1) plot(iv1) # example for the caiv data(rpjdl) millog <- log(rpjdl$mil + 1) coa1 <- dudi.coa(rpjdl$fau, scann = FALSE) caiv1 <- pcaiv(coa1, millog, scan = FALSE) if(adegraphicsLoaded()) { G1 <- plot(caiv1) # analysis with c1 - as - li -ls # projections of inertia axes on PCAIV axes G2 <- s.corcircle(caiv1$as) # Species positions g31 <- s.label(caiv1$c1, xax = 2, yax = 1, plab.cex = 0.5, xlim = c(-4, 4), plot = FALSE) # Sites positions at the weighted mean of present species g32 <- s.label(caiv1$ls, xax = 2, yax = 1, plab.cex = 0, plot = FALSE) G3 <- superpose(g31, g32, plot = TRUE) # Prediction of the positions by regression on environmental variables G4 <- s.match(caiv1$ls, caiv1$li, xax = 2, yax = 1, plab.cex = 0.5) # analysis with fa - l1 - co -cor # canonical weights giving unit variance combinations G5 <- s.arrow(caiv1$fa) # sites position by environmental variables combinations # position of species by averaging g61 <- s.label(caiv1$l1, xax = 2, yax = 1, plab.cex = 0, ppoi.cex = 1.5, plot = FALSE) g62 <- s.label(caiv1$co, xax = 2, yax = 1, plot = FALSE) G6 <- superpose(g61, g62, plot = TRUE) G7 <- s.distri(caiv1$l1, rpjdl$fau, xax = 2, yax = 1, ellipseSize = 0, starSize = 0.33) # coherence between weights and correlations g81 <- s.corcircle(caiv1$cor, xax = 2, yax = 1, plot = FALSE) g82 <- s.arrow(caiv1$fa, xax = 2, yax = 1, plot = FALSE) G8 <- cbindADEg(g81, g82, plot = TRUE) } else { plot(caiv1) # analysis with c1 - as - li -ls # projections of inertia axes on PCAIV axes s.corcircle(caiv1$as) # Species positions s.label(caiv1$c1, 2, 1, clab = 0.5, xlim = c(-4, 4)) # Sites positions at the weighted mean of present species s.label(caiv1$ls, 2, 1, clab = 0, cpoi = 1, add.p = TRUE) # Prediction of the positions by regression on environmental variables s.match(caiv1$ls, caiv1$li, 2, 1, clab = 0.5) # analysis with fa - l1 - co -cor # canonical weights giving unit variance combinations s.arrow(caiv1$fa) # sites position by environmental variables combinations # position of species by averaging s.label(caiv1$l1, 2, 1, clab = 0, cpoi = 1.5) s.label(caiv1$co, 2, 1, add.plot = TRUE) s.distri(caiv1$l1, rpjdl$fau, 2, 1, cell = 0, csta = 0.33) s.label(caiv1$co, 2, 1, clab = 0.75, add.plot = TRUE) # coherence between weights and correlations par(mfrow = c(1, 2)) s.corcircle(caiv1$cor, 2, 1) s.arrow(caiv1$fa, 2, 1) par(mfrow = c(1, 1)) }
# example for the pcaiv data(rhone) pca1 <- dudi.pca(rhone$tab, scan = FALSE, nf = 3) iv1 <- pcaiv(pca1, rhone$disch, scan = FALSE) summary(iv1) plot(iv1) # example for the caiv data(rpjdl) millog <- log(rpjdl$mil + 1) coa1 <- dudi.coa(rpjdl$fau, scann = FALSE) caiv1 <- pcaiv(coa1, millog, scan = FALSE) if(adegraphicsLoaded()) { G1 <- plot(caiv1) # analysis with c1 - as - li -ls # projections of inertia axes on PCAIV axes G2 <- s.corcircle(caiv1$as) # Species positions g31 <- s.label(caiv1$c1, xax = 2, yax = 1, plab.cex = 0.5, xlim = c(-4, 4), plot = FALSE) # Sites positions at the weighted mean of present species g32 <- s.label(caiv1$ls, xax = 2, yax = 1, plab.cex = 0, plot = FALSE) G3 <- superpose(g31, g32, plot = TRUE) # Prediction of the positions by regression on environmental variables G4 <- s.match(caiv1$ls, caiv1$li, xax = 2, yax = 1, plab.cex = 0.5) # analysis with fa - l1 - co -cor # canonical weights giving unit variance combinations G5 <- s.arrow(caiv1$fa) # sites position by environmental variables combinations # position of species by averaging g61 <- s.label(caiv1$l1, xax = 2, yax = 1, plab.cex = 0, ppoi.cex = 1.5, plot = FALSE) g62 <- s.label(caiv1$co, xax = 2, yax = 1, plot = FALSE) G6 <- superpose(g61, g62, plot = TRUE) G7 <- s.distri(caiv1$l1, rpjdl$fau, xax = 2, yax = 1, ellipseSize = 0, starSize = 0.33) # coherence between weights and correlations g81 <- s.corcircle(caiv1$cor, xax = 2, yax = 1, plot = FALSE) g82 <- s.arrow(caiv1$fa, xax = 2, yax = 1, plot = FALSE) G8 <- cbindADEg(g81, g82, plot = TRUE) } else { plot(caiv1) # analysis with c1 - as - li -ls # projections of inertia axes on PCAIV axes s.corcircle(caiv1$as) # Species positions s.label(caiv1$c1, 2, 1, clab = 0.5, xlim = c(-4, 4)) # Sites positions at the weighted mean of present species s.label(caiv1$ls, 2, 1, clab = 0, cpoi = 1, add.p = TRUE) # Prediction of the positions by regression on environmental variables s.match(caiv1$ls, caiv1$li, 2, 1, clab = 0.5) # analysis with fa - l1 - co -cor # canonical weights giving unit variance combinations s.arrow(caiv1$fa) # sites position by environmental variables combinations # position of species by averaging s.label(caiv1$l1, 2, 1, clab = 0, cpoi = 1.5) s.label(caiv1$co, 2, 1, add.plot = TRUE) s.distri(caiv1$l1, rpjdl$fau, 2, 1, cell = 0, csta = 0.33) s.label(caiv1$co, 2, 1, clab = 0.75, add.plot = TRUE) # coherence between weights and correlations par(mfrow = c(1, 2)) s.corcircle(caiv1$cor, 2, 1) s.arrow(caiv1$fa, 2, 1) par(mfrow = c(1, 1)) }
performs a Principal Component Analysis with respect to orthogonal instrumental variables.
pcaivortho(dudi, df, scannf = TRUE, nf = 2) ## S3 method for class 'pcaivortho' summary(object, ...)
pcaivortho(dudi, df, scannf = TRUE, nf = 2) ## S3 method for class 'pcaivortho' summary(object, ...)
dudi |
a duality diagram, object of class |
df |
a data frame with the same rows |
scannf |
a logical value indicating whether the eigenvalues bar plot should be displayed |
nf |
if scannf FALSE, an integer indicating the number of kept axes |
object |
an object of class |
... |
further arguments passed to or from other methods |
an object of class 'pcaivortho' sub-class of class dudi
rank |
an integer indicating the rank of the studied matrix |
nf |
an integer indicating the number of kept axes |
eig |
a vector with the all eigenvalues |
lw |
a numeric vector with the row weigths (from |
cw |
a numeric vector with the column weigths (from |
Y |
a data frame with the dependant variables |
X |
a data frame with the explanatory variables |
tab |
a data frame with the modified array (projected variables) |
c1 |
a data frame with the Pseudo Principal Axes (PPA) |
as |
a data frame with the Principal axis of |
ls |
a data frame with the projection of lines of |
li |
a data frame |
l1 |
a data frame with the Constraint Principal Components (CPC) |
co |
a data frame with the inner product between the CPC and Y |
param |
a data frame containing a summary |
Daniel Chessel
Anne-Béatrice Dufour [email protected]
Stéphane Dray [email protected]
Rao, C. R. (1964) The use and interpretation of principal component analysis in applied research. Sankhya, A 26, 329–359.
Sabatier, R., Lebreton J. D. and Chessel D. (1989) Principal component analysis with instrumental variables as a tool for modelling composition data. In R. Coppi and S. Bolasco, editors. Multiway data analysis, Elsevier Science Publishers B.V., North-Holland, 341–352
## Not run: data(avimedi) cla <- avimedi$plan$reg:avimedi$plan$str # simple ordination coa1 <- dudi.coa(avimedi$fau, scan = FALSE, nf = 3) # within region w1 <- wca(coa1, avimedi$plan$reg, scan = FALSE) # no region the same result pcaivnonA <- pcaivortho(coa1, avimedi$plan$reg, scan = FALSE) summary(pcaivnonA) # region + strate interAplusB <- pcaiv(coa1, avimedi$plan, scan = FALSE) if(adegraphicsLoaded()) { g1 <- s.class(coa1$li, cla, psub.text = "Sans contrainte", plot = FALSE) g21 <- s.match(w1$li, w1$ls, plab.cex = 0, psub.text = "Intra Région", plot = FALSE) g22 <- s.class(w1$li, cla, plot = FALSE) g2 <- superpose(g21, g22) g31 <- s.match(pcaivnonA$li, pcaivnonA$ls, plab.cex = 0, psub.tex = "Contrainte Non A", plot = FALSE) g32 <- s.class(pcaivnonA$li, cla, plot = FALSE) g3 <- superpose(g31, g32) g41 <- s.match(interAplusB$li, interAplusB$ls, plab.cex = 0, psub.text = "Contrainte A + B", plot = FALSE) g42 <- s.class(interAplusB$li, cla, plot = FALSE) g4 <- superpose(g41, g42) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) s.class(coa1$li, cla, sub = "Sans contrainte") s.match(w1$li, w1$ls, clab = 0, sub = "Intra Région") s.class(w1$li, cla, add.plot = TRUE) s.match(pcaivnonA$li, pcaivnonA$ls, clab = 0, sub = "Contrainte Non A") s.class(pcaivnonA$li, cla, add.plot = TRUE) s.match(interAplusB$li, interAplusB$ls, clab = 0, sub = "Contrainte A + B") s.class(interAplusB$li, cla, add.plot = TRUE) par(mfrow = c(1,1)) } ## End(Not run)
## Not run: data(avimedi) cla <- avimedi$plan$reg:avimedi$plan$str # simple ordination coa1 <- dudi.coa(avimedi$fau, scan = FALSE, nf = 3) # within region w1 <- wca(coa1, avimedi$plan$reg, scan = FALSE) # no region the same result pcaivnonA <- pcaivortho(coa1, avimedi$plan$reg, scan = FALSE) summary(pcaivnonA) # region + strate interAplusB <- pcaiv(coa1, avimedi$plan, scan = FALSE) if(adegraphicsLoaded()) { g1 <- s.class(coa1$li, cla, psub.text = "Sans contrainte", plot = FALSE) g21 <- s.match(w1$li, w1$ls, plab.cex = 0, psub.text = "Intra Région", plot = FALSE) g22 <- s.class(w1$li, cla, plot = FALSE) g2 <- superpose(g21, g22) g31 <- s.match(pcaivnonA$li, pcaivnonA$ls, plab.cex = 0, psub.tex = "Contrainte Non A", plot = FALSE) g32 <- s.class(pcaivnonA$li, cla, plot = FALSE) g3 <- superpose(g31, g32) g41 <- s.match(interAplusB$li, interAplusB$ls, plab.cex = 0, psub.text = "Contrainte A + B", plot = FALSE) g42 <- s.class(interAplusB$li, cla, plot = FALSE) g4 <- superpose(g41, g42) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) s.class(coa1$li, cla, sub = "Sans contrainte") s.match(w1$li, w1$ls, clab = 0, sub = "Intra Région") s.class(w1$li, cla, add.plot = TRUE) s.match(pcaivnonA$li, pcaivnonA$ls, clab = 0, sub = "Contrainte Non A") s.class(pcaivnonA$li, cla, add.plot = TRUE) s.match(interAplusB$li, interAplusB$ls, clab = 0, sub = "Contrainte A + B") s.class(interAplusB$li, cla, add.plot = TRUE) par(mfrow = c(1,1)) } ## End(Not run)
performs a simplified analysis in principal coordinates,
using an object of class dist
.
pcoscaled(distmat, tol = 1e-07)
pcoscaled(distmat, tol = 1e-07)
distmat |
an object of class |
tol |
a tolerance threshold, an eigenvalue is considered as positive if it is larger than |
returns a data frame containing the Euclidean representation of the distance matrix with a total inertia equal to 1
Daniel Chessel
Gower, J. C. (1966) Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika, 53, 325–338.
a <- 1 / sqrt(3) - 0.2 w <- matrix(c(0,0.8,0.8,a,0.8,0,0.8,a, 0.8,0.8,0,a,a,a,a,0),4,4) w <- as.dist(w) w <- cailliez(w) w pcoscaled(w) dist(pcoscaled(w)) # w dist(pcoscaled(2 * w)) # the same sum(pcoscaled(w)^2) # unity
a <- 1 / sqrt(3) - 0.2 w <- matrix(c(0,0.8,0.8,a,0.8,0,0.8,a, 0.8,0.8,0,a,a,a,a,0),4,4) w <- as.dist(w) w <- cailliez(w) w pcoscaled(w) dist(pcoscaled(w)) # w dist(pcoscaled(2 * w)) # the same sum(pcoscaled(w)^2) # unity
Abundance of tropical trees, environmental variables and spatial coordinates for 50 sites. Data are available at doi:10.1126/science.1066854 but plots from Barro Colorado Island were removed.
data(pcw)
data(pcw)
A list with 5 components.
Distribution of the abundances of 778 species in 50 sites
Measurements of environmental variables for the 50 sites
Spatial coordinates for the sites (decimal degrees)
Spatial coordinates for the sites (UTM)
Map of the study area stored as a SpatialPolygons object
Condit, R., N. Pitman, E. G. Leigh, J. Chave, J. Terborgh, R. B. Foster, P. Núnez, S. Aguilar, R. Valencia, G. Villa, H. C. Muller-Landau, E. Losos, and S. P. Hubbell. (2002) Beta-diversity in tropical forest trees. Science, 295, 666-669.
Pyke, C. R., R. Condit, S. Aguilar, and S. Lao. (2001) Floristic composition across a climatic gradient in a neotropical lowland forest. Journal of Vegetation Science, 12, 553–566.
Dray, S., R. Pélissier, P. Couteron, M. J. Fortin, P. Legendre, P. R. Peres-Neto, E. Bellier, R. Bivand, F. G. Blanchet, M. De Caceres, A. B. Dufour, E. Heegaard, T. Jombart, F. Munoz, J. Oksanen, J. Thioulouse, and H. H. Wagner. (2012) Community ecology in the age of multivariate multiscale spatial analysis. Ecological Monographs, 82, 257–275.
if(adegraphicsLoaded()) { data(pcw) if(requireNamespace("spdep", quietly = TRUE)) { nb1 <- spdep::graph2nb(spdep::gabrielneigh(pcw$xy.utm), sym = TRUE) s.label(pcw$xy, nb = nb1, Sp = pcw$map) } }
if(adegraphicsLoaded()) { data(pcw) if(requireNamespace("spdep", quietly = TRUE)) { nb1 <- spdep::graph2nb(spdep::gabrielneigh(pcw$xy.utm), sym = TRUE) s.label(pcw$xy, nb = nb1, Sp = pcw$map) } }
This data set gives the amino acids of 904 proteins distributed in three classes.
data(perthi02)
data(perthi02)
perthi02
is a list of 2 components.
is a data frame 904 rows (proteins of 201 species) 20 columns (amino acids).
is a factor of 3 classes of protein
The levels of perthi02$cla
are cyto
(cytoplasmic proteins) memb
(integral membran proteins) peri
(periplasmic proteins)
Perriere, G. and Thioulouse, J. (2002) Use of Correspondence Discriminant Analysis to predict the subcellular location of bacterial proteins. Computer Methods and Programs in Biomedicine, 70, 2, 99–105.
data(perthi02) plot(discrimin.coa(perthi02$tab, perthi02$cla, scan = FALSE))
data(perthi02) plot(discrimin.coa(perthi02$tab, perthi02$cla, scan = FALSE))
Create and use objects of class phylog
. phylog.extract
returns objects of class phylog
. It extracts sub-trees from a tree. phylog.permut
returns objects of class phylog
. It creates the different representations compatible with tree topology.
## S3 method for class 'phylog' print(x, ...) phylog.extract(phylog, node, distance = TRUE) phylog.permut(phylog, list.nodes = NULL, distance = TRUE)
## S3 method for class 'phylog' print(x, ...) phylog.extract(phylog, node, distance = TRUE) phylog.permut(phylog, list.nodes = NULL, distance = TRUE)
x , phylog
|
: an object of class |
... |
: further arguments passed to or from other methods |
node |
: a string of characters giving a node name. The functions extracts the tree rooted at this node. |
distance |
: if TRUE, both functions retain branch lengths. If FALSE, they returns tree with arbitrary branch lengths (each branch length equals one) |
list.nodes |
: a list which elements are vectors of string of character corresponding to direct descendants of nodes. This list defines one representation compatible with tree topology among the set of possibilities. |
Returns a list of class phylog
:
tre |
: a character string of the phylogenetic tree in Newick format whithout branch length values |
leaves |
: a vector which names corresponds to leaves and values gives the distance between leaves and nodes closest to these leaves |
nodes |
: a vector which names corresponds to nodes and values gives the distance between nodes and nodes closest to these leaves |
parts |
: a list which elements gives the direct descendants of each nodes |
paths |
: a list which elements gives the path leading from the root to taxonomic units (leaves and nodes) |
droot |
: a vector which names corresponds to taxonomic units and values gives distance between taxonomic units and the root |
call |
: call |
Wmat |
: a phylogenetic link matrix, generally called the covariance matrix. Matrix values |
Wdist |
: a phylogenetic distance matrix of class |
Wvalues |
: a vector with the eigen values of Wmat |
Wscores |
: a data frame with eigen vectors of Wmat. This data frame defines an orthobasis that could be used to calculate the orthonormal decomposition of a biological trait on a tree. |
Amat |
: a phylogenetic link matrix stemed from Abouheif's test and defined in Ollier et al. (submited) |
Avalues |
: a vector with the eigen values of Amat |
Adim |
: number of positive eigen values |
Ascores |
: a data frame with eigen vectors of Amat. This data frame defines an orthobasis that could be used to calculate the orthonormal decomposition of a biological trait on a tree. |
Aparam |
: a data frame with attributes associated to nodes. |
Bindica |
: a data frame giving for some taxonomic units the partition of leaves that is associated to its |
Bscores |
: a data frame giving an orthobasis defined by Ollier et al. (submited) that could be used to calculate the orthonormal decomposition of a biological trait on a tree. |
Bvalues |
: a vector giving the degree of phylogenetic autocorrelation for each vectors of Bscores (Moran's form calculated with the matrix Wmat) |
Blabels |
: a vector giving for each nodes the name of the vector of Bscores that is associated to its |
Daniel Chessel
Sébastien Ollier [email protected]
Ollier, S., Couteron, P. and Chessel, D. (2006) Orthonormal transform to decompose the variance of a life-history trait across a phylogenetic tree. Biometrics Biometrics, 62, 2, 471–477.
marthans.tre <- NULL marthans.tre[1] <-"((((1:4,2:4)a:5,(3:7,4:7)b:2)c:2,5:11)d:2," marthans.tre[2] <- "((6:5,7:5)e:4,(8:4,9:4)f:5)g:4);" marthans.phylog <- newick2phylog(marthans.tre) marthans.phylog if(requireNamespace("ape", quietly = TRUE)) { marthans.phylo <- ape::read.tree(text = marthans.tre) marthans.phylo par(mfrow = c(1, 2)) plot(marthans.phylog, cnode = 3, f = 0.8, cle = 3) plot(marthans.phylo) par(mfrow = c(1, 1)) }
marthans.tre <- NULL marthans.tre[1] <-"((((1:4,2:4)a:5,(3:7,4:7)b:2)c:2,5:11)d:2," marthans.tre[2] <- "((6:5,7:5)e:4,(8:4,9:4)f:5)g:4);" marthans.phylog <- newick2phylog(marthans.tre) marthans.phylog if(requireNamespace("ape", quietly = TRUE)) { marthans.phylo <- ape::read.tree(text = marthans.tre) marthans.phylo par(mfrow = c(1, 2)) plot(marthans.phylog, cnode = 3, f = 0.8, cle = 3) plot(marthans.phylo) par(mfrow = c(1, 1)) }
This function ensures to transform a data set written for the Phylogenetic Independance package of Abouheif (1999) in a data set formatting for the functions of ade4.
PI2newick(x)
PI2newick(x)
x |
is a data frame that contains information on phylogeny topology and trait values |
Returns a list containing :
tre |
: a character string giving the phylogenetic tree in Newick format |
trait |
: a vector containing values of the trait |
Sébastien Ollier [email protected]
Daniel Chessel
Abouheif, E. (1999) A method for testing the assumption of phylogenetic independence in comparative data. Evolutionary Ecology Research, 1, 895–909.
x <- c(2.0266, 0.5832, 0.2460, 1.2963, 0.2460, 0.1565, -99.0000, -99.0000, 10.1000, -99.0000, 20.2000, 28.2000, -99.0000, 14.1000, 11.2000, -99.0000, 21.3000, 27.5000, 1.0000, 2.0000, -1.0000, 4.0000, -1.0000, -1.0000, 3.0000, -1.0000, -1.0000, 5.0000, -1.0000, -1.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000) x <- matrix(x, nrow = 6) x <- as.data.frame(x) res <- PI2newick(x) dotchart.phylog(newick2phylog(res$tre), res$trait)
x <- c(2.0266, 0.5832, 0.2460, 1.2963, 0.2460, 0.1565, -99.0000, -99.0000, 10.1000, -99.0000, 20.2000, 28.2000, -99.0000, 14.1000, 11.2000, -99.0000, 21.3000, 27.5000, 1.0000, 2.0000, -1.0000, 4.0000, -1.0000, -1.0000, 3.0000, -1.0000, -1.0000, 5.0000, -1.0000, -1.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000) x <- matrix(x, nrow = 6) x <- as.data.frame(x) res <- PI2newick(x) dotchart.phylog(newick2phylog(res$tre), res$trait)
Plant species cover, traits and environmental parameters recorded around livestock watering points in different habitats of central Namibian farmlands. See the Wesuls et al. (2012) paper for a full description of the data set.
data(piosphere)
data(piosphere)
piosphere
is a list of 4 components.
is a data frame containing plant species cover
is a data frame with plant traits
is a data frame with environmental variables
is a factor describing habitat/years for each site
Wesuls, D., Oldeland, J. and Dray, S. (2012) Disentangling plant trait responses to livestock grazing from spatio-temporal variation: the partial RLQ approach. Journal of Vegetation Science, 23, 98–113.
data(piosphere) names(piosphere) afcL <- dudi.coa(log(piosphere$veg + 1), scannf = FALSE) acpR <- dudi.pca(piosphere$env, scannf = FALSE, row.w = afcL$lw) acpQ <- dudi.hillsmith(piosphere$traits, scannf = FALSE, row.w = afcL$cw) rlq1 <- rlq(acpR, afcL, acpQ, scannf = FALSE) plot(rlq1)
data(piosphere) names(piosphere) afcL <- dudi.coa(log(piosphere$veg + 1), scannf = FALSE) acpR <- dudi.pca(piosphere$env, scannf = FALSE, row.w = afcL$lw) acpQ <- dudi.hillsmith(piosphere$traits, scannf = FALSE, row.w = afcL$cw) rlq1 <- rlq(acpR, afcL, acpQ, scannf = FALSE) plot(rlq1)
plot.phylog
draws phylogenetic trees as linear dendograms. radial.phylog
draws phylogenetic trees as circular dendograms. enum.phylog
enumerate all the possible representations for a phylogeny.
## S3 method for class 'phylog' plot(x, y = NULL, f.phylog = 0.5, cleaves = 1, cnodes = 0, labels.leaves = names(x$leaves), clabel.leaves = 1, labels.nodes = names(x$nodes), clabel.nodes = 0, sub = "", csub = 1.25, possub = "bottomleft", draw.box = FALSE, ...) radial.phylog(phylog, circle = 1, cleaves = 1, cnodes = 0, labels.leaves = names(phylog$leaves), clabel.leaves = 1, labels.nodes = names(phylog$nodes), clabel.nodes = 0, draw.box = FALSE) enum.phylog(phylog, no.over = 1000)
## S3 method for class 'phylog' plot(x, y = NULL, f.phylog = 0.5, cleaves = 1, cnodes = 0, labels.leaves = names(x$leaves), clabel.leaves = 1, labels.nodes = names(x$nodes), clabel.nodes = 0, sub = "", csub = 1.25, possub = "bottomleft", draw.box = FALSE, ...) radial.phylog(phylog, circle = 1, cleaves = 1, cnodes = 0, labels.leaves = names(phylog$leaves), clabel.leaves = 1, labels.nodes = names(phylog$nodes), clabel.nodes = 0, draw.box = FALSE) enum.phylog(phylog, no.over = 1000)
x , phylog
|
an object of class |
y |
a vector which values correspond to leaves positions |
f.phylog |
a size coefficient for tree size (a parameter to draw the tree in proportion to leaves label) |
circle |
a size coefficient for the outer circle |
cleaves |
a character size for plotting the points that represent the leaves, used with |
cnodes |
a character size for plotting the points that represent the nodes, used with |
labels.leaves |
a vector of strings of characters for the leaves labels |
clabel.leaves |
a character size for the leaves labels, used with |
labels.nodes |
a vector of strings of characters for the nodes labels |
clabel.nodes |
a character size for the nodes labels, used with |
sub |
a string of characters to be inserted as legend |
csub |
a character size for the legend, used with |
possub |
a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", "bottomright") |
draw.box |
if TRUE draws a box around the current plot with the function |
... |
further arguments passed to or from other methods |
no.over |
a size coefficient for the number of representations |
The vector y is an argument of the function plot.phylog
that ensures to plot one of the possible representations of a phylogeny.
The vector y is a permutation of the set of leaves {1,2,...,f} compatible with the phylogeny's topology.
The function enum.phylog
returns a matrix with as many columns as leaves. Each row gives a permutation of the set of leaves {1,2,...,f} compatible with the phylogeny's topology.
Daniel Chessel
Sébastien Ollier [email protected]
data(newick.eg) par(mfrow = c(3,2)) for(i in 1:6) plot(newick2phylog(newick.eg[[i]], FALSE), clea = 2, clabel.l = 3, cnod = 2.5) par(mfrow = c(1,1)) ## Not run: par(mfrow = c(1,2)) plot(newick2phylog(newick.eg[[11]], FALSE), clea = 1.5, clabel.l = 1.5, clabel.nod = 0.75, f = 0.8) plot(newick2phylog(newick.eg[[10]], FALSE), clabel.l = 0, clea = 0, cn = 0, f = 1) par(mfrow = c(1,1)) ## End(Not run) par(mfrow = c(2,2)) w7 <- newick2phylog("(((((1,2,3)b),(6)c),(4,5)d,7)f);") plot(w7,clabel.l = 1.5, clabel.n = 1.5, f = 0.8, cle = 2, cnod = 3, sub = "(((((1,2,3)b),(6)c),(4,5)d,7)f);", csub = 2) w <- NULL w[1] <- "((((e1:4,e2:4)a:5,(e3:7,e4:7)b:2)c:2,e5:11)d:2," w[2] <- "((e6:5,e7:5)e:4,(e8:4,e9:4)f:5)g:4);" plot(newick2phylog(w), f = 0.8, cnod = 2, cleav = 2, clabel.l = 2) data(taxo.eg) w <- taxo2phylog(as.taxo(taxo.eg[[1]])) plot(w, clabel.lea = 1.25, clabel.n = 1.25, sub = "Taxonomy", csub = 3, f = 0.8, possub = "topleft") provi.tre <- "(((a,b,c,d,e)A,(f,g,h)B)C)D;" provi.phy <- newick2phylog(provi.tre) plot(provi.phy, clabel.l = 2, clabel.n = 2, f = 0.8) par(mfrow = c(1,1)) ## Not run: par(mfrow = c(3,3)) for (j in 1:6) radial.phylog(newick2phylog(newick.eg[[j]], FALSE), clabel.l = 2, cnodes = 2) radial.phylog(newick2phylog(newick.eg[[7]],FALSE), clabel.l = 2) radial.phylog(newick2phylog(newick.eg[[8]],FALSE), clabel.l = 0, circle = 1.8) radial.phylog(newick2phylog(newick.eg[[9]],FALSE), clabel.l = 1, clabel.n = 1, cle = 0, cnode = 1) par(mfrow = c(1,1)) data(bsetal97) bsetal.phy = taxo2phylog(as.taxo(bsetal97$taxo[,1:3]), FALSE) radial.phylog(bsetal.phy, cnod = 1, clea = 1, clabel.l = 0.75, draw.box = TRUE, cir = 1.1) par(mfrow = c(1,1)) ## End(Not run) ## Not run: # plot all the possible representations of a phylogenetic tree a <- "((a,b)A,(c,d,(e,f)B)C)D;" wa <- newick2phylog(a) wx <- enum.phylog(wa) dim(wx) par(mfrow = c(6,8)) fun <- function(x) { w <-NULL lapply(x, function(y) w<<-paste(w,as.character(y),sep="")) plot(wa, x, clabel.n = 1.25, f = 0.75, clabel.l = 2, box = FALSE, cle = 1.5, sub = w, csub = 2) invisible()} apply(wx,1,fun) par(mfrow = c(1,1)) ## End(Not run)
data(newick.eg) par(mfrow = c(3,2)) for(i in 1:6) plot(newick2phylog(newick.eg[[i]], FALSE), clea = 2, clabel.l = 3, cnod = 2.5) par(mfrow = c(1,1)) ## Not run: par(mfrow = c(1,2)) plot(newick2phylog(newick.eg[[11]], FALSE), clea = 1.5, clabel.l = 1.5, clabel.nod = 0.75, f = 0.8) plot(newick2phylog(newick.eg[[10]], FALSE), clabel.l = 0, clea = 0, cn = 0, f = 1) par(mfrow = c(1,1)) ## End(Not run) par(mfrow = c(2,2)) w7 <- newick2phylog("(((((1,2,3)b),(6)c),(4,5)d,7)f);") plot(w7,clabel.l = 1.5, clabel.n = 1.5, f = 0.8, cle = 2, cnod = 3, sub = "(((((1,2,3)b),(6)c),(4,5)d,7)f);", csub = 2) w <- NULL w[1] <- "((((e1:4,e2:4)a:5,(e3:7,e4:7)b:2)c:2,e5:11)d:2," w[2] <- "((e6:5,e7:5)e:4,(e8:4,e9:4)f:5)g:4);" plot(newick2phylog(w), f = 0.8, cnod = 2, cleav = 2, clabel.l = 2) data(taxo.eg) w <- taxo2phylog(as.taxo(taxo.eg[[1]])) plot(w, clabel.lea = 1.25, clabel.n = 1.25, sub = "Taxonomy", csub = 3, f = 0.8, possub = "topleft") provi.tre <- "(((a,b,c,d,e)A,(f,g,h)B)C)D;" provi.phy <- newick2phylog(provi.tre) plot(provi.phy, clabel.l = 2, clabel.n = 2, f = 0.8) par(mfrow = c(1,1)) ## Not run: par(mfrow = c(3,3)) for (j in 1:6) radial.phylog(newick2phylog(newick.eg[[j]], FALSE), clabel.l = 2, cnodes = 2) radial.phylog(newick2phylog(newick.eg[[7]],FALSE), clabel.l = 2) radial.phylog(newick2phylog(newick.eg[[8]],FALSE), clabel.l = 0, circle = 1.8) radial.phylog(newick2phylog(newick.eg[[9]],FALSE), clabel.l = 1, clabel.n = 1, cle = 0, cnode = 1) par(mfrow = c(1,1)) data(bsetal97) bsetal.phy = taxo2phylog(as.taxo(bsetal97$taxo[,1:3]), FALSE) radial.phylog(bsetal.phy, cnod = 1, clea = 1, clabel.l = 0.75, draw.box = TRUE, cir = 1.1) par(mfrow = c(1,1)) ## End(Not run) ## Not run: # plot all the possible representations of a phylogenetic tree a <- "((a,b)A,(c,d,(e,f)B)C)D;" wa <- newick2phylog(a) wx <- enum.phylog(wa) dim(wx) par(mfrow = c(6,8)) fun <- function(x) { w <-NULL lapply(x, function(y) w<<-paste(w,as.character(y),sep="")) plot(wa, x, clabel.n = 1.25, f = 0.75, clabel.l = 2, box = FALSE, cle = 1.5, sub = w, csub = 2) invisible()} apply(wx,1,fun) par(mfrow = c(1,1)) ## End(Not run)
presid2002
is a list of two data frames tour1
and tour2
with 93 rows (93 departments from continental Metropolitan France) and,
4 and 12 variables respectively .
data(presid2002)
data(presid2002)
tour1
contains the following arguments:
the number of registered voters (inscrits
); the number of abstentions (abstentions
);
the number of voters (votants
); the number of expressed votes (exprimes
) and,
the numbers of votes for each candidate: Megret
, Lepage
, Gluksten
, Bayrou
,
Chirac
, Le_Pen
, Taubira
, Saint.josse
, Mamere
, Jospin
, Boutin
,
Hue
, Chevenement
, Madelin
, Besancenot
.tour2
contains the following arguments:
the number of registered voters (inscrits
); the number of abstentions (abstentions
);
the number of voters (votants
); the number of expressed votes (exprimes
) and,
the numbers of votes for each candidate: Chirac
and Le_Pen
.
Site of the ministry of the Interior, of the Internal Security and of the local liberties
https://www.interieur.gouv.fr/Elections/Les-resultats/Presidentielles/elecresult__presidentielle_2002/(path)/presidentielle_2002/index.html
This dataset is compatible with elec88
and cnc2003
data(presid2002) all((presid2002$tour2$Chirac + presid2002$tour2$Le_Pen) == presid2002$tour2$exprimes) ## Not run: data(elec88) data(cnc2003) w0 <- ade4:::area.util.class(elec88$area, cnc2003$reg) w1 <- scale(elec88$tab$Chirac) w2 <- scale(presid2002$tour1$Chirac / presid2002$tour1$exprimes) w3 <- scale(elec88$tab$Mitterand) w4 <- scale(presid2002$tour2$Chirac / presid2002$tour2$exprimes) if(adegraphicsLoaded()) { g1 <- s.value(elec88$xy, w1, Sp = elec88$Spatial, pSp.col = "white", pgrid.draw = FALSE, psub.text = "Chirac 1988 T1", plot = FALSE) g2 <- s.value(elec88$xy, w2, Sp = elec88$Spatial, pSp.col = "white", pgrid.draw = FALSE, psub.text = "Chirac 2002 T1", plot = FALSE) g3 <- s.value(elec88$xy, w3, Sp = elec88$Spatial, pSp.col = "white", pgrid.draw = FALSE, psub.text = "Mitterand 1988 T1", plot = FALSE) g4 <- s.value(elec88$xy, w4, Sp = elec88$Spatial, pSp.col = "white", pgrid.draw = FALSE, psub.text = "Chirac 2002 T2", plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) par(mar = c(0.1, 0.1, 0.1, 0.1)) area.plot(w0) s.value(elec88$xy, w1, add.plot = TRUE) scatterutil.sub("Chirac 1988 T1", csub = 2, "topleft") area.plot(w0) s.value(elec88$xy, w2, add.plot = TRUE) scatterutil.sub("Chirac 2002 T1", csub = 2, "topleft") area.plot(w0) s.value(elec88$xy, w3, add.plot = TRUE) scatterutil.sub("Mitterand 1988 T1", csub = 2, "topleft") area.plot(w0) s.value(elec88$xy, w4, add.plot = TRUE) scatterutil.sub("Chirac 2002 T2", csub = 2, "topleft") } ## End(Not run)
data(presid2002) all((presid2002$tour2$Chirac + presid2002$tour2$Le_Pen) == presid2002$tour2$exprimes) ## Not run: data(elec88) data(cnc2003) w0 <- ade4:::area.util.class(elec88$area, cnc2003$reg) w1 <- scale(elec88$tab$Chirac) w2 <- scale(presid2002$tour1$Chirac / presid2002$tour1$exprimes) w3 <- scale(elec88$tab$Mitterand) w4 <- scale(presid2002$tour2$Chirac / presid2002$tour2$exprimes) if(adegraphicsLoaded()) { g1 <- s.value(elec88$xy, w1, Sp = elec88$Spatial, pSp.col = "white", pgrid.draw = FALSE, psub.text = "Chirac 1988 T1", plot = FALSE) g2 <- s.value(elec88$xy, w2, Sp = elec88$Spatial, pSp.col = "white", pgrid.draw = FALSE, psub.text = "Chirac 2002 T1", plot = FALSE) g3 <- s.value(elec88$xy, w3, Sp = elec88$Spatial, pSp.col = "white", pgrid.draw = FALSE, psub.text = "Mitterand 1988 T1", plot = FALSE) g4 <- s.value(elec88$xy, w4, Sp = elec88$Spatial, pSp.col = "white", pgrid.draw = FALSE, psub.text = "Chirac 2002 T2", plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) par(mar = c(0.1, 0.1, 0.1, 0.1)) area.plot(w0) s.value(elec88$xy, w1, add.plot = TRUE) scatterutil.sub("Chirac 1988 T1", csub = 2, "topleft") area.plot(w0) s.value(elec88$xy, w2, add.plot = TRUE) scatterutil.sub("Chirac 2002 T1", csub = 2, "topleft") area.plot(w0) s.value(elec88$xy, w3, add.plot = TRUE) scatterutil.sub("Mitterand 1988 T1", csub = 2, "topleft") area.plot(w0) s.value(elec88$xy, w4, add.plot = TRUE) scatterutil.sub("Chirac 2002 T2", csub = 2, "topleft") } ## End(Not run)
This data set describes the phylogeny of 19 birds as reported by Bried et al. (2002). It also gives 6 traits corresponding to these 19 species.
data(procella)
data(procella)
procella
is a list containing the 2 following objects:
is a character string giving the phylogenetic tree in Newick format.
is a data frame with 19 species and 6 traits
Variables of procella$traits
are the following ones:
site.fid: a numeric vector that describes the percentage of site fidelity
mate.fid: a numeric vector that describes the percentage of mate fidelity
mass: an integer vector that describes the adult body weight (g)
ALE: a numeric vector that describes the adult life expectancy (years)
BF: a numeric vector that describes the breeding frequencies
col.size: an integer vector that describes the colony size (no nests monitored)
Bried, J., Pontier, D. and Jouventin, P. (2002) Mate fidelity in monogamus birds: a re-examination of the Procellariiformes. Animal Behaviour, 65, 235–246.
See a data description at http://pbil.univ-lyon1.fr/R/pdf/pps037.pdf (in French).
data(procella) pro.phy <- newick2phylog(procella$tre) plot(pro.phy,clabel.n = 1, clabel.l = 1) wt <- procella$traits wt$site.fid[is.na(wt$site.fid)] <- mean(wt$site.fid[!is.na(wt$site.fid)]) wt$site.fid <- asin(sqrt(wt$site.fid/100)) wt$ALE[is.na(wt$ALE)] <- mean(wt$ALE[!is.na(wt$ALE)]) wt$ALE <- sqrt(wt$ALE) wt$BF[is.na(wt$BF)] <- mean(wt$BF[!is.na(wt$BF)]) wt$mass <- log(wt$mass) wt <- wt[, -6] table.phylog(scalewt(wt), pro.phy, csi = 2) gearymoran(pro.phy$Amat,wt,9999)
data(procella) pro.phy <- newick2phylog(procella$tre) plot(pro.phy,clabel.n = 1, clabel.l = 1) wt <- procella$traits wt$site.fid[is.na(wt$site.fid)] <- mean(wt$site.fid[!is.na(wt$site.fid)]) wt$site.fid <- asin(sqrt(wt$site.fid/100)) wt$ALE[is.na(wt$ALE)] <- mean(wt$ALE[!is.na(wt$ALE)]) wt$ALE <- sqrt(wt$ALE) wt$BF[is.na(wt$BF)] <- mean(wt$BF[!is.na(wt$BF)]) wt$mass <- log(wt$mass) wt <- wt[, -6] table.phylog(scalewt(wt), pro.phy, csi = 2) gearymoran(pro.phy$Amat,wt,9999)
performs a simple procruste rotation between two sets of points.
procuste(dfX, dfY, scale = TRUE, nf = 4, tol = 1e-07) ## S3 method for class 'procuste' plot(x, xax = 1, yax = 2, ...) ## S3 method for class 'procuste' print(x, ...) ## S3 method for class 'procuste' randtest(xtest, nrepet = 999, ...)
procuste(dfX, dfY, scale = TRUE, nf = 4, tol = 1e-07) ## S3 method for class 'procuste' plot(x, xax = 1, yax = 2, ...) ## S3 method for class 'procuste' print(x, ...) ## S3 method for class 'procuste' randtest(xtest, nrepet = 999, ...)
dfX , dfY
|
two data frames with the same rows |
scale |
a logical value indicating whether a transformation by the Gower's scaling (1971) should be applied |
nf |
an integer indicating the number of kept axes |
tol |
a tolerance threshold to test whether the distance matrix is Euclidean : an eigenvalue is considered positive if it is larger than |
x , xtest
|
an objet of class |
xax |
the column number for the x-axis |
yax |
the column number for the y-axis |
nrepet |
the number of repetitions to perform the randomization test |
... |
further arguments passed to or from other methods |
returns a list of the class procuste
with 9 components
d |
a numeric vector of the singular values |
rank |
an integer indicating the rank of the crossed matrix |
nf |
an integer indicating the number of kept axes |
tabX |
a data frame with the array X, possibly scaled |
tabY |
a data frame with the array Y, possibly scaled |
rotX |
a data frame with the result of the rotation from array X to array Y |
rotY |
a data frame with the result of the rotation from array Y to array X |
loadX |
a data frame with the loadings of array X |
loadY |
a data frame with the loadings of array Y |
scorX |
a data frame with the scores of array X |
scorY |
a data frame with the scores of array Y |
call |
a call order of the analysis |
Daniel Chessel
Anne-Béatrice Dufour [email protected]
Digby, P. G. N. and Kempton, R. A. (1987) Multivariate Analysis of Ecological Communities. Population and Community Biology Series, Chapman and Hall, London.
Gower, J.C. (1971) Statistical methods of comparing different multivariate analyses of the same data. In Mathematics in the archaeological and historical sciences, Hodson, F.R, Kendall, D.G. & Tautu, P. (Eds.) University Press, Edinburgh, 138–149.
Schönemann, P.H. (1968) On two-sided Procustes problems. Psychometrika, 33, 19–34.
Torre, F. and Chessel, D. (1994) Co-structure de deux tableaux totalement appariés. Revue de Statistique Appliquée, 43, 109–121.
Dray, S., Chessel, D. and Thioulouse, J. (2003) Procustean co-inertia analysis for the linking of multivariate datasets. Ecoscience, 10, 1, 110-119.
data(macaca) pro1 <- procuste(macaca$xy1, macaca$xy2, scal = FALSE) pro2 <- procuste(macaca$xy1, macaca$xy2) if(adegraphicsLoaded()) { g1 <- s.match(pro1$tabX, pro1$rotY, plab.cex = 0.7, plot = FALSE) g2 <- s.match(pro1$tabY, pro1$rotX, plab.cex = 0.7, plot = FALSE) g3 <- s.match(pro2$tabX, pro2$rotY, plab.cex = 0.7, plot = FALSE) g4 <- s.match(pro2$tabY, pro2$rotX, plab.cex = 0.7, plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) s.match(pro1$tabX, pro1$rotY, clab = 0.7) s.match(pro1$tabY, pro1$rotX, clab = 0.7) s.match(pro2$tabX, pro2$rotY, clab = 0.7) s.match(pro2$tabY, pro2$rotX, clab = 0.7) par(mfrow = c(1,1)) } data(doubs) pca1 <- dudi.pca(doubs$env, scal = TRUE, scann = FALSE) pca2 <- dudi.pca(doubs$fish, scal = FALSE, scann = FALSE) pro3 <- procuste(pca1$tab, pca2$tab, nf = 2) if(adegraphicsLoaded()) { g11 <- s.traject(pro3$scorX, plab.cex = 0, plot = FALSE) g12 <- s.label(pro3$scorX, plab.cex = 0.8, plot = FALSE) g1 <- superpose(g11, g12) g21 <- s.traject(pro3$scorY, plab.cex = 0, plot = FALSE) g22 <- s.label(pro3$scorY, plab.cex = 0.8, plot = FALSE) g2 <- superpose(g21, g22) g3 <- s.arrow(pro3$loadX, plab.cex = 0.75, plot = FALSE) g4 <- s.arrow(pro3$loadY, plab.cex = 0.75, plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) s.traject(pro3$scorX, clab = 0) s.label(pro3$scorX, clab = 0.8, add.p = TRUE) s.traject(pro3$scorY, clab = 0) s.label(pro3$scorY, clab = 0.8, add.p = TRUE) s.arrow(pro3$loadX, clab = 0.75) s.arrow(pro3$loadY, clab = 0.75) par(mfrow = c(1, 1)) } plot(pro3) randtest(pro3) data(fruits) plot(procuste(scalewt(fruits$jug), scalewt(fruits$var)))
data(macaca) pro1 <- procuste(macaca$xy1, macaca$xy2, scal = FALSE) pro2 <- procuste(macaca$xy1, macaca$xy2) if(adegraphicsLoaded()) { g1 <- s.match(pro1$tabX, pro1$rotY, plab.cex = 0.7, plot = FALSE) g2 <- s.match(pro1$tabY, pro1$rotX, plab.cex = 0.7, plot = FALSE) g3 <- s.match(pro2$tabX, pro2$rotY, plab.cex = 0.7, plot = FALSE) g4 <- s.match(pro2$tabY, pro2$rotX, plab.cex = 0.7, plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) s.match(pro1$tabX, pro1$rotY, clab = 0.7) s.match(pro1$tabY, pro1$rotX, clab = 0.7) s.match(pro2$tabX, pro2$rotY, clab = 0.7) s.match(pro2$tabY, pro2$rotX, clab = 0.7) par(mfrow = c(1,1)) } data(doubs) pca1 <- dudi.pca(doubs$env, scal = TRUE, scann = FALSE) pca2 <- dudi.pca(doubs$fish, scal = FALSE, scann = FALSE) pro3 <- procuste(pca1$tab, pca2$tab, nf = 2) if(adegraphicsLoaded()) { g11 <- s.traject(pro3$scorX, plab.cex = 0, plot = FALSE) g12 <- s.label(pro3$scorX, plab.cex = 0.8, plot = FALSE) g1 <- superpose(g11, g12) g21 <- s.traject(pro3$scorY, plab.cex = 0, plot = FALSE) g22 <- s.label(pro3$scorY, plab.cex = 0.8, plot = FALSE) g2 <- superpose(g21, g22) g3 <- s.arrow(pro3$loadX, plab.cex = 0.75, plot = FALSE) g4 <- s.arrow(pro3$loadY, plab.cex = 0.75, plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) s.traject(pro3$scorX, clab = 0) s.label(pro3$scorX, clab = 0.8, add.p = TRUE) s.traject(pro3$scorY, clab = 0) s.label(pro3$scorY, clab = 0.8, add.p = TRUE) s.arrow(pro3$loadX, clab = 0.75) s.arrow(pro3$loadY, clab = 0.75) par(mfrow = c(1, 1)) } plot(pro3) randtest(pro3) data(fruits) plot(procuste(scalewt(fruits$jug), scalewt(fruits$var)))
performs a Monte-Carlo Test on the sum of the singular values of a procustean rotation.
procuste.randtest(df1, df2, nrepet = 999, ...)
procuste.randtest(df1, df2, nrepet = 999, ...)
df1 |
a data frame |
df2 |
a data frame |
nrepet |
the number of permutations |
... |
further arguments passed to or from other methods |
returns a list of class randtest
Jean Thioulouse [email protected]
Jackson, D.A. (1995) PROTEST: a PROcustean randomization TEST of community environment concordance. Ecosciences, 2, 297–303.
data(doubs) pca1 <- dudi.pca(doubs$env, scal = TRUE, scann = FALSE) pca2 <- dudi.pca(doubs$fish, scal = FALSE, scann = FALSE) protest1 <- procuste.randtest(pca1$tab, pca2$tab, 999) protest1 plot(protest1,main="PROTEST")
data(doubs) pca1 <- dudi.pca(doubs$env, scal = TRUE, scann = FALSE) pca2 <- dudi.pca(doubs$fish, scal = FALSE, scann = FALSE) protest1 <- procuste.randtest(pca1$tab, pca2$tab, 999) protest1 plot(protest1,main="PROTEST")
performs a Monte-Carlo Test on the sum of the singular values of a procustean rotation.
procuste.rtest(df1, df2, nrepet = 99, ...)
procuste.rtest(df1, df2, nrepet = 99, ...)
df1 |
a data frame |
df2 |
a data frame |
nrepet |
the number of permutations |
... |
further arguments passed to or from other methods |
returns a list of class rtest
Daniel Chessel
Anne-Béatrice Dufour [email protected]
Jackson, D.A. (1995) PROTEST: a PROcustean randomization TEST of community environment concordance. Ecosciences, 2, 297–303.
data(doubs) pca1 <- dudi.pca(doubs$env, scal = TRUE, scann = FALSE) pca2 <- dudi.pca(doubs$fish, scal = FALSE, scann = FALSE) proc1 <- procuste(pca1$tab, pca2$tab) protest1 <- procuste.rtest(pca1$tab, pca2$tab, 999) protest1 plot(protest1)
data(doubs) pca1 <- dudi.pca(doubs$env, scal = TRUE, scann = FALSE) pca2 <- dudi.pca(doubs$fish, scal = FALSE, scann = FALSE) proc1 <- procuste(pca1$tab, pca2$tab) protest1 <- procuste.rtest(pca1$tab, pca2$tab, 999) protest1 plot(protest1)
performs a partial triadic analysis of a K-tables,
using an object of class ktab
.
pta(X, scannf = TRUE, nf = 2) ## S3 method for class 'pta' plot(x, xax = 1, yax = 2, option = 1:4, ...) ## S3 method for class 'pta' print(x, ...)
pta(X, scannf = TRUE, nf = 2) ## S3 method for class 'pta' plot(x, xax = 1, yax = 2, option = 1:4, ...) ## S3 method for class 'pta' print(x, ...)
X |
an object of class |
scannf |
a logical value indicating whether the eigenvalues bar plot should be displayed |
nf |
if scannf FALSE, an integer indicating the number of kept axes |
x |
an object of class 'pta' |
xax , yax
|
the numbers of the x-axis and the y-axis |
option |
an integer between 1 and 4, otherwise the 4 components of the plot are displayed |
... |
further arguments passed to or from other methods |
returns a list of class 'pta', sub-class of 'dudi' containing :
RV |
a matrix with the all RV coefficients |
RV.eig |
a numeric vector with the all eigenvalues (interstructure) |
RV.coo |
a data frame with the scores of the arrays |
tab.names |
a vector of characters with the array names |
nf |
an integer indicating the number of kept axes |
rank |
an integer indicating the rank of the studied matrix |
tabw |
a numeric vector with the array weights |
cw |
a numeric vector with the column weights |
lw |
a numeric vector with the row weights |
eig |
a numeric vector with the all eigenvalues (compromis) |
cos2 |
a numeric vector with the |
tab |
a data frame with the modified array |
li |
a data frame with the row coordinates |
l1 |
a data frame with the row normed scores |
co |
a data frame with the column coordinates |
c1 |
a data frame with the column normed scores |
Tli |
a data frame with the row coordinates (each table) |
Tco |
a data frame with the column coordinates (each table) |
Tcomp |
a data frame with the principal components (each table) |
Tax |
a data frame with the principal axes (each table) |
TL |
a data frame with the factors for Tli |
TC |
a data frame with the factors for Tco |
T4 |
a data frame with the factors for Tax and Tcomp |
Pierre Bady [email protected]
Anne-Béatrice Dufour [email protected]
Blanc, L., Chessel, D. and Dolédec, S. (1998) Etude de la stabilité temporelle des structures spatiales par Analyse d'une série de tableaux faunistiques totalement appariés. Bulletin Français de la Pêche et de la Pisciculture, 348, 1–21.
Thioulouse, J., and D. Chessel. 1987. Les analyses multi-tableaux en écologie factorielle. I De la typologie d'état à la typologie de fonctionnement par l'analyse triadique. Acta Oecologica, Oecologia Generalis, 8, 463–480.
data(meaudret) wit1 <- withinpca(meaudret$env, meaudret$design$season, scan = FALSE, scal = "partial") kta1 <- ktab.within(wit1, colnames = rep(c("S1", "S2", "S3", "S4", "S5"), 4)) kta2 <- t(kta1) pta1 <- pta(kta2, scann = FALSE) pta1 plot(pta1)
data(meaudret) wit1 <- withinpca(meaudret$env, meaudret$design$season, scan = FALSE, scal = "partial") kta1 <- ktab.within(wit1, colnames = rep(c("S1", "S2", "S3", "S4", "S5"), 4)) kta2 <- t(kta1) pta1 <- pta(kta2, scann = FALSE) pta1 plot(pta1)
transforms a distance matrix in a Euclidean one.
quasieuclid(distmat)
quasieuclid(distmat)
distmat |
an object of class |
The function creates a distance matrice with the positive eigenvalues of the Euclidean representation.
Only for Euclidean distances which are not Euclidean for numeric approximations (for examples, in papers as the following example).
object of class dist
containing a Euclidean distance matrice
Daniel Chessel
Stéphane Dray [email protected]
data(yanomama) geo <- as.dist(yanomama$geo) is.euclid(geo) # FALSE geo1 <- quasieuclid(geo) is.euclid(geo1) # TRUE par(mfrow = c(2,2)) lapply(yanomama, function(x) plot(as.dist(x), quasieuclid(as.dist(x)))) par(mfrow = c(1,1))
data(yanomama) geo <- as.dist(yanomama$geo) is.euclid(geo) # FALSE geo1 <- quasieuclid(geo) is.euclid(geo1) # TRUE par(mfrow = c(2,2)) lapply(yanomama, function(x) plot(as.dist(x), quasieuclid(as.dist(x)))) par(mfrow = c(1,1))
Functions and classes to manage outputs of bootstrap
simulations for one (class randboot
) or several (class krandboot
) statistics
as.krandboot(obs, boot, quantiles = c(0.025, 0.975), names = colnames(boot), call = match.call()) ## S3 method for class 'krandboot' print(x, ...) as.randboot(obs, boot, quantiles = c(0.025, 0.975), call = match.call()) ## S3 method for class 'randboot' print(x, ...) randboot(object, ...)
as.krandboot(obs, boot, quantiles = c(0.025, 0.975), names = colnames(boot), call = match.call()) ## S3 method for class 'krandboot' print(x, ...) as.randboot(obs, boot, quantiles = c(0.025, 0.975), call = match.call()) ## S3 method for class 'randboot' print(x, ...) randboot(object, ...)
obs |
a value (class |
boot |
a vector (class |
quantiles |
a vector indicating the lower and upper quantiles to compute |
names |
a vector of names for the statistics |
call |
the matching call |
x |
an object of class |
object |
an object on which bootstrap should be perform |
... |
other arguments to be passed to methods |
an object of class randboot
or krandboot
Stéphane Dray ([email protected])
Carpenter, J. and Bithell, J. (2000) Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians.Statistics in medicine, 19, 1141-1164
## an example corresponding to 10 statistics and 100 repetitions bt <- as.krandboot(obs = rnorm(10), boot = matrix(rnorm(1000), nrow = 100)) bt if(adegraphicsLoaded()) plot(bt)
## an example corresponding to 10 statistics and 100 repetitions bt <- as.krandboot(obs = rnorm(10), boot = matrix(rnorm(1000), nrow = 100)) bt if(adegraphicsLoaded()) plot(bt)
Function to perform bootstraped simulations for multiblock principal component analysis with instrumental variables or multiblock partial least squares, in order to get confidence intervals for some parameters, i.e., regression coefficients, variable and block importances
## S3 method for class 'multiblock' randboot(object, nrepet = 199, optdim, ...)
## S3 method for class 'multiblock' randboot(object, nrepet = 199, optdim, ...)
object |
|
nrepet |
integer indicating the number of repetitions |
optdim |
integer indicating the optimal number of dimensions, i.e., the optimal number of global components to be introduced in the model |
... |
other arguments to be passed to methods |
A list containing objects of class krandboot
Stéphanie Bougeard ([email protected]) and Stéphane Dray ([email protected])
Carpenter, J. and Bithell, J. (2000) Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians.Statistics in medicine, 19, 1141-1164.
Bougeard, S. and Dray S. (2018) Supervised Multiblock Analysis in R with the ade4 Package. Journal of Statistical Software, 86 (1), 1-17. doi:10.18637/jss.v086.i01
mbpcaiv
, mbpls
,
testdim.multiblock
, as.krandboot
data(chickenk) Mortality <- chickenk[[1]] dudiY.chick <- dudi.pca(Mortality, center = TRUE, scale = TRUE, scannf = FALSE) ktabX.chick <- ktab.list.df(chickenk[2:5]) resmbpcaiv.chick <- mbpcaiv(dudiY.chick, ktabX.chick, scale = TRUE, option = "uniform", scannf = FALSE, nf = 4) ## nrepet should be higher for a real analysis test <- randboot(resmbpcaiv.chick, optdim = 4, nrepet = 10) test if(adegraphicsLoaded()) plot(test$bipc)
data(chickenk) Mortality <- chickenk[[1]] dudiY.chick <- dudi.pca(Mortality, center = TRUE, scale = TRUE, scannf = FALSE) ktabX.chick <- ktab.list.df(chickenk[2:5]) resmbpcaiv.chick <- mbpcaiv(dudiY.chick, ktabX.chick, scale = TRUE, option = "uniform", scannf = FALSE, nf = 4) ## nrepet should be higher for a real analysis test <- randboot(resmbpcaiv.chick, optdim = 4, nrepet = 10) test if(adegraphicsLoaded()) plot(test$bipc)
randtest
is a generic function. It proposes methods for the following objects between
, discrimin
, coinertia
...
randtest(xtest, ...) as.randtest(sim, obs, alter = c("greater", "less", "two-sided"), output = c("light", "full"), call = match.call(), subclass = NULL) ## S3 method for class 'randtest' plot(x, nclass = 10, coeff = 1, ...) ## S3 method for class 'randtest' print(x, ...)
randtest(xtest, ...) as.randtest(sim, obs, alter = c("greater", "less", "two-sided"), output = c("light", "full"), call = match.call(), subclass = NULL) ## S3 method for class 'randtest' plot(x, nclass = 10, coeff = 1, ...) ## S3 method for class 'randtest' print(x, ...)
xtest |
an object used to select a method |
x |
an object of class |
... |
further arguments passed to or from other methods; in |
output |
a character string specifying if all simulations should be stored ( |
nclass |
a number of intervals for the histogram. Ignored if object output is |
coeff |
to fit the magnitude of the graph. Ignored if object output is |
sim |
a numeric vector of simulated values |
obs |
a numeric vector of an observed value |
alter |
a character string specifying the alternative hypothesis, must be one of "greater" (default), "less" or "two-sided" |
call |
a call order |
subclass |
a character vector indicating the subclasses associated to the returned object |
If the alternative hypothesis is "greater", a p-value is estimated as: (number of random values equal to or greater than the observed one + 1)/(number of permutations + 1). The null hypothesis is rejected if the p-value is less than the significance level. If the alternative hypothesis is "less", a p-value is estimated as: (number of random values equal to or less than the observed one + 1)/(number of permutations + 1). Again, the null hypothesis is rejected if the p-value is less than the significance level. Lastly, if the alternative hypothesis is "two-sided", the estimation of the p-value is equivalent to the one used for "greater" except that random and observed values are firstly centered (using the average of random values) and secondly transformed to their absolute values. Note that this is only suitable for symmetric random distribution.
as.randtest
returns a list of class randtest
.plot.randtest
draws the simulated values histograms and the position of the observed value.
randtest.amova, randtest.between, randtest.coinertia, randtest.discrimin, randtest.dpcoa, randtest.pcaiv, rtest, rtest.between, rtest.discrimin, RV.rtest, RVdist.randtest, mantel.randtest, mantel.rtest, procuste.randtest, procuste.rtest
par(mfrow = c(2,2)) for (x0 in c(2.4,3.4,5.4,20.4)) { l0 <- as.randtest(sim = rnorm(200), obs = x0) print(l0) plot(l0,main=paste("p.value = ", round(l0$pvalue, dig = 5))) } par(mfrow = c(1,1))
par(mfrow = c(2,2)) for (x0 in c(2.4,3.4,5.4,20.4)) { l0 <- as.randtest(sim = rnorm(200), obs = x0) print(l0) plot(l0,main=paste("p.value = ", round(l0$pvalue, dig = 5))) } par(mfrow = c(1,1))
Tests the components of covariance with permutation processes described by Excoffier et al. (1992).
## S3 method for class 'amova' randtest(xtest, nrepet = 99, ...)
## S3 method for class 'amova' randtest(xtest, nrepet = 99, ...)
xtest |
an object of class |
nrepet |
the number of permutations |
... |
further arguments passed to or from other methods |
returns an object of class krandtest
or randtest
Sandrine Pavoine [email protected]
Excoffier, L., Smouse, P.E. and Quattro, J.M. (1992) Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics, 131, 479–491.
data(humDNAm) amovahum <- amova(humDNAm$samples, sqrt(humDNAm$distances), humDNAm$structures) amovahum randtesthum <- randtest(amovahum, 49) plot(randtesthum)
data(humDNAm) amovahum <- amova(humDNAm$samples, sqrt(humDNAm$distances), humDNAm$structures) amovahum randtesthum <- randtest(amovahum, 49) plot(randtesthum)
Performs a Monte-Carlo test on the between-groups inertia percentage.
## S3 method for class 'between' randtest(xtest, nrepet = 999, ...)
## S3 method for class 'between' randtest(xtest, nrepet = 999, ...)
xtest |
an object of class |
nrepet |
the number of permutations |
... |
further arguments passed to or from other methods |
a list of the class randtest
Jean Thioulouse [email protected]
Romesburg, H. C. (1985) Exploring, confirming and randomization tests. Computers and Geosciences, 11, 19–37.
data(meaudret) pca1 <- dudi.pca(meaudret$env, scan = FALSE, nf = 3) rand1 <- randtest(bca(pca1, meaudret$design$season, scan = FALSE), 99) rand1 plot(rand1, main = "Monte-Carlo test")
data(meaudret) pca1 <- dudi.pca(meaudret$env, scan = FALSE, nf = 3) rand1 <- randtest(bca(pca1, meaudret$design$season, scan = FALSE), 99) rand1 plot(rand1, main = "Monte-Carlo test")
Performs a Monte-Carlo test on a Co-inertia analysis.
## S3 method for class 'coinertia' randtest(xtest, nrepet = 999, fixed=0, ...)
## S3 method for class 'coinertia' randtest(xtest, nrepet = 999, fixed=0, ...)
xtest |
an object of class |
nrepet |
the number of permutations |
fixed |
when non uniform row weights are used in the coinertia analysis, this parameter must be the number of the table that should be kept fixed in the permutations |
... |
further arguments passed to or from other methods |
a list of the class randtest
A testing procedure based on the total coinertia of the analysis
is available by the function randtest.coinertia
. The function
allows to deal with various analyses for the two tables. The test is
based on random permutations of the rows of the two tables. If the row
weights are not uniform, mean and variances are recomputed for each
permutation (PCA); for MCA, tables are recentred and column weights are recomputed. If weights are computed using the data contained in one
table (e.g. COA), you must fix this table and permute only the rows of
the other table. The case of decentred PCA (PCA where centers are
entered by the user) is not yet implemented. If you want to use the
testing procedure for this case, you must firstly center the table and then perform a
non-centered PCA on the modified table. The case where one table is
treated by hill-smith analysis (mix of quantitative and qualitative
variables) will be soon implemented.
Jean Thioulouse [email protected] modified by Stéphane Dray [email protected]
Dolédec, S. and Chessel, D. (1994) Co-inertia analysis: an alternative method for studying species-environment relationships. Freshwater Biology, 31, 277–294.
data(doubs) dudi1 <- dudi.pca(doubs$env, scale = TRUE, scan = FALSE, nf = 3) dudi2 <- dudi.pca(doubs$fish, scale = FALSE, scan = FALSE, nf = 2) coin1 <- coinertia(dudi1,dudi2, scan = FALSE, nf = 2) plot(randtest(coin1))
data(doubs) dudi1 <- dudi.pca(doubs$env, scale = TRUE, scan = FALSE, nf = 3) dudi2 <- dudi.pca(doubs$fish, scale = FALSE, scan = FALSE, nf = 2) coin1 <- coinertia(dudi1,dudi2, scan = FALSE, nf = 2) plot(randtest(coin1))
Test of the sum of a discriminant analysis eigenvalues (divided by the rank). Non parametric version of the Pillai's test. It authorizes any weighting.
## S3 method for class 'discrimin' randtest(xtest, nrepet = 999, ...)
## S3 method for class 'discrimin' randtest(xtest, nrepet = 999, ...)
xtest |
an object of class |
nrepet |
the number of permutations |
... |
further arguments passed to or from other methods |
returns a list of class randtest
Jean Thioulouse [email protected]
data(meaudret) pca1 <- dudi.pca(meaudret$env, scan = FALSE, nf = 3) rand1 <- randtest(discrimin(pca1, meaudret$design$season, scan = FALSE), 99) rand1 #Monte-Carlo test #Observation: 0.3035 #Call: as.randtest(sim = sim, obs = obs) #Based on 999 replicates #Simulated p-value: 0.001 plot(rand1, main = "Monte-Carlo test") summary.manova(manova(as.matrix(meaudret$env)~meaudret$design$season), "Pillai") # Df Pillai approx F num Df den Df Pr(>F) # meaudret$design$season 3 2.73 11.30 27 30 1.6e-09 *** # Residuals 16 # --- # Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 # 2.731/9 = 0.3034
data(meaudret) pca1 <- dudi.pca(meaudret$env, scan = FALSE, nf = 3) rand1 <- randtest(discrimin(pca1, meaudret$design$season, scan = FALSE), 99) rand1 #Monte-Carlo test #Observation: 0.3035 #Call: as.randtest(sim = sim, obs = obs) #Based on 999 replicates #Simulated p-value: 0.001 plot(rand1, main = "Monte-Carlo test") summary.manova(manova(as.matrix(meaudret$env)~meaudret$design$season), "Pillai") # Df Pillai approx F num Df den Df Pr(>F) # meaudret$design$season 3 2.73 11.30 27 30 1.6e-09 *** # Residuals 16 # --- # Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 # 2.731/9 = 0.3034
randtest.dpcoa
calculates the ratio of beta to gamma diversity associated with DPCoA and compares the observed value to values obtained by permuting data.
## S3 method for class 'dpcoa' randtest(xtest, model = c("1p","1s"), nrepet = 99, alter = c("greater", "less", "two-sided"), ...)
## S3 method for class 'dpcoa' randtest(xtest, model = c("1p","1s"), nrepet = 99, alter = c("greater", "less", "two-sided"), ...)
xtest |
an object of class |
model |
either "1p", "1s", or the name of a function, (see details) |
nrepet |
the number of permutations to perform, the default is 99 |
alter |
a character string specifying the alternative hypothesis, must be one of "greater" (default), "less" or "two-sided" |
... |
further arguments passed to or from other methods |
Model 1p permutes the names of the columns of the abundance matrix. Model 1s permutes the abundances of the categories (columns of the abundance matrix, usually species) within collections (rows of the abundance matrix, usually communities). Only the categories with positive abundances are permuted. The null models were introduced in Hardy (2008).
Other null model can be used by entering the name of a function. For example, loading the picante
package of R, if model=randomizeMatrix
, then the permutations will follow function randomizeMatrix
available in picante. Any function can be used provided it returns an abundance matrix of similar size as the observed abundance matrix. Parameters of the chosen function can be added to randtest.dpcoa
. For example, using parameter null.model
of randomizeMatrix
, the following command can be used:
randtest.dpcoa(xtest, model = randomizeMatrix, null.model = "trialswap")
an object of class randtest
Sandrine Pavoine [email protected]
Hardy, O. (2008) Testing the spatial phylogenetic structure of local communities: statistical performances of different null models and test statistics on a locally neutral community. Journal of Ecology, 96, 914–926
data(humDNAm) dpcoahum <- dpcoa(data.frame(t(humDNAm$samples)), sqrt(humDNAm$distances), scan = FALSE, nf = 2) randtest(dpcoahum)
data(humDNAm) dpcoahum <- dpcoa(data.frame(t(humDNAm$samples)), sqrt(humDNAm$distances), scan = FALSE, nf = 2) randtest(dpcoahum)
Performs a Monte-Carlo test on on the percentage of explained (i.e. constrained) inertia. The statistic is the ratio of the inertia (sum of eigenvalues) of the constrained analysis divided by the inertia of the unconstrained analysis.
## S3 method for class 'pcaiv' randtest(xtest, nrepet = 99, ...) ## S3 method for class 'pcaivortho' randtest(xtest, nrepet = 99, ...)
## S3 method for class 'pcaiv' randtest(xtest, nrepet = 99, ...) ## S3 method for class 'pcaivortho' randtest(xtest, nrepet = 99, ...)
xtest |
an object of class |
nrepet |
the number of permutations |
... |
further arguments passed to or from other methods |
a list of the class randtest
Stéphane Dray [email protected], original code by Raphaël Pélissier
data(rpjdl) millog <- log(rpjdl$mil + 1) coa1 <- dudi.coa(rpjdl$fau, scann = FALSE) caiv1 <- pcaiv(coa1, millog, scan = FALSE) randtest(caiv1)
data(rpjdl) millog <- log(rpjdl$mil + 1) coa1 <- dudi.coa(rpjdl$fau, scann = FALSE) caiv1 <- pcaiv(coa1, millog, scan = FALSE) randtest(caiv1)
Functions and classes to manage outputs of two-fold
cross-validation for one (class randxval
) or several (class
krandxval
) statistics
as.krandxval(RMSEc, RMSEv, quantiles = c(0.25, 0.75), names = colnames(RMSEc), call = match.call()) ## S3 method for class 'krandxval' print(x, ...) as.randxval(RMSEc, RMSEv, quantiles = c(0.25, 0.75), call = match.call()) ## S3 method for class 'randxval' print(x, ...)
as.krandxval(RMSEc, RMSEv, quantiles = c(0.25, 0.75), names = colnames(RMSEc), call = match.call()) ## S3 method for class 'krandxval' print(x, ...) as.randxval(RMSEc, RMSEv, quantiles = c(0.25, 0.75), call = match.call()) ## S3 method for class 'randxval' print(x, ...)
RMSEc |
a vector (class |
RMSEv |
a vector (class |
quantiles |
a vector indicating the lower and upper quantiles to compute |
names |
a vector of names for the statistics |
call |
the matching call |
x |
an object of class |
... |
other arguments to be passed to methods |
an object of class randxval
or krandxval
Stéphane Dray ([email protected])
Stone M. (1974) Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, 36, 111-147
## an example corresponding to 10 statistics and 100 repetitions cv <- as.krandxval(RMSEc = matrix(rnorm(1000), nrow = 100), RMSEv = matrix(rnorm(1000, mean = 1), nrow = 100)) cv if(adegraphicsLoaded()) plot(cv)
## an example corresponding to 10 statistics and 100 repetitions cv <- as.krandxval(RMSEc = matrix(rnorm(1000), nrow = 100), RMSEv = matrix(rnorm(1000, mean = 1), nrow = 100)) cv if(adegraphicsLoaded()) plot(cv)
This data set gives the classification in order of preference of 10 music groups by 51 students.
data(rankrock)
data(rankrock)
A data frame with 10 rows and 51 columns.
Each column contains the rank (1 for the favorite, ..., 10 for the less appreciated)
attributed to the group by a student.
data(rankrock) dudi1 <- dudi.pca(rankrock, scannf = FALSE, nf = 3) if(adegraphicsLoaded()) { g <- scatter(dudi1, row.plab.cex = 1.5) } else { scatter(dudi1, clab.r = 1.5) }
data(rankrock) dudi1 <- dudi.pca(rankrock, scannf = FALSE, nf = 3) if(adegraphicsLoaded()) { g <- scatter(dudi1, row.plab.cex = 1.5) } else { scatter(dudi1, clab.r = 1.5) }
Generic Function for the reconstitution of data from a principal component analysis or a correspondence analysis
reconst (dudi, ...) ## S3 method for class 'pca' reconst(dudi, nf = 1, ...) ## S3 method for class 'coa' reconst(dudi, nf = 1, ...)
reconst (dudi, ...) ## S3 method for class 'pca' reconst(dudi, nf = 1, ...) ## S3 method for class 'coa' reconst(dudi, nf = 1, ...)
dudi |
an object of class |
nf |
an integer indicating the number of kept axes for the reconstitution |
... |
further arguments passed to or from other methods |
returns a data frame containing the reconstituted data
Daniel Chessel
Anne-Béatrice Dufour [email protected]
Gabriel, K.R. (1978) Least-squares approximation of matrices by additive and multiplicative models. Journal of the Royal Statistical Society, B , 40, 186–196.
data(rhone) dd1 <- dudi.pca(rhone$tab, nf = 2, scann = FALSE) rh1 <- reconst(dd1, 1) rh2 <- reconst(dd1, 2) par(mfrow = c(4,4)) par(mar = c(2.6,2.6,1.1,1.1)) for (i in 1:15) { plot(rhone$date, rhone$tab[,i]) lines(rhone$date, rh1[,i], lty = 2) lines(rhone$date, rh2[,i], lty = 1) ade4:::scatterutil.sub(names(rhone$tab)[i], 2, "topright")} data(chats) chatsw <- data.frame(t(chats)) chatscoa <- dudi.coa(chatsw, scann = FALSE) model0 <- reconst(chatscoa, 0) round(model0,3) round(chisq.test(chatsw)$expected,3) chisq.test(chatsw)$statistic sum(((chatsw-model0)^2)/model0) effectif <- sum(chatsw) sum(chatscoa$eig)*effectif model1 <- reconst(chatscoa, 1) round(model1, 3) sum(((chatsw-model1)^2)/model0) sum(chatscoa$eig[-1])*effectif
data(rhone) dd1 <- dudi.pca(rhone$tab, nf = 2, scann = FALSE) rh1 <- reconst(dd1, 1) rh2 <- reconst(dd1, 2) par(mfrow = c(4,4)) par(mar = c(2.6,2.6,1.1,1.1)) for (i in 1:15) { plot(rhone$date, rhone$tab[,i]) lines(rhone$date, rh1[,i], lty = 2) lines(rhone$date, rh2[,i], lty = 1) ade4:::scatterutil.sub(names(rhone$tab)[i], 2, "topright")} data(chats) chatsw <- data.frame(t(chats)) chatscoa <- dudi.coa(chatsw, scann = FALSE) model0 <- reconst(chatscoa, 0) round(model0,3) round(chisq.test(chatsw)$expected,3) chisq.test(chatsw)$statistic sum(((chatsw-model0)^2)/model0) effectif <- sum(chatsw) sum(chatscoa$eig)*effectif model1 <- reconst(chatscoa, 1) round(model1, 3) sum(((chatsw-model1)^2)/model0) sum(chatscoa$eig[-1])*effectif
The data set concerns fixing bacteria belonging to the genus Sinorhizobium (Rhizobiaceae) associated with the plant genus Medicago (Fabaceae). It is a combination of two data sets fully available online from GenBank and published in two recent papers (see reference below). The complete sampling procedure is described in the Additional file 3 of the reference below. We delineated six populations according to geographical origin (France: F, Tunisia Hadjeb: TH, Tunisia Enfidha: TE), the host plant (M. truncatula or similar symbiotic specificity: T, M. laciniata: L), and the taxonomical status of bacteria (S. meliloti: mlt, S. medicae: mdc). Each population will be called hereafter according to the three above criteria, e.g. THLmlt is the population sampled in Tunisia at Hadjeb from M. laciniata nodules which include S. meliloti isolates. S. medicae interacts with M. truncatula while S. meliloti interacts with both M. laciniata (S. meliloti bv. medicaginis) and M. truncatula (S. meliloti bv. meliloti). The numbers of individuals are respectively 46 for FTmdc, 43 for FTmlt, 20 for TETmdc, 24 for TETmlt, 20 for TELmlt, 42 for THTmlt and 20 for THLmlt.
Four different intergenic spacers (IGS), IGSNOD, IGSEXO, IGSGAB, and IGSRKP, distributed on the different replication units of the model strain 1021 of S. meliloti bv. meliloti had been sequenced to characterize each bacterial isolate (DNA extraction and sequencing procedures are described in an additional file). It is noteworthy that the IGSNOD marker is located within the nod gene cluster and that specific alleles at these loci determine the ability of S. meliloti strains to interact with either M. laciniata or M. truncatula.
data(rhizobium)
data(rhizobium)
rhizobium
is a list of 2 components.
dnaobj: list of dna lists. Each dna list corresponds to a locus. For a given locus, the dna list provides the dna sequences The ith sequences of all loci corresponds to the ith individual of the data set.
pop: The list of the populations which each individual sequence belongs to.
Pavoine, S. and Bailly, X. (2007) New analysis for consistency among markers in the study of genetic diversity: development and application to the description of bacterial diversity. BMC Evolutionary Biology, 7, e156.
# The functions used below require the package ape data(rhizobium) if(requireNamespace("ape", quietly = TRUE)) { dat <- prep.mdpcoa(rhizobium[[1]], rhizobium[[2]], model = c("F84", "F84", "F84", "F81"), pairwise.deletion = TRUE) sam <- dat$sam dis <- dat$dis # The distances should be Euclidean. # Several transformations exist to render a distance object Euclidean # (see functions cailliez, lingoes and quasieuclid in the ade4 package). # Here we use the quasieuclid function. dis <- lapply(dis, quasieuclid) mdpcoa1 <- mdpcoa(sam, dis, scann = FALSE, nf = 2) # Reference analysis plot(mdpcoa1) # Differences between the loci kplot(mdpcoa1) # Alleles projected on the population maps. kplotX.mdpcoa(mdpcoa1) }
# The functions used below require the package ape data(rhizobium) if(requireNamespace("ape", quietly = TRUE)) { dat <- prep.mdpcoa(rhizobium[[1]], rhizobium[[2]], model = c("F84", "F84", "F84", "F81"), pairwise.deletion = TRUE) sam <- dat$sam dis <- dat$dis # The distances should be Euclidean. # Several transformations exist to render a distance object Euclidean # (see functions cailliez, lingoes and quasieuclid in the ade4 package). # Here we use the quasieuclid function. dis <- lapply(dis, quasieuclid) mdpcoa1 <- mdpcoa(sam, dis, scann = FALSE, nf = 2) # Reference analysis plot(mdpcoa1) # Differences between the loci kplot(mdpcoa1) # Alleles projected on the population maps. kplotX.mdpcoa(mdpcoa1) }
This data set gives for 39 water samples a physico-chemical description with the number of sample date and the flows of three tributaries.
data(rhone)
data(rhone)
rhone
is a list of 3 components.
is a data frame with 39 water samples and 15 physico-chemical variables.
is a vector of the sample date (in days).
is a data frame with 39 water samples and the flows of the three tributaries.
Carrel, G., Barthelemy, D., Auda, Y. and Chessel, D. (1986) Approche graphique de l'analyse en composantes principales normée : utilisation en hydrobiologie. Acta Oecologica, Oecologia Generalis, 7, 189–203.
data(rhone) pca1 <- dudi.pca(rhone$tab, nf = 2, scann = FALSE) rh1 <- reconst(pca1, 1) rh2 <- reconst(pca1, 2) par(mfrow = c(4,4)) par(mar = c(2.6,2.6,1.1,1.1)) for (i in 1:15) { plot(rhone$date, rhone$tab[,i]) lines(rhone$date, rh1[,i], lwd = 2) lines(rhone$date, rh2[,i]) ade4:::scatterutil.sub(names(rhone$tab)[i], 2, "topright") } par(mfrow = c(1,1))
data(rhone) pca1 <- dudi.pca(rhone$tab, nf = 2, scann = FALSE) rh1 <- reconst(pca1, 1) rh2 <- reconst(pca1, 2) par(mfrow = c(4,4)) par(mar = c(2.6,2.6,1.1,1.1)) for (i in 1:15) { plot(rhone$date, rhone$tab[,i]) lines(rhone$date, rh1[,i], lwd = 2) lines(rhone$date, rh2[,i]) ade4:::scatterutil.sub(names(rhone$tab)[i], 2, "topright") } par(mfrow = c(1,1))
RLQ analysis performs a double inertia analysis of two arrays (R and Q) with a link expressed by a contingency table (L). The rows of L correspond to the rows of R and the columns of L correspond to the rows of Q.
rlq(dudiR, dudiL, dudiQ, scannf = TRUE, nf = 2) ## S3 method for class 'rlq' print(x, ...) ## S3 method for class 'rlq' plot(x, xax = 1, yax = 2, ...) ## S3 method for class 'rlq' summary(object, ...) ## S3 method for class 'rlq' randtest(xtest,nrepet = 999, modeltype = 6,...)
rlq(dudiR, dudiL, dudiQ, scannf = TRUE, nf = 2) ## S3 method for class 'rlq' print(x, ...) ## S3 method for class 'rlq' plot(x, xax = 1, yax = 2, ...) ## S3 method for class 'rlq' summary(object, ...) ## S3 method for class 'rlq' randtest(xtest,nrepet = 999, modeltype = 6,...)
dudiR |
a duality diagram providing from one of the functions dudi.hillsmith, dudi.pca, ... |
dudiL |
a duality diagram of the function dudi.coa |
dudiQ |
a duality diagram providing from one of the functions dudi.hillsmith, dudi.pca, ... |
scannf |
a logical value indicating whether the eigenvalues bar plot should be displayed |
nf |
if scannf FALSE, an integer indicating the number of kept axes |
x |
an rlq object |
xax |
the column number for the x-axis |
yax |
the column number for the y-axis |
object |
an rlq object |
xtest |
an rlq object |
nrepet |
the number of permutations |
modeltype |
the model used to permute data(2: permute rows of R, 4: permute rows of Q, 5: permute both, 6: sequential approach, see ter Braak et al. 2012) |
... |
further arguments passed to or from other methods |
Returns a list of class 'dudi', sub-class 'rlq' containing:
call |
call |
rank |
rank |
nf |
a numeric value indicating the number of kept axes |
RV |
a numeric value, the RV coefficient |
eig |
a numeric vector with all the eigenvalues |
lw |
a numeric vector with the rows weigths (crossed array) |
cw |
a numeric vector with the columns weigths (crossed array) |
tab |
a crossed array (CA) |
li |
R col = CA row: coordinates |
l1 |
R col = CA row: normed scores |
co |
Q col = CA column: coordinates |
c1 |
Q col = CA column: normed scores |
lR |
the row coordinates (R) |
mR |
the normed row scores (R) |
lQ |
the row coordinates (Q) |
mQ |
the normed row scores (Q) |
aR |
the axis onto co-inertia axis (R) |
aQ |
the axis onto co-inertia axis (Q) |
IMPORTANT : row weights for dudiR
and dudiQ
must be taken from dudiL
.
A testing procedure based on the total coinertia of the RLQ
analysis is available by the function randtest.rlq
. The
function allows to deal with various analyses for tables R and Q. Means and variances are recomputed for each
permutation (PCA); for MCA, tables are recentred and column weights are recomputed.The
case of decentred PCA (PCA where centers are entered by the user) for
R or Q is not yet implemented. If you want to use the testing
procedure for this case, you must firstly center the table and then perform a non-centered PCA on the modified table.
Stéphane Dray [email protected]
Doledec, S., Chessel, D., ter Braak, C.J.F. and Champely, S. (1996) Matching species traits to environmental variables: a new three-table ordination method. Environmental and Ecological Statistics, 3, 143–166.
Dray, S., Pettorelli, N., Chessel, D. (2002) Matching data sets from two different spatial samplings. Journal of Vegetation Science, 13, 867–874.
Dray, S. and Legendre, P. (2008) Testing the species traits-environment relationships: the fourth-corner problem revisited. Ecology, 89, 3400–3412.
ter Braak, C., Cormont, A., Dray, S. (2012) Improved testing of species traits-environment relationships in the fourth corner problem. Ecology, 93, 1525–1526.
data(aviurba) coa1 <- dudi.coa(aviurba$fau, scannf = FALSE, nf = 2) dudimil <- dudi.hillsmith(aviurba$mil, scannf = FALSE, nf = 2, row.w = coa1$lw) duditrait <- dudi.hillsmith(aviurba$traits, scannf = FALSE, nf = 2, row.w = coa1$cw) rlq1 <- rlq(dudimil, coa1, duditrait, scannf = FALSE, nf = 2) plot(rlq1) summary(rlq1) randtest(rlq1) fourthcorner.rlq(rlq1,type="Q.axes") fourthcorner.rlq(rlq1,type="R.axes")
data(aviurba) coa1 <- dudi.coa(aviurba$fau, scannf = FALSE, nf = 2) dudimil <- dudi.hillsmith(aviurba$mil, scannf = FALSE, nf = 2, row.w = coa1$lw) duditrait <- dudi.hillsmith(aviurba$traits, scannf = FALSE, nf = 2, row.w = coa1$cw) rlq1 <- rlq(dudimil, coa1, duditrait, scannf = FALSE, nf = 2) plot(rlq1) summary(rlq1) randtest(rlq1) fourthcorner.rlq(rlq1,type="Q.axes") fourthcorner.rlq(rlq1,type="R.axes")
This data set gives the abundance of 51 species and 8 environmental variables in 182 sites.
data(rpjdl)
data(rpjdl)
rpjdl
is a list of 5 components.
is the faunistic array of 182 sites (rows) and 51 species (columns).
is the array of environmental variables : 182 sites and 8 variables.
is a vector of the names of species in French.
is a vector of the names of species in Latin.
is a vector of the simplified labels of species.
Prodon, R. and Lebreton, J.D. (1981) Breeding avifauna of a Mediterranean succession : the holm oak and cork oak series in the eastern Pyrénées. 1 : Analysis and modelling of the structure gradient. Oïkos, 37, 21–38.
Lebreton, J. D., Chessel D., Prodon R. and Yoccoz N. (1988) L'analyse des relations espèces-milieu par l'analyse canonique des correspondances. I. Variables de milieu quantitatives. Acta Oecologica, Oecologia Generalis, 9, 53–67.
See a data description at http://pbil.univ-lyon1.fr/R/pdf/pps048.pdf (in French).
## Not run: data(rpjdl) coa1 <- dudi.coa(rpjdl$fau, scann = FALSE) pca1 <- dudi.pca(rpjdl$fau, scal = FALSE, scann = FALSE) if(adegraphicsLoaded()) { g1 <- s.distri(coa1$l1, rpjdl$fau, xax = 2, yax = 1, starSize = 0.3, ellipseSize = 0, plab.cex = 0) g2 <- s.distri(pca1$l1, rpjdl$fau, xax = 2, yax = 1, starSize = 0.3, ellipseSize = 0, plab.cex = 0) } else { s.distri(coa1$l1, rpjdl$fau, 2, 1, cstar = 0.3, cell = 0) s.distri(pca1$l1, rpjdl$fau, 2, 1, cstar = 0.3, cell = 0) } caiv1 <- pcaiv(coa1, rpjdl$mil, scan = FALSE) plot(caiv1) ## End(Not run)
## Not run: data(rpjdl) coa1 <- dudi.coa(rpjdl$fau, scann = FALSE) pca1 <- dudi.pca(rpjdl$fau, scal = FALSE, scann = FALSE) if(adegraphicsLoaded()) { g1 <- s.distri(coa1$l1, rpjdl$fau, xax = 2, yax = 1, starSize = 0.3, ellipseSize = 0, plab.cex = 0) g2 <- s.distri(pca1$l1, rpjdl$fau, xax = 2, yax = 1, starSize = 0.3, ellipseSize = 0, plab.cex = 0) } else { s.distri(coa1$l1, rpjdl$fau, 2, 1, cstar = 0.3, cell = 0) s.distri(pca1$l1, rpjdl$fau, 2, 1, cstar = 0.3, cell = 0) } caiv1 <- pcaiv(coa1, rpjdl$mil, scan = FALSE) plot(caiv1) ## End(Not run)
rtest is a generic function. It proposes methods for the following objects between
, discrimin
, procuste
...
rtest(xtest, ...)
rtest(xtest, ...)
xtest |
an object used to select a method |
... |
further arguments passed to or from other methods; in |
rtest
returns an object of class randtest
Daniel Chessel
RV.rtest
, mantel.rtest
, procuste.rtest
, randtest
par(mfrow = c(2, 2)) for (x0 in c(2.4, 3.4, 5.4, 20.4)) { l0 <- as.randtest(sim = rnorm(200), obs = x0) print(l0) plot(l0, main = paste("p.value = ", round(l0$pvalue, dig = 5))) } par(mfrow = c(1, 1))
par(mfrow = c(2, 2)) for (x0 in c(2.4, 3.4, 5.4, 20.4)) { l0 <- as.randtest(sim = rnorm(200), obs = x0) print(l0) plot(l0, main = paste("p.value = ", round(l0$pvalue, dig = 5))) } par(mfrow = c(1, 1))
Performs a Monte-Carlo test on the between-groups inertia percentage.
## S3 method for class 'between' rtest(xtest, nrepet = 99, ...)
## S3 method for class 'between' rtest(xtest, nrepet = 99, ...)
xtest |
an object of class |
nrepet |
the number of permutations |
... |
further arguments passed to or from other methods |
a list of the class rtest
Daniel Chessel
Romesburg, H. C. (1985) Exploring, confirming and randomization tests. Computers and Geosciences, 11, 19–37.
data(meaudret) pca1 <- dudi.pca(meaudret$env, scan = FALSE, nf = 3) rand1 <- rtest(bca(pca1, meaudret$design$season, scan = FALSE), 99) rand1 plot(rand1, main = "Monte-Carlo test")
data(meaudret) pca1 <- dudi.pca(meaudret$env, scan = FALSE, nf = 3) rand1 <- rtest(bca(pca1, meaudret$design$season, scan = FALSE), 99) rand1 plot(rand1, main = "Monte-Carlo test")
Test of the sum of a discriminant analysis eigenvalues (divided by the rank). Non parametric version of the Pillai's test. It authorizes any weighting.
## S3 method for class 'discrimin' rtest(xtest, nrepet = 99, ...)
## S3 method for class 'discrimin' rtest(xtest, nrepet = 99, ...)
xtest |
an object of class |
nrepet |
the number of permutations |
... |
further arguments passed to or from other methods |
returns a list of class rtest
Daniel Chessel
data(meaudret) pca1 <- dudi.pca(meaudret$env, scan = FALSE, nf = 3) rand1 <- rtest(discrimin(pca1, meaudret$design$season, scan = FALSE), 99) rand1 #Monte-Carlo test #Observation: 0.3035 #Call: as.rtest(sim = sim, obs = obs) #Based on 999 replicates #Simulated p-value: 0.001 plot(rand1, main = "Monte-Carlo test") summary.manova(manova(as.matrix(meaudret$env)~meaudret$design$season), "Pillai") # Df Pillai approx F num Df den Df Pr(>F) # meaudret$design$season 3 2.73 11.30 27 30 1.6e-09 *** # Residuals 16 # --- # Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 # 2.731/9 = 0.3034
data(meaudret) pca1 <- dudi.pca(meaudret$env, scan = FALSE, nf = 3) rand1 <- rtest(discrimin(pca1, meaudret$design$season, scan = FALSE), 99) rand1 #Monte-Carlo test #Observation: 0.3035 #Call: as.rtest(sim = sim, obs = obs) #Based on 999 replicates #Simulated p-value: 0.001 plot(rand1, main = "Monte-Carlo test") summary.manova(manova(as.matrix(meaudret$env)~meaudret$design$season), "Pillai") # Df Pillai approx F num Df den Df Pr(>F) # meaudret$design$season 3 2.73 11.30 27 30 1.6e-09 *** # Residuals 16 # --- # Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 # 2.731/9 = 0.3034
performs a Monte-Carlo Test on the sum of eigenvalues of a co-inertia analysis.
RV.randtest(df1, df2, nrepet = 999, ...)
RV.randtest(df1, df2, nrepet = 999, ...)
df1 , df2
|
two data frames with the same rows |
nrepet |
the number of permutations |
... |
further arguments passed to or from other methods |
returns a list of class 'randtest'
Daniel Chessel and Jean Thioulouse
Heo, M. & Gabriel, K.R. (1997) A permutation test of association between configurations by means of the RV coefficient. Communications in Statistics - Simulation and Computation, 27, 843-856.
data(doubs) pca1 <- dudi.pca(doubs$env, scal = TRUE, scann = FALSE) pca2 <- dudi.pca(doubs$fish, scal = FALSE, scann = FALSE) rv1 <- RV.randtest(pca1$tab, pca2$tab, 99) rv1 plot(rv1)
data(doubs) pca1 <- dudi.pca(doubs$env, scal = TRUE, scann = FALSE) pca2 <- dudi.pca(doubs$fish, scal = FALSE, scann = FALSE) rv1 <- RV.randtest(pca1$tab, pca2$tab, 99) rv1 plot(rv1)
performs a Monte-Carlo Test on the sum of eigenvalues of a co-inertia analysis.
RV.rtest(df1, df2, nrepet = 99, ...)
RV.rtest(df1, df2, nrepet = 99, ...)
df1 , df2
|
two data frames with the same rows |
nrepet |
the number of permutations |
... |
further arguments passed to or from other methods |
returns a list of class 'rtest'
Daniel Chessel
Heo, M. & Gabriel, K.R. (1997) A permutation test of association between configurations by means of the RV coefficient. Communications in Statistics - Simulation and Computation, 27, 843-856.
data(doubs) pca1 <- dudi.pca(doubs$env, scal = TRUE, scann = FALSE) pca2 <- dudi.pca(doubs$fish, scal = FALSE, scann = FALSE) rv1 <- RV.rtest(pca1$tab, pca2$tab, 99) rv1 plot(rv1)
data(doubs) pca1 <- dudi.pca(doubs$env, scal = TRUE, scann = FALSE) pca2 <- dudi.pca(doubs$fish, scal = FALSE, scann = FALSE) rv1 <- RV.rtest(pca1$tab, pca2$tab, 99) rv1 plot(rv1)
performs a RV Test between two distance matrices.
RVdist.randtest(m1, m2, nrepet = 999, ...)
RVdist.randtest(m1, m2, nrepet = 999, ...)
m1 , m2
|
two Euclidean matrices |
nrepet |
the number of permutations |
... |
further arguments passed to or from other methods |
returns a list of class 'randtest'
Daniel Chessel
Heo, M. & Gabriel, K.R. (1997) A permutation test of association between configurations by means of the RV coefficient. Communications in Statistics - Simulation and Computation, 27, 843-856.
performs a Monte-Carlo Test on the sum of eigenvalues of a within-class co-inertia analysis.
RVintra.randtest(df1, df2, fac, nrepet = 999, ...)
RVintra.randtest(df1, df2, fac, nrepet = 999, ...)
df1 , df2
|
two data frames with the same rows |
fac |
the factor defining classes |
nrepet |
the number of permutations |
... |
further arguments passed to or from other methods |
returns a list of class 'randtest'
Daniel Chessel and Jean Thioulouse
Heo, M. & Gabriel, K.R. (1997) A permutation test of association between configurations by means of the RV coefficient. Communications in Statistics - Simulation and Computation, 27, 843-856.
data(meaudret) pca1 <- dudi.pca(meaudret$env, scan = FALSE, nf = 4) pca2 <- dudi.pca(meaudret$spe, scal = FALSE, scan = FALSE, nf = 4) wit1 <- wca(pca1, meaudret$design$season, scan = FALSE, nf = 2) wit2 <- wca(pca2, meaudret$design$season, scan = FALSE, nf = 2) coiw <- coinertia(wit1, wit2, scann = FALSE) rv1 <- RVintra.randtest(pca1$tab, pca2$tab, meaudret$design$season, nrep=999) rv1 plot(rv1)
data(meaudret) pca1 <- dudi.pca(meaudret$env, scan = FALSE, nf = 4) pca2 <- dudi.pca(meaudret$spe, scal = FALSE, scan = FALSE, nf = 4) wit1 <- wca(pca1, meaudret$design$season, scan = FALSE, nf = 2) wit2 <- wca(pca2, meaudret$design$season, scan = FALSE, nf = 2) coiw <- coinertia(wit1, wit2, scann = FALSE) rv1 <- RVintra.randtest(pca1$tab, pca2$tab, meaudret$design$season, nrep=999) rv1 plot(rv1)
performs the scatter diagrams of the projection of a vector basis.
s.arrow(dfxy, xax = 1, yax = 2, label = row.names(dfxy), clabel = 1, pch = 20, cpoint = 0, boxes = TRUE, edge = TRUE, origin = c(0,0), xlim = NULL, ylim = NULL, grid = TRUE, addaxes = TRUE, cgrid = 1, sub = "", csub = 1.25, possub = "bottomleft", pixmap = NULL, contour = NULL, area = NULL, add.plot = FALSE)
s.arrow(dfxy, xax = 1, yax = 2, label = row.names(dfxy), clabel = 1, pch = 20, cpoint = 0, boxes = TRUE, edge = TRUE, origin = c(0,0), xlim = NULL, ylim = NULL, grid = TRUE, addaxes = TRUE, cgrid = 1, sub = "", csub = 1.25, possub = "bottomleft", pixmap = NULL, contour = NULL, area = NULL, add.plot = FALSE)
dfxy |
a data frame containing the two columns for the axes |
xax |
the column number of x in |
yax |
the column number of y in |
label |
a vector of strings of characters for the point labels |
clabel |
if not NULL, a character size for the labels used with par("cex")* |
pch |
if |
cpoint |
a character size for plotting the points, used with par("cex")* |
boxes |
if TRUE, labels are framed |
edge |
a logical value indicating whether the arrows should be plotted |
origin |
the fixed point in the graph space, by default c(0,0) the origin of axes. The arrows begin at |
xlim |
the ranges to be encompassed by the x-axis, if NULL they are computed |
ylim |
the ranges to be encompassed by the y-axis, if NULL they are computed |
grid |
a logical value indicating whether a grid in the background of the plot should be drawn |
addaxes |
a logical value indicating whether the axes should be plotted |
cgrid |
a character size, parameter used with |
sub |
a string of characters to be inserted as legend |
csub |
a character size for the legend, used with |
possub |
a string of characters indicating the legend position ("topleft", "topright", "bottomleft", "bottomright") |
pixmap |
an object 'pixmap' displayed in the map background |
contour |
a data frame with 4 columns to plot the contour of the map : each row gives a segment (x1,y1,x2,y2) |
area |
a data frame of class 'area' to plot a set of surface units in contour |
add.plot |
if TRUE uses the current graphics window |
The matched call.
Daniel Chessel
s.arrow(cbind.data.frame(runif(55,-2,3), runif(55,-3,2)))
s.arrow(cbind.data.frame(runif(55,-2,3), runif(55,-3,2)))
performs the scatter diagrams with polygons of contour by level of a factor.
s.chull(dfxy, fac, xax = 1, yax = 2, optchull = c(0.25, 0.5, 0.75, 1), label = levels(fac), clabel = 1, cpoint = 0, col = rep(1, length(levels(fac))), xlim = NULL, ylim = NULL, grid = TRUE, addaxes = TRUE, origin = c(0,0), include.origin = TRUE, sub = "", csub = 1, possub = "bottomleft", cgrid = 1, pixmap = NULL, contour = NULL, area = NULL, add.plot = FALSE)
s.chull(dfxy, fac, xax = 1, yax = 2, optchull = c(0.25, 0.5, 0.75, 1), label = levels(fac), clabel = 1, cpoint = 0, col = rep(1, length(levels(fac))), xlim = NULL, ylim = NULL, grid = TRUE, addaxes = TRUE, origin = c(0,0), include.origin = TRUE, sub = "", csub = 1, possub = "bottomleft", cgrid = 1, pixmap = NULL, contour = NULL, area = NULL, add.plot = FALSE)
dfxy |
a data frame containing the two columns for the axes |
fac |
a factor partioning the rows of the data frame in classes |
xax |
the column number of x in |
yax |
the column number of y in |
optchull |
the number of convex hulls and their interval |
label |
a vector of strings of characters for the point labels |
clabel |
if not NULL, a character size for the labels, used with |
cpoint |
a character size for plotting the points, used with |
col |
a vector of colors used to draw each class in a different color |
xlim |
the ranges to be encompassed by the x axis, if NULL, they are computed |
ylim |
the ranges to be encompassed by the y axis, if NULL they are computed |
grid |
a logical value indicating whether a grid in the background of the plot should be drawn |
addaxes |
a logical value indicating whether the axes should be plotted |
origin |
the fixed point in the graph space, for example c(0,0) the origin axes |
include.origin |
a logical value indicating whether the point "origin" should be belonged to the graph space |
sub |
a string of characters to be inserted as legend |
csub |
a character size for the legend, used with |
possub |
a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", "bottomright") |
cgrid |
a character size, parameter used with par("cex")* |
pixmap |
an object 'pixmap' displayed in the map background |
contour |
a data frame with 4 columns to plot the contour of the map : each row gives a segment (x1,y1,x2,y2) |
area |
a data frame of class 'area' to plot a set of surface units in contour |
add.plot |
if TRUE uses the current graphics window |
The matched call.
Daniel Chessel
xy <- cbind.data.frame(x = runif(200,-1,1), y = runif(200,-1,1)) posi <- factor(xy$x > 0) : factor(xy$y > 0) coul <- c("black", "red", "green", "blue") if(adegraphicsLoaded()) { s.class(xy, posi, ppoi.cex = 1.5, chullSize = c(0.25, 0.5, 0.75, 1), ellipseSize = 0, starSize = 0, ppoly = list(col = "white", border = coul)) } else { s.chull(xy, posi, cpoi = 1.5, col = coul) }
xy <- cbind.data.frame(x = runif(200,-1,1), y = runif(200,-1,1)) posi <- factor(xy$x > 0) : factor(xy$y > 0) coul <- c("black", "red", "green", "blue") if(adegraphicsLoaded()) { s.class(xy, posi, ppoi.cex = 1.5, chullSize = c(0.25, 0.5, 0.75, 1), ellipseSize = 0, starSize = 0, ppoly = list(col = "white", border = coul)) } else { s.chull(xy, posi, cpoi = 1.5, col = coul) }
performs the scatter diagrams with representation of point classes.
s.class(dfxy, fac, wt = rep(1, length(fac)), xax = 1, yax = 2, cstar = 1, cellipse = 1.5, axesell = TRUE, label = levels(fac), clabel = 1, cpoint = 1, pch = 20, col = rep(1, length(levels(fac))), xlim = NULL, ylim = NULL, grid = TRUE, addaxes = TRUE, origin = c(0,0), include.origin = TRUE, sub = "", csub = 1, possub = "bottomleft", cgrid = 1, pixmap = NULL, contour = NULL, area = NULL, add.plot = FALSE)
s.class(dfxy, fac, wt = rep(1, length(fac)), xax = 1, yax = 2, cstar = 1, cellipse = 1.5, axesell = TRUE, label = levels(fac), clabel = 1, cpoint = 1, pch = 20, col = rep(1, length(levels(fac))), xlim = NULL, ylim = NULL, grid = TRUE, addaxes = TRUE, origin = c(0,0), include.origin = TRUE, sub = "", csub = 1, possub = "bottomleft", cgrid = 1, pixmap = NULL, contour = NULL, area = NULL, add.plot = FALSE)
dfxy |
a data frame containing the two columns for the axes |
fac |
a factor partitioning the rows of the data frame in classes |
wt |
a vector of the point weightings of the data frame used for computing the means (star centers) and the ellipses of dispersion |
xax |
the column number of x in |
yax |
the column number of y in |
cstar |
a number between 0 and 1 which defines the length of the star size |
cellipse |
a positive coefficient for the inertia ellipse size |
axesell |
a logical value indicating whether the ellipse axes should be drawn |
label |
a vector of strings of characters for the point labels |
clabel |
if not NULL, a character size for the labels, used with |
cpoint |
a character size for plotting the points, used with |
pch |
if |
col |
a vector of colors used to draw each class in a different color |
xlim |
the ranges to be encompassed by the x, if NULL they are computed |
ylim |
the ranges to be encompassed by the y, if NULL they are computed |
grid |
a logical value indicating whether a grid in the background of the plot should be drawn |
addaxes |
a logical value indicating whether the axes should be plotted |
origin |
the fixed point in the graph space, for example c(0,0) the origin axes |
include.origin |
a logical value indicating whether the point "origin" should be belonged to the graph space |
sub |
a string of characters to be inserted as legend |
csub |
a character size for the legend, used with |
possub |
a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", "bottomright") |
cgrid |
a character size, parameter used with par("cex")* |
pixmap |
an object 'pixmap' displayed in the map background |
contour |
a data frame with 4 columns to plot the contour of the map : each row gives a segment (x1,y1,x2,y2) |
area |
a data frame of class 'area' to plot a set of surface units in contour |
add.plot |
if TRUE uses the current graphics window |
The matched call.
Daniel Chessel
if(!adegraphicsLoaded()) { xy <- cbind.data.frame(x = runif(200, -1, 1), y = runif(200, -1, 1)) posi <- factor(xy$x > 0) : factor(xy$y > 0) coul <- c("black", "red", "green", "blue") par(mfrow = c(2, 2)) s.class(xy, posi, cpoi = 2) s.class(xy, posi, cell = 0, cstar = 0.5) s.class(xy, posi, cell = 2, axesell = FALSE, csta = 0, col = coul) s.chull(xy, posi, cpoi = 1) par(mfrow = c(1, 1)) ## Not run: data(banque) dudi1 <- dudi.acm(banque, scannf = FALSE) coul = rainbow(length(levels(banque[, 20]))) par(mfrow = c(2, 2)) s.label(dudi1$li, sub = "Factorial map from ACM", csub = 1.5, possub = "topleft") s.class(dudi1$li, banque[, 20], sub = names(banque)[20], possub = "bottomright", cell = 0, cstar = 0.5, cgrid = 0, csub = 1.5) s.class(dudi1$li, banque[, 20], csta = 0, cell = 2, cgrid = 0, clab = 1.5) s.class(dudi1$li, banque[, 20], sub = names(banque)[20], possub = "topright", cgrid = 0, col = coul) par(mfrow = c(1, 1)) par(mfrow = n2mfrow(ncol(banque))) for(i in 1:(ncol(banque))) s.class(dudi1$li, banque[, i], clab = 1.5, sub = names(banque)[i], csub = 2, possub = "topleft", cgrid = 0, csta = 0, cpoi = 0) s.label(dudi1$li, clab = 0, sub = "Common background") par(mfrow = c(1, 1)) ## End(Not run) }
if(!adegraphicsLoaded()) { xy <- cbind.data.frame(x = runif(200, -1, 1), y = runif(200, -1, 1)) posi <- factor(xy$x > 0) : factor(xy$y > 0) coul <- c("black", "red", "green", "blue") par(mfrow = c(2, 2)) s.class(xy, posi, cpoi = 2) s.class(xy, posi, cell = 0, cstar = 0.5) s.class(xy, posi, cell = 2, axesell = FALSE, csta = 0, col = coul) s.chull(xy, posi, cpoi = 1) par(mfrow = c(1, 1)) ## Not run: data(banque) dudi1 <- dudi.acm(banque, scannf = FALSE) coul = rainbow(length(levels(banque[, 20]))) par(mfrow = c(2, 2)) s.label(dudi1$li, sub = "Factorial map from ACM", csub = 1.5, possub = "topleft") s.class(dudi1$li, banque[, 20], sub = names(banque)[20], possub = "bottomright", cell = 0, cstar = 0.5, cgrid = 0, csub = 1.5) s.class(dudi1$li, banque[, 20], csta = 0, cell = 2, cgrid = 0, clab = 1.5) s.class(dudi1$li, banque[, 20], sub = names(banque)[20], possub = "topright", cgrid = 0, col = coul) par(mfrow = c(1, 1)) par(mfrow = n2mfrow(ncol(banque))) for(i in 1:(ncol(banque))) s.class(dudi1$li, banque[, i], clab = 1.5, sub = names(banque)[i], csub = 2, possub = "topleft", cgrid = 0, csta = 0, cpoi = 0) s.label(dudi1$li, clab = 0, sub = "Common background") par(mfrow = c(1, 1)) ## End(Not run) }
performs the scatter diagram of a correlation circle.
s.corcircle(dfxy, xax = 1, yax = 2, label = row.names(df), clabel = 1, grid = TRUE, sub = "", csub = 1, possub = "bottomleft", cgrid = 0, fullcircle = TRUE, box = FALSE, add.plot = FALSE)
s.corcircle(dfxy, xax = 1, yax = 2, label = row.names(df), clabel = 1, grid = TRUE, sub = "", csub = 1, possub = "bottomleft", cgrid = 0, fullcircle = TRUE, box = FALSE, add.plot = FALSE)
dfxy |
a data frame with two coordinates |
xax |
the column number for the x-axis |
yax |
the column number for the y-axis |
label |
a vector of strings of characters for the point labels |
clabel |
if not NULL, a character size for the labels, used with |
grid |
a logical value indicating whether a grid in the background of the plot should be drawn |
sub |
a string of characters to be inserted as legend |
csub |
a character size for the legend, used with |
possub |
a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", "bottomright") |
cgrid |
a character size, parameter used with par("cex")* |
fullcircle |
a logical value indicating whether the complete circle sould be drawn |
box |
a logical value indcating whether a box should be drawn |
add.plot |
if TRUE uses the current graphics window |
The matched call.
Daniel Chessel
if(!adegraphicsLoaded()) { data (olympic) dudi1 <- dudi.pca(olympic$tab, scan = FALSE) # a normed PCA par(mfrow = c(2, 2)) s.corcircle(dudi1$co, lab = names(olympic$tab)) s.corcircle(dudi1$co, cgrid = 0, full = FALSE, clab = 0.8) s.corcircle(dudi1$co, lab = as.character(1:11), cgrid = 2, full = FALSE, sub = "Correlation circle", csub = 2.5, possub = "bottomleft", box = TRUE) s.arrow(dudi1$co, clab = 1) par(mfrow = c(1, 1)) }
if(!adegraphicsLoaded()) { data (olympic) dudi1 <- dudi.pca(olympic$tab, scan = FALSE) # a normed PCA par(mfrow = c(2, 2)) s.corcircle(dudi1$co, lab = names(olympic$tab)) s.corcircle(dudi1$co, cgrid = 0, full = FALSE, clab = 0.8) s.corcircle(dudi1$co, lab = as.character(1:11), cgrid = 2, full = FALSE, sub = "Correlation circle", csub = 2.5, possub = "bottomleft", box = TRUE) s.arrow(dudi1$co, clab = 1) par(mfrow = c(1, 1)) }
performs the scatter diagram of a frequency distribution.
s.distri(dfxy, dfdistri, xax = 1, yax = 2, cstar = 1, cellipse = 1.5, axesell = TRUE, label = names(dfdistri), clabel = 0, cpoint = 1, pch = 20, xlim = NULL, ylim = NULL, grid = TRUE, addaxes = TRUE, origin = c(0,0), include.origin = TRUE, sub = "", csub = 1, possub = "bottomleft", cgrid = 1, pixmap = NULL, contour = NULL, area = NULL, add.plot = FALSE)
s.distri(dfxy, dfdistri, xax = 1, yax = 2, cstar = 1, cellipse = 1.5, axesell = TRUE, label = names(dfdistri), clabel = 0, cpoint = 1, pch = 20, xlim = NULL, ylim = NULL, grid = TRUE, addaxes = TRUE, origin = c(0,0), include.origin = TRUE, sub = "", csub = 1, possub = "bottomleft", cgrid = 1, pixmap = NULL, contour = NULL, area = NULL, add.plot = FALSE)
dfxy |
a data frame containing two columns for the axes |
dfdistri |
a data frame containing the mass distributions in columns |
xax |
the column number for the x-axis |
yax |
the column number for the y-axis |
cstar |
a number between 0 and 1 which defines the length of the star size |
cellipse |
a positive coefficient for the inertia ellipse size |
axesell |
a logical value indicating whether the ellipse axes should be drawn |
label |
a vector of strings of characters for the distribution centers labels |
clabel |
if not NULL, a character size for the labels, used with |
cpoint |
a character size for plotting the points, used with |
pch |
if |
xlim |
the ranges to be encompassed by the x, if NULL they are computed |
ylim |
the ranges to be encompassed by the y, if NULL they are computed |
grid |
a logical value indicating whether a grid in the background of the plot should be drawn |
addaxes |
a logical value indicating whether the axes should be plotted |
origin |
the fixed point in the graph space, for example c(0,0) the origin axes |
include.origin |
a logical value indicating whether the point "origin" should be belonged to the graph space |
sub |
a string of characters to be inserted as legend |
csub |
a character size for the legend, used with |
possub |
a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", "bottomright") |
cgrid |
a character size, parameter used with par("cex")* |
pixmap |
an object 'pixmap' displayed in the map background |
contour |
a data frame with 4 columns to plot the contour of the map : each row gives a segment (x1,y1,x2,y2) |
area |
a data frame of class 'area' to plot a set of surface units in contour |
add.plot |
if TRUE uses the current graphics window |
The matched call.
Daniel Chessel
if(!adegraphicsLoaded()) { xy <- cbind.data.frame(x = runif(200, -1, 1), y = runif(200, -1, 1)) distri <- data.frame(w1 = rpois(200, xy$x * (xy$x > 0))) s.value(xy, distri$w1, cpoi = 1) s.distri(xy, distri, add.p = TRUE) w1 <- as.numeric((xy$x> 0) & (xy$y > 0)) w2 <- ((xy$x > 0) & (xy$y < 0)) * (1 - xy$y) * xy$x w3 <- ((xy$x < 0) & (xy$y > 0)) * (1 - xy$x) * xy$y w4 <- ((xy$x < 0) & (xy$y < 0)) * xy$y * xy$x distri <- data.frame(a = w1 / sum(w1), b = w2 / sum(w2), c = w3 / sum(w3), d = w4 / sum(w4)) s.value(xy, unlist(apply(distri, 1, sum)), cleg = 0, csi = 0.75) s.distri(xy, distri, clab = 2, add.p = TRUE) data(rpjdl) xy <- dudi.coa(rpjdl$fau, scan = FALSE)$li par(mfrow = c(3, 4)) for (i in c(1, 5, 8, 20, 21, 23, 26, 33, 36, 44, 47, 49)) { s.distri(xy, rpjdl$fau[, i], cell = 1.5, sub = rpjdl$frlab[i], csub = 2, cgrid = 1.5)} par(mfrow = c(1, 1)) }
if(!adegraphicsLoaded()) { xy <- cbind.data.frame(x = runif(200, -1, 1), y = runif(200, -1, 1)) distri <- data.frame(w1 = rpois(200, xy$x * (xy$x > 0))) s.value(xy, distri$w1, cpoi = 1) s.distri(xy, distri, add.p = TRUE) w1 <- as.numeric((xy$x> 0) & (xy$y > 0)) w2 <- ((xy$x > 0) & (xy$y < 0)) * (1 - xy$y) * xy$x w3 <- ((xy$x < 0) & (xy$y > 0)) * (1 - xy$x) * xy$y w4 <- ((xy$x < 0) & (xy$y < 0)) * xy$y * xy$x distri <- data.frame(a = w1 / sum(w1), b = w2 / sum(w2), c = w3 / sum(w3), d = w4 / sum(w4)) s.value(xy, unlist(apply(distri, 1, sum)), cleg = 0, csi = 0.75) s.distri(xy, distri, clab = 2, add.p = TRUE) data(rpjdl) xy <- dudi.coa(rpjdl$fau, scan = FALSE)$li par(mfrow = c(3, 4)) for (i in c(1, 5, 8, 20, 21, 23, 26, 33, 36, 44, 47, 49)) { s.distri(xy, rpjdl$fau[, i], cell = 1.5, sub = rpjdl$frlab[i], csub = 2, cgrid = 1.5)} par(mfrow = c(1, 1)) }
performs a scatterplot and the two marginal histograms of each axis.
s.hist(dfxy, xax = 1, yax = 2, cgrid = 1, cbreaks = 2, adjust = 1, ...)
s.hist(dfxy, xax = 1, yax = 2, cgrid = 1, cbreaks = 2, adjust = 1, ...)
dfxy |
a data frame with two coordinates |
xax |
column for the x axis |
yax |
column for the y axis |
cgrid |
a character size, parameter used with |
cbreaks |
a parameter used to define the numbers of cells for the histograms. By default,
two cells are defined for each interval of the grid displayed in |
adjust |
a parameter passed to |
... |
further arguments passed from the |
The matched call.
Daniel Chessel
data(rpjdl) coa1 <- dudi.coa(rpjdl$fau, scannf = FALSE, nf = 4) s.hist(coa1$li) s.hist(coa1$li, cgrid = 2, cbr = 3, adj = 0.5, clab = 0) s.hist(coa1$co, cgrid = 2, cbr = 3, adj = 0.5, clab = 0)
data(rpjdl) coa1 <- dudi.coa(rpjdl$fau, scannf = FALSE, nf = 4) s.hist(coa1$li) s.hist(coa1$li, cgrid = 2, cbr = 3, adj = 0.5, clab = 0) s.hist(coa1$co, cgrid = 2, cbr = 3, adj = 0.5, clab = 0)
performs a scatterplot
s.image(dfxy, z, xax = 1, yax = 2, span = 0.5, xlim = NULL, ylim = NULL, kgrid = 2, scale = TRUE, grid = FALSE, addaxes = FALSE, cgrid = 0, include.origin = FALSE, origin = c(0, 0), sub = "", csub = 1, possub = "topleft", neig = NULL, cneig = 1, image.plot = TRUE, contour.plot = TRUE, pixmap = NULL, contour = NULL, area = NULL, add.plot = FALSE)
s.image(dfxy, z, xax = 1, yax = 2, span = 0.5, xlim = NULL, ylim = NULL, kgrid = 2, scale = TRUE, grid = FALSE, addaxes = FALSE, cgrid = 0, include.origin = FALSE, origin = c(0, 0), sub = "", csub = 1, possub = "topleft", neig = NULL, cneig = 1, image.plot = TRUE, contour.plot = TRUE, pixmap = NULL, contour = NULL, area = NULL, add.plot = FALSE)
dfxy |
a data frame containing the two columns for the axes |
z |
a vector of values on the |
xax |
the column number of x in |
yax |
the column number of y in |
span |
the parameter alpha which controls the degree of smoothing |
xlim |
the ranges to be encompassed by the x-axis, if NULL they are computed |
ylim |
the ranges to be encompassed by the y-axis, if NULL they are computed |
kgrid |
a number of points used to locally estimate the level line through the nodes of the grid,
used by |
scale |
if TRUE, data are centered and reduced |
grid |
if TRUE, the background grid is traced |
addaxes |
a logical value indicating whether the axes should be plotted |
cgrid |
a character size, parameter used with par("cex")* |
include.origin |
a logical value indicating whether the point "origin" should be belonged to the graph space |
origin |
the fixed point in the graph space, for example c(0,0) the origin axes |
sub |
a string of characters to be inserted as legend |
csub |
a character size for the legend, used with |
possub |
a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", "bottomright") |
neig |
an object of class |
cneig |
a size for the neighbouring graph lines used with |
image.plot |
if TRUE, the image is traced |
contour.plot |
if TRUE, the contour lines are plotted |
pixmap |
an object 'pixmap' displayed in the map background |
contour |
a data frame with 4 columns to plot the contour of the map : each row gives a segment (x1,y1,x2,y2) |
area |
a data frame of class 'area' to plot a set of surface units in contour |
add.plot |
if TRUE uses the current graphics window |
The matched call.
Daniel Chessel
if(!adegraphicsLoaded()) { if(requireNamespace("splancs", quietly = TRUE)) { wxy <- data.frame(expand.grid(-3:3, -3:3)) names(wxy) <- c("x", "y") z <- (1 / sqrt(2)) * exp(-(wxy$x ^ 2 + wxy$y ^ 2) / 2) par(mfrow = c(2, 2)) s.value(wxy, z) s.image(wxy, z) s.image(wxy, z, kgrid = 5) s.image(wxy, z, kgrid = 15) par(mfrow = c(1, 1)) } ## Not run: data(t3012) if(requireNamespace("splancs", quietly = TRUE)) { par(mfrow = c(3, 4)) for(k in 1:12) s.image(t3012$xy, scalewt(t3012$temp[, k]), kgrid = 3) par(mfrow = c(1, 1)) } data(elec88) if(requireNamespace("splancs", quietly = TRUE)) { par(mfrow = c(3,4)) for(k in 1:12) s.image(t3012$xy, scalewt(t3012$temp[, k]), kgrid = 3, sub = names(t3012$temp)[k], csub = 3, area = elec88$area) par(mfrow = c(1, 1)) } ## End(Not run) }
if(!adegraphicsLoaded()) { if(requireNamespace("splancs", quietly = TRUE)) { wxy <- data.frame(expand.grid(-3:3, -3:3)) names(wxy) <- c("x", "y") z <- (1 / sqrt(2)) * exp(-(wxy$x ^ 2 + wxy$y ^ 2) / 2) par(mfrow = c(2, 2)) s.value(wxy, z) s.image(wxy, z) s.image(wxy, z, kgrid = 5) s.image(wxy, z, kgrid = 15) par(mfrow = c(1, 1)) } ## Not run: data(t3012) if(requireNamespace("splancs", quietly = TRUE)) { par(mfrow = c(3, 4)) for(k in 1:12) s.image(t3012$xy, scalewt(t3012$temp[, k]), kgrid = 3) par(mfrow = c(1, 1)) } data(elec88) if(requireNamespace("splancs", quietly = TRUE)) { par(mfrow = c(3,4)) for(k in 1:12) s.image(t3012$xy, scalewt(t3012$temp[, k]), kgrid = 3, sub = names(t3012$temp)[k], csub = 3, area = elec88$area) par(mfrow = c(1, 1)) } ## End(Not run) }
performs a scatter of points without labels by a kernel Density Estimation in One or Two Dimensions
s.kde2d(dfxy, xax = 1, yax = 2, pch = 20, cpoint = 1, neig = NULL, cneig = 2, xlim = NULL, ylim = NULL, grid = TRUE, addaxes = TRUE, cgrid = 1, include.origin = TRUE, origin = c(0, 0), sub = "", csub = 1.25, possub = "bottomleft", pixmap = NULL, contour = NULL, area = NULL, add.plot = FALSE)
s.kde2d(dfxy, xax = 1, yax = 2, pch = 20, cpoint = 1, neig = NULL, cneig = 2, xlim = NULL, ylim = NULL, grid = TRUE, addaxes = TRUE, cgrid = 1, include.origin = TRUE, origin = c(0, 0), sub = "", csub = 1.25, possub = "bottomleft", pixmap = NULL, contour = NULL, area = NULL, add.plot = FALSE)
dfxy |
a data frame with at least two coordinates |
xax |
the column number for the x-axis |
yax |
the column number for the y-axis |
pch |
if |
cpoint |
a character size for plotting the points, used with
|
neig |
a neighbouring graph |
cneig |
a size for the neighbouring graph lines used with
par("lwd")* |
xlim |
the ranges to be encompassed by the x axis, if NULL, they are computed |
ylim |
the ranges to be encompassed by the y axis, if NULL, they are computed |
grid |
a logical value indicating whether a grid in the background of the plot should be drawn |
addaxes |
a logical value indicating whether the axes should be plotted |
cgrid |
a character size, parameter used with par("cex")* 'cgrid' to indicate the mesh of the grid |
include.origin |
a logical value indicating whether the point "origin" should be belonged to the graph space |
origin |
the fixed point in the graph space, for example c(0,0) the origin axes |
sub |
a string of characters to be inserted as legend |
csub |
a character size for the legend, used with |
possub |
a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", "bottomright") |
pixmap |
an object |
contour |
a data frame with 4 columns to plot the contour of the map : each row gives a segment (x1,y1,x2,y2) |
area |
a data frame of class 'area' to plot a set of surface units in contour |
add.plot |
if TRUE uses the current graphics window |
The matched call.
Daniel Chessel
# To recognize groups of points if(!adegraphicsLoaded()) { data(rpjdl) coa1 <- dudi.coa(rpjdl$fau, scannf = FALSE, nf = 3) s.kde2d(coa1$li) }
# To recognize groups of points if(!adegraphicsLoaded()) { data(rpjdl) coa1 <- dudi.coa(rpjdl$fau, scannf = FALSE, nf = 3) s.kde2d(coa1$li) }
performs the scatter diagrams with labels.
s.label(dfxy, xax = 1, yax = 2, label = row.names(dfxy), clabel = 1, pch = 20, cpoint = if (clabel == 0) 1 else 0, boxes = TRUE, neig = NULL, cneig = 2, xlim = NULL, ylim = NULL, grid = TRUE, addaxes = TRUE, cgrid = 1, include.origin = TRUE, origin = c(0,0), sub = "", csub = 1.25, possub = "bottomleft", pixmap = NULL, contour = NULL, area = NULL, add.plot = FALSE)
s.label(dfxy, xax = 1, yax = 2, label = row.names(dfxy), clabel = 1, pch = 20, cpoint = if (clabel == 0) 1 else 0, boxes = TRUE, neig = NULL, cneig = 2, xlim = NULL, ylim = NULL, grid = TRUE, addaxes = TRUE, cgrid = 1, include.origin = TRUE, origin = c(0,0), sub = "", csub = 1.25, possub = "bottomleft", pixmap = NULL, contour = NULL, area = NULL, add.plot = FALSE)
dfxy |
a data frame with at least two coordinates |
xax |
the column number for the x-axis |
yax |
the column number for the y-axis |
label |
a vector of strings of characters for the point labels |
clabel |
if not NULL, a character size for the labels, used with |
pch |
if |
cpoint |
a character size for plotting the points, used with |
boxes |
if TRUE, labels are framed |
neig |
a neighbouring graph |
cneig |
a size for the neighbouring graph lines used with par("lwd")* |
xlim |
the ranges to be encompassed by the x axis, if NULL, they are computed |
ylim |
the ranges to be encompassed by the y axis, if NULL, they are computed |
grid |
a logical value indicating whether a grid in the background of the plot should be drawn |
addaxes |
a logical value indicating whether the axes should be plotted |
cgrid |
a character size, parameter used with par("cex")* |
include.origin |
a logical value indicating whether the point "origin" should be belonged to the graph space |
origin |
the fixed point in the graph space, for example c(0,0) the origin axes |
sub |
a string of characters to be inserted as legend |
csub |
a character size for the legend, used with |
possub |
a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", "bottomright") |
pixmap |
an object 'pixmap' displayed in the map background |
contour |
a data frame with 4 columns to plot the contour of the map : each row gives a segment (x1,y1,x2,y2) |
area |
a data frame of class 'area' to plot a set of surface units in contour |
add.plot |
if TRUE uses the current graphics window |
The matched call.
Daniel Chessel
if(!adegraphicsLoaded()) { layout(matrix(c(1, 2, 3, 2), 2, 2)) data(atlas) s.label(atlas$xy, lab = atlas$names.district, area = atlas$area, inc = FALSE, addax = FALSE) data(irishdata) s.label(irishdata$xy, inc = FALSE, contour = irishdata$contour, addax = FALSE) par(mfrow = c(2, 2)) cha <- ls() s.label(cbind.data.frame(runif(length(cha)), runif(length(cha))), lab = cha) x <- runif(50, -2, 2) y <- runif(50, -2, 2) z <- x^2 + y^2 s.label(data.frame(x, y), lab = as.character(z < 1)) s.label(data.frame(x, y), clab = 0, cpoi = 1, add.plot = TRUE) symbols(0, 0, circles = 1, add = TRUE, inch = FALSE) s.label(cbind.data.frame(runif(100, 0, 10), runif(100, 5, 12)), incl = FALSE, clab = 0) s.label(cbind.data.frame(runif(100, -3, 12), runif(100, 2, 10)), cl = 0, cp = 2, include = FALSE) }
if(!adegraphicsLoaded()) { layout(matrix(c(1, 2, 3, 2), 2, 2)) data(atlas) s.label(atlas$xy, lab = atlas$names.district, area = atlas$area, inc = FALSE, addax = FALSE) data(irishdata) s.label(irishdata$xy, inc = FALSE, contour = irishdata$contour, addax = FALSE) par(mfrow = c(2, 2)) cha <- ls() s.label(cbind.data.frame(runif(length(cha)), runif(length(cha))), lab = cha) x <- runif(50, -2, 2) y <- runif(50, -2, 2) z <- x^2 + y^2 s.label(data.frame(x, y), lab = as.character(z < 1)) s.label(data.frame(x, y), clab = 0, cpoi = 1, add.plot = TRUE) symbols(0, 0, circles = 1, add = TRUE, inch = FALSE) s.label(cbind.data.frame(runif(100, 0, 10), runif(100, 5, 12)), incl = FALSE, clab = 0) s.label(cbind.data.frame(runif(100, -3, 12), runif(100, 2, 10)), cl = 0, cp = 2, include = FALSE) }
performs the scatter diagrams using pictures to represent the points
s.logo(dfxy, listlogo, klogo=NULL, clogo=1, rectlogo=TRUE, xax = 1, yax = 2, neig = NULL, cneig = 1, xlim = NULL, ylim = NULL, grid = TRUE, addaxes = TRUE, cgrid = 1, include.origin = TRUE, origin = c(0, 0), sub = "", csub = 1.25, possub = "bottomleft", pixmap = NULL, contour = NULL, area = NULL, add.plot = FALSE)
s.logo(dfxy, listlogo, klogo=NULL, clogo=1, rectlogo=TRUE, xax = 1, yax = 2, neig = NULL, cneig = 1, xlim = NULL, ylim = NULL, grid = TRUE, addaxes = TRUE, cgrid = 1, include.origin = TRUE, origin = c(0, 0), sub = "", csub = 1.25, possub = "bottomleft", pixmap = NULL, contour = NULL, area = NULL, add.plot = FALSE)
dfxy |
a data frame with at least two coordinates |
listlogo |
a list of pixmap pictures |
klogo |
a numeric vector giving the order in which pictures of listlogo are used; if NULL, the order is the same than the rows of dfxy |
clogo |
a numeric vector giving the size factor applied to each picture |
rectlogo |
a logical to decide whether a rectangle should be drawn around the picture (TRUE) or not (FALSE) |
xax |
the column number for the x-axis |
yax |
the column number for the y-axis |
neig |
a neighbouring graph |
cneig |
a size for the neighbouring graph lines used with par("lwd")* |
xlim |
the ranges to be encompassed by the x axis, if NULL, they are computed |
ylim |
the ranges to be encompassed by the y axis, if NULL, they are computed |
grid |
a logical value indicating whether a grid in the background of the plot should be drawn |
addaxes |
a logical value indicating whether the axes should be plotted |
cgrid |
a character size, parameter used with par("cex")* |
include.origin |
a logical value indicating whether the point "origin" should be belonged to the graph space |
origin |
the fixed point in the graph space, for example c(0,0) the origin axes |
sub |
a string of characters to be inserted as legend |
csub |
a character size for the legend, used with |
possub |
a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", "bottomright") |
pixmap |
an object 'pixmap' displayed in the map background |
contour |
a data frame with 4 columns to plot the contour of the map : each row gives a segment (x1,y1,x2,y2) |
area |
a data frame of class 'area' to plot a set of surface units in contour |
add.plot |
if TRUE uses the current graphics window |
The matched call.
Daniel Chessel and Thibaut Jombart [email protected]
if(requireNamespace("pixmap", quietly = TRUE) & requireNamespace("sp", quietly = TRUE)) { if(!adegraphicsLoaded()) { data(ggtortoises) a1 <- ggtortoises$area area.plot(a1) rect(min(a1$x), min(a1$y), max(a1$x), max(a1$y), col = "lightblue") invisible(lapply(split(a1, a1$id), function(x) polygon(x[, -1],col = "white"))) s.label(ggtortoises$misc, grid = FALSE, include.ori = FALSE, addaxes = FALSE, add.p = TRUE) listico <- ggtortoises$ico[as.character(ggtortoises$pop$carap)] s.logo(ggtortoises$pop, listico, add.p = TRUE) } else { data(capitales, package = "ade4") # 'capitales' data doesn't work with ade4 anymore g3 <- s.logo(capitales$xy[sort(rownames(capitales$xy)), ], capitales$logo, Sp = capitales$Spatial, pbackground.col = "lightblue", pSp.col = "white", pgrid.draw = FALSE) } }
if(requireNamespace("pixmap", quietly = TRUE) & requireNamespace("sp", quietly = TRUE)) { if(!adegraphicsLoaded()) { data(ggtortoises) a1 <- ggtortoises$area area.plot(a1) rect(min(a1$x), min(a1$y), max(a1$x), max(a1$y), col = "lightblue") invisible(lapply(split(a1, a1$id), function(x) polygon(x[, -1],col = "white"))) s.label(ggtortoises$misc, grid = FALSE, include.ori = FALSE, addaxes = FALSE, add.p = TRUE) listico <- ggtortoises$ico[as.character(ggtortoises$pop$carap)] s.logo(ggtortoises$pop, listico, add.p = TRUE) } else { data(capitales, package = "ade4") # 'capitales' data doesn't work with ade4 anymore g3 <- s.logo(capitales$xy[sort(rownames(capitales$xy)), ], capitales$logo, Sp = capitales$Spatial, pbackground.col = "lightblue", pSp.col = "white", pgrid.draw = FALSE) } }
performs the scatter diagram for a paired coordinates.
s.match(df1xy, df2xy, xax = 1, yax = 2, pch = 20, cpoint = 1, label = row.names(df1xy), clabel=1, edge = TRUE, xlim = NULL, ylim = NULL, grid = TRUE, addaxes = TRUE, cgrid = 1, include.origin = TRUE, origin = c(0,0), sub = "", csub = 1.25, possub = "bottomleft", pixmap = NULL, contour = NULL, area = NULL, add.plot = FALSE)
s.match(df1xy, df2xy, xax = 1, yax = 2, pch = 20, cpoint = 1, label = row.names(df1xy), clabel=1, edge = TRUE, xlim = NULL, ylim = NULL, grid = TRUE, addaxes = TRUE, cgrid = 1, include.origin = TRUE, origin = c(0,0), sub = "", csub = 1.25, possub = "bottomleft", pixmap = NULL, contour = NULL, area = NULL, add.plot = FALSE)
df1xy |
a data frame containing two columns from the first system |
df2xy |
a data frame containing two columns from teh second system |
xax |
the column number for the x-axis of both the two systems |
yax |
the column number for the y-axis of both the two systems |
pch |
if |
cpoint |
a character size for plotting the points, used with |
label |
a vector of strings of characters for the couple labels |
clabel |
if not NULL, a character size for the labels, used with |
edge |
If TRUE the arrows are plotted, otherwise only the segments are drawn |
xlim |
the ranges to be encompassed by the x axis, if NULL they are computed |
ylim |
the ranges to be encompassed by the y axis, if NULL they are computed |
grid |
a logical value indicating whether a grid in the background of the plot should be drawn |
addaxes |
a logical value indicating whether the axes should be plotted |
cgrid |
a character size, parameter used with par("cex")* |
include.origin |
a logical value indicating whether the point "origin" should be belonged to the graph space |
origin |
the fixed point in the graph space, for example c(0,0) the origin axes |
sub |
a string of characters to be inserted as legend |
csub |
a character size for the legend, used with |
possub |
a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", "bottomright") |
pixmap |
aan object |
contour |
a data frame with 4 columns to plot the contour of the map : each row gives a segment (x1,y1,x2,y2) |
area |
a data frame of class 'area' to plot a set of surface units in contour |
add.plot |
if TRUE uses the current graphics window |
The matched call.
Daniel Chessel
if(!adegraphicsLoaded()) { X <- data.frame(x = runif(50, -1, 2), y = runif(50, -1, 2)) Y <- X + rnorm(100, sd = 0.3) par(mfrow = c(2, 2)) s.match(X, Y) s.match(X, Y, edge = FALSE, clab = 0) s.match(X, Y, edge = FALSE, clab = 0) s.label(X, clab = 1, add.plot = TRUE) s.label(Y, clab = 0.75, add.plot = TRUE) s.match(Y, X, clab = 0) par(mfrow = c(1, 1)) }
if(!adegraphicsLoaded()) { X <- data.frame(x = runif(50, -1, 2), y = runif(50, -1, 2)) Y <- X + rnorm(100, sd = 0.3) par(mfrow = c(2, 2)) s.match(X, Y) s.match(X, Y, edge = FALSE, clab = 0) s.match(X, Y, edge = FALSE, clab = 0) s.label(X, clab = 1, add.plot = TRUE) s.label(Y, clab = 0.75, add.plot = TRUE) s.match(Y, X, clab = 0) par(mfrow = c(1, 1)) }
Performs a graphical representation of two sets of coordinates (different colors and symbols) and a partitionning into classes
s.match.class(df1xy, df2xy, fac, wt = rep(1/nrow(df1xy), nrow(df1xy)), xax = 1, yax = 2, pch1 = 16, pch2 = 15, col1 = rep("lightgrey", nlevels(fac)), col2 = rep("darkgrey", nlevels(fac)), cpoint = 1, label = levels(fac), clabel = 1, cstar = 1, cellipse = 0, axesell = TRUE, xlim = NULL, ylim = NULL, grid = TRUE, addaxes = TRUE, cgrid = 1, include.origin = TRUE, origin = c(0, 0), sub = "", csub = 1.25, possub = "bottomleft", pixmap = NULL, contour = NULL, area = NULL, add.plot = FALSE)
s.match.class(df1xy, df2xy, fac, wt = rep(1/nrow(df1xy), nrow(df1xy)), xax = 1, yax = 2, pch1 = 16, pch2 = 15, col1 = rep("lightgrey", nlevels(fac)), col2 = rep("darkgrey", nlevels(fac)), cpoint = 1, label = levels(fac), clabel = 1, cstar = 1, cellipse = 0, axesell = TRUE, xlim = NULL, ylim = NULL, grid = TRUE, addaxes = TRUE, cgrid = 1, include.origin = TRUE, origin = c(0, 0), sub = "", csub = 1.25, possub = "bottomleft", pixmap = NULL, contour = NULL, area = NULL, add.plot = FALSE)
df1xy |
a dataframe with the first system of coordinates |
df2xy |
a dataframe with the secocnd system of coordinates |
fac |
a factor partitioning the rows of the data frame in classes |
wt |
a vector of weights |
xax |
a number indicating which column should be plotted on the x-axis |
yax |
a number indicating which column should be plotted on the x-axis |
pch1 |
if |
pch2 |
if |
col1 |
a color for symbols |
col2 |
a color for symbols |
cpoint |
a character size for plotting the points, used with |
label |
a vector of strings of characters for the couple labels |
clabel |
if not NULL, a character size for the labels, used with |
cstar |
a number between 0 and 1 which defines the length of the star size |
cellipse |
a positive coefficient for the inertia ellipse size |
axesell |
a logical value indicating whether the ellipse axes should be drawn |
xlim |
the ranges to be encompassed by the x axis, if NULL they are computed |
ylim |
the ranges to be encompassed by the y axis, if NULL they are computed |
grid |
a logical value indicating whether a grid in the background of the plot should be drawn |
addaxes |
a logical value indicating whether the axes should be plotted |
cgrid |
a character size, parameter used with par("cex")* |
include.origin |
a logical value indicating whether the point "origin" should belong to the graph space |
origin |
a fixed point in the graph space, for example c(0,0) for the origin of axes |
sub |
a string of characters to be inserted as legend |
csub |
a character size for the legend, used with |
possub |
a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", "bottomright") |
pixmap |
a |
contour |
a dataframe with 4 columns to plot the contour of the map : each row gives a segment (x1,y1,x2,y2) |
area |
a dataframe of class 'area' to plot an areal map |
add.plot |
if TRUE, add the plot to the current graphic device |
The matched call.
Stéphane Dray [email protected]
xy <- data.frame(matrix(rnorm(100), 50, 2)) xy[, 1] <- xy[, 1] + rep(seq(0, 12, by = 3), rep(10, 5)) xy[, 2] <- xy[, 2] + rep(seq(0, 12, by = 3), rep(10, 5)) fac <- gl(5, 10) xy2 <- xy + matrix(rnorm(100), 50, 2) + 1 if(adegraphicsLoaded()) { mat <- rbind(xy, xy2) minmat <- apply(mat, 2, min) maxmat <- apply(mat, 2, max) lag <- 0.1 * abs(minmat - maxmat) xli <- c(minmat[1] - lag[1], maxmat[1] + lag[1]) yli <- c(minmat[2] - lag[2], maxmat[2] + lag[2]) g1 <- s.class(xy, fac, ellipseSize = 0, col = rep("grey45", nlevels(fac)), xlim = xli, ylim = yli, plabels.cex = 0, plot = FALSE) g2 <- s.class(xy2, fac, ellipseSize = 0, col = rep("grey75", nlevels(fac)), xlim = xli, ylim = yli, plabels.cex = 0, plot = FALSE) g3 <- s.match(g1@stats$means, g2@stats$means, xlim = xli, ylim = yli, plines.lwd = 2, psub.text = "xy -> xy2", plot = FALSE) g4 <- do.call("superpose", list(g1, g2)) g4@Call <- call("superpose", g1@Call, g2@Call) g4 <- do.call("superpose", list(g4, g3)) g4@Call <- call("superpose", g4@Call, g3@Call) g4 } else { s.match.class(xy, xy2, fac) }
xy <- data.frame(matrix(rnorm(100), 50, 2)) xy[, 1] <- xy[, 1] + rep(seq(0, 12, by = 3), rep(10, 5)) xy[, 2] <- xy[, 2] + rep(seq(0, 12, by = 3), rep(10, 5)) fac <- gl(5, 10) xy2 <- xy + matrix(rnorm(100), 50, 2) + 1 if(adegraphicsLoaded()) { mat <- rbind(xy, xy2) minmat <- apply(mat, 2, min) maxmat <- apply(mat, 2, max) lag <- 0.1 * abs(minmat - maxmat) xli <- c(minmat[1] - lag[1], maxmat[1] + lag[1]) yli <- c(minmat[2] - lag[2], maxmat[2] + lag[2]) g1 <- s.class(xy, fac, ellipseSize = 0, col = rep("grey45", nlevels(fac)), xlim = xli, ylim = yli, plabels.cex = 0, plot = FALSE) g2 <- s.class(xy2, fac, ellipseSize = 0, col = rep("grey75", nlevels(fac)), xlim = xli, ylim = yli, plabels.cex = 0, plot = FALSE) g3 <- s.match(g1@stats$means, g2@stats$means, xlim = xli, ylim = yli, plines.lwd = 2, psub.text = "xy -> xy2", plot = FALSE) g4 <- do.call("superpose", list(g1, g2)) g4@Call <- call("superpose", g1@Call, g2@Call) g4 <- do.call("superpose", list(g4, g3)) g4@Call <- call("superpose", g4@Call, g3@Call) g4 } else { s.match.class(xy, xy2, fac) }
The main purpose of this function is to draw categories using scores and profiles by their gravity center. Confidence intervals of the average position (issued from a multinomial distribution) can be superimposed.
s.multinom(dfxy, dfrowprof, translate = FALSE, xax = 1, yax = 2, labelcat = row.names(dfxy), clabelcat = 1, cpointcat = if (clabelcat == 0) 2 else 0, labelrowprof = row.names(dfrowprof), clabelrowprof = 0.75, cpointrowprof = if (clabelrowprof == 0) 2 else 0, pchrowprof = 20, coulrowprof = grey(0.8), proba = 0.95, n.sample = apply(dfrowprof, 1, sum), axesell = TRUE, ...)
s.multinom(dfxy, dfrowprof, translate = FALSE, xax = 1, yax = 2, labelcat = row.names(dfxy), clabelcat = 1, cpointcat = if (clabelcat == 0) 2 else 0, labelrowprof = row.names(dfrowprof), clabelrowprof = 0.75, cpointrowprof = if (clabelrowprof == 0) 2 else 0, pchrowprof = 20, coulrowprof = grey(0.8), proba = 0.95, n.sample = apply(dfrowprof, 1, sum), axesell = TRUE, ...)
dfxy |
|
dfrowprof |
|
translate |
a logical value indicating whether the plot should be translated(TRUE) or not. The origin becomes the gravity center weighted by profiles. |
xax |
the column number of |
yax |
the column number of |
labelcat |
a vector of strings of characters for the labels of categories |
clabelcat |
an integer specifying the character size for the labels of categories,
used with |
cpointcat |
an integer specifying the character size for the points showing the categories,
used with |
labelrowprof |
a vector of strings of characters for the labels of profiles (rows of |
clabelrowprof |
an integer specifying the character size for the labels of profiles used with par("cex")*clabelrowprof |
cpointrowprof |
an integer specifying the character size for the points representative of the profiles used with par("cex")*cpointrowprof |
pchrowprof |
either an integer specifying a symbol or a single character to be used for the profile labels |
coulrowprof |
a vector of colors used for ellipses, possibly recycled |
proba |
a value lying between 0.500 and 0.999 to draw a confidence interval |
n.sample |
a vector containing the sample size, possibly recycled. Used |
axesell |
a logical value indicating whether the ellipse axes should be drawn |
... |
further arguments passed from the |
Returns in a hidden way a list of three components :
tra |
a vector with two values giving the done original translation. |
ell |
a matrix, with 5 columns and for rows the number of profiles, giving the means,
the variances and the covariance of the profile for the used
numerical codes (column of |
call |
the matched call |
Daniel Chessel
par(mfrow = c(2,2)) par(mar = c(0.1,0.1,0.1,0.1)) proba <- matrix(c(0.49,0.47,0.04,0.4,0.3,0.3,0.05,0.05,0.9,0.05,0.7,0.25), ncol = 3, byrow = TRUE) proba.df <- as.data.frame (proba) names(proba.df) <- c("A","B","C") ; row.names(proba.df) <- c("P1","P2","P3","P4") w.proba <- triangle.plot(proba.df, clab = 2, show = FALSE) box() w.tri = data.frame(x = c(-sqrt(1/2),sqrt(1/2),0), y = c(-1/sqrt(6),-1/sqrt(6),2/sqrt(6))) L3 <- c("A","B","C") row.names(w.tri) <- L3 s.multinom(w.tri, proba.df, n.sample = 0, coulrowprof = "black", clabelrowprof = 1.5) s.multinom(w.tri, proba.df, n.sample = 30, coul = palette()[5]) s.multinom(w.tri, proba.df, n.sample = 60, coul = palette()[6], add.p = TRUE) s.multinom(w.tri, proba.df, n.sample = 120, coul = grey(0.8), add.p = TRUE) print(s.multinom(w.tri, proba.df[-3,], n.sample = 0, translate = TRUE)$tra)
par(mfrow = c(2,2)) par(mar = c(0.1,0.1,0.1,0.1)) proba <- matrix(c(0.49,0.47,0.04,0.4,0.3,0.3,0.05,0.05,0.9,0.05,0.7,0.25), ncol = 3, byrow = TRUE) proba.df <- as.data.frame (proba) names(proba.df) <- c("A","B","C") ; row.names(proba.df) <- c("P1","P2","P3","P4") w.proba <- triangle.plot(proba.df, clab = 2, show = FALSE) box() w.tri = data.frame(x = c(-sqrt(1/2),sqrt(1/2),0), y = c(-1/sqrt(6),-1/sqrt(6),2/sqrt(6))) L3 <- c("A","B","C") row.names(w.tri) <- L3 s.multinom(w.tri, proba.df, n.sample = 0, coulrowprof = "black", clabelrowprof = 1.5) s.multinom(w.tri, proba.df, n.sample = 30, coul = palette()[5]) s.multinom(w.tri, proba.df, n.sample = 60, coul = palette()[6], add.p = TRUE) s.multinom(w.tri, proba.df, n.sample = 120, coul = grey(0.8), add.p = TRUE) print(s.multinom(w.tri, proba.df[-3,], n.sample = 0, translate = TRUE)$tra)
performs the scatter diagram with trajectories.
s.traject(dfxy, fac = factor(rep(1, nrow(dfxy))), ord = (1:length(fac)), xax = 1, yax = 2, label = levels(fac), clabel = 1, cpoint = 1, pch = 20, xlim = NULL, ylim = NULL, grid = TRUE, addaxes = TRUE, edge = TRUE, origin = c(0,0), include.origin = TRUE, sub = "", csub = 1, possub = "bottomleft", cgrid = 1, pixmap = NULL, contour = NULL, area = NULL, add.plot = FALSE)
s.traject(dfxy, fac = factor(rep(1, nrow(dfxy))), ord = (1:length(fac)), xax = 1, yax = 2, label = levels(fac), clabel = 1, cpoint = 1, pch = 20, xlim = NULL, ylim = NULL, grid = TRUE, addaxes = TRUE, edge = TRUE, origin = c(0,0), include.origin = TRUE, sub = "", csub = 1, possub = "bottomleft", cgrid = 1, pixmap = NULL, contour = NULL, area = NULL, add.plot = FALSE)
dfxy |
a data frame containing two columns for the axes |
fac |
a factor partioning the rows of the data frame in classes |
ord |
a vector of length equal to fac. The trajectory is drawn in an ascending order of the ord values |
xax |
the column number for the x-axis |
yax |
the column number for the y-axis |
label |
a vector of strings of characters for the point labels |
clabel |
if not NULL, a character size for the labels, used with |
cpoint |
a character size for plotting the points, used with |
pch |
if |
xlim |
the ranges to be encompassed by the x, if NULL they are computed |
ylim |
the ranges to be encompassed by the y, if NULL they are computed |
grid |
a logical value indicating whether a grid in the background of the plot should be drawn |
addaxes |
a logical value indicating whether the axes should be plotted |
edge |
if TRUE the arrows are plotted, otherwhise only the segments |
origin |
the fixed point in the graph space, for example c(0,0) the origin axes |
include.origin |
a logical value indicating whether the point "origin" should be belonged to the graph space |
sub |
a string of characters to be inserted as legend |
csub |
a character size for the legend, used with |
possub |
a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", "bottomright") |
cgrid |
a character size, parameter used with |
pixmap |
aan object 'pixmap' displayed in the map background |
contour |
a data frame with 4 columns to plot the contour of the map : each row gives a segment (x1,y1,x2,y2) |
area |
a data frame of class 'area' to plot a set of surface units in contour |
add.plot |
if TRUE uses the current graphics window |
The matched call.
Daniel Chessel
if(!adegraphicsLoaded()) { rw <- function(a) { x <- 0 for(i in 1:49) x <- c(x, x[length(x)] + runif(1, -1, 1)) x } y <- unlist(lapply(1:5, rw)) x <- unlist(lapply(1:5, rw)) z <- gl(5, 50) s.traject(data.frame(x, y), z, edge = FALSE) }
if(!adegraphicsLoaded()) { rw <- function(a) { x <- 0 for(i in 1:49) x <- c(x, x[length(x)] + runif(1, -1, 1)) x } y <- unlist(lapply(1:5, rw)) x <- unlist(lapply(1:5, rw)) z <- gl(5, 50) s.traject(data.frame(x, y), z, edge = FALSE) }
performs the scatter diagram with the representation of a value for a variable
s.value(dfxy, z, xax = 1, yax = 2, method = c("squaresize", "greylevel"), zmax=NULL, csize = 1, cpoint = 0, pch = 20, clegend = 0.75, neig = NULL, cneig = 1, xlim = NULL, ylim = NULL, grid = TRUE, addaxes = TRUE, cgrid = 0.75, include.origin = TRUE, origin = c(0,0), sub = "", csub = 1, possub = "topleft", pixmap = NULL, contour = NULL, area = NULL, add.plot = FALSE)
s.value(dfxy, z, xax = 1, yax = 2, method = c("squaresize", "greylevel"), zmax=NULL, csize = 1, cpoint = 0, pch = 20, clegend = 0.75, neig = NULL, cneig = 1, xlim = NULL, ylim = NULL, grid = TRUE, addaxes = TRUE, cgrid = 0.75, include.origin = TRUE, origin = c(0,0), sub = "", csub = 1, possub = "topleft", pixmap = NULL, contour = NULL, area = NULL, add.plot = FALSE)
dfxy |
a data frame with two coordinates |
z |
a vector of the values corresponding to the rows of |
xax |
column for the x axis |
yax |
column for the y axis |
method |
a string of characters |
zmax |
a numeric value, equal by default to max(abs(z)), can be used to impose a common scale of the size of the squares to several drawings in the same device |
csize |
a size coefficient for symbols |
cpoint |
a character size for plotting the points, used with |
pch |
if |
clegend |
a character size for the legend used by |
neig |
a neighbouring graph |
cneig |
a size for the neighbouring graph lines used with |
xlim |
the ranges to be encompassed by the x, if NULL they are computed |
ylim |
the ranges to be encompassed by the y, if NULL they are computed |
grid |
a logical value indicating whether a grid in the background of the plot should be drawn |
addaxes |
a logical value indicating whether the axes should be plotted |
cgrid |
a character size, parameter used with |
include.origin |
a logical value indicating whether the point "origin" should be belonged to the graph space |
origin |
the fixed point in the graph space, for example c(0,0) the origin axes |
sub |
a string of characters to be inserted as legend |
csub |
a character size for the legend, used with |
possub |
a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", "bottomright") |
pixmap |
an object 'pixmap' displayed in the map background |
contour |
a data frame with 4 columns to plot the contour of the map : each row gives a segment (x1,y1,x2,y2) |
area |
a data frame of class 'area' to plot a set of surface units in contour |
add.plot |
if TRUE uses the current graphics window |
The matched call.
Daniel Chessel
if(!adegraphicsLoaded()) { xy <- cbind.data.frame(x = runif(500), y = runif(500)) z <- rnorm(500) s.value(xy, z) s.value(xy, z, method = "greylevel") data(rpjdl) fau.coa <- dudi.coa(rpjdl$fau, scan = FALSE, nf = 3) s.value(fau.coa$li, fau.coa$li[,3], csi = 0.75, cleg = 0.75) data(irishdata) par(mfrow = c(3, 4)) irq0 <- data.frame(scale(irishdata$tab, scale = TRUE)) for (i in 1:12) { z <- irq0[, i] nam <- names(irq0)[i] s.value(irishdata$xy, z, area = irishdata$area, csi = 3, csub = 2, sub = nam, cleg = 1.5, cgrid = 0, inc = FALSE, xlim = c(16, 205), ylim = c(-50, 268), adda = FALSE, grid = FALSE) } }
if(!adegraphicsLoaded()) { xy <- cbind.data.frame(x = runif(500), y = runif(500)) z <- rnorm(500) s.value(xy, z) s.value(xy, z, method = "greylevel") data(rpjdl) fau.coa <- dudi.coa(rpjdl$fau, scan = FALSE, nf = 3) s.value(fau.coa$li, fau.coa$li[,3], csi = 0.75, cleg = 0.75) data(irishdata) par(mfrow = c(3, 4)) irq0 <- data.frame(scale(irishdata$tab, scale = TRUE)) for (i in 1:12) { z <- irq0[, i] nam <- names(irq0)[i] s.value(irishdata$xy, z, area = irishdata$area, csi = 3, csub = 2, sub = nam, cleg = 1.5, cgrid = 0, inc = FALSE, xlim = c(16, 205), ylim = c(-50, 268), adda = FALSE, grid = FALSE) } }
This data set gives the densities per hectare of 11 species of trees for 10 transects of topographic moisture values (mean of several stations per class).
data(santacatalina)
data(santacatalina)
a data frame with 11 rows and 10 columns
Gauch, H. G. J., Chase, G. B. and Whittaker R. H. (1974) Ordination of vegetation samples by Gaussian species distributions. Ecology, 55, 1382–1390.
data(santacatalina) coa1 <- dudi.coa(log(santacatalina + 1), scan = FALSE) # 2 factors if(adegraphicsLoaded()) { g1 <- table.value(log(santacatalina + 1), plot = FALSE) g2 <- table.value(log(santacatalina + 1)[, sample(10)], plot = FALSE) g3 <- table.value(log(santacatalina + 1)[order(coa1$li[, 1]), order(coa1$co[, 1])], plot = FALSE) g4 <- scatter(coa1, posi = "bottomright", plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) table.value(log(santacatalina + 1)) table.value(log(santacatalina + 1)[, sample(10)]) table.value(log(santacatalina + 1)[order(coa1$li[, 1]), order(coa1$co[, 1])]) scatter(coa1, posi = "bottomright") par(mfrow = c(1, 1)) }
data(santacatalina) coa1 <- dudi.coa(log(santacatalina + 1), scan = FALSE) # 2 factors if(adegraphicsLoaded()) { g1 <- table.value(log(santacatalina + 1), plot = FALSE) g2 <- table.value(log(santacatalina + 1)[, sample(10)], plot = FALSE) g3 <- table.value(log(santacatalina + 1)[order(coa1$li[, 1]), order(coa1$co[, 1])], plot = FALSE) g4 <- scatter(coa1, posi = "bottomright", plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) table.value(log(santacatalina + 1)) table.value(log(santacatalina + 1)[, sample(10)]) table.value(log(santacatalina + 1)[order(coa1$li[, 1]), order(coa1$co[, 1])]) scatter(coa1, posi = "bottomright") par(mfrow = c(1, 1)) }
The data frame sarcelles$tab
contains the number of the winter teals
(Anas C. Crecca) for which the ring was retrieved in the area i
during the month j (n=3049).
data(sarcelles)
data(sarcelles)
sarcelles
is a list with the following components:
a data frame with 14 rows-areas and 12 columns-months
a data frame with the 2 spatial coordinates of the 14 region centers
a vector containing the month items
a neighborhood object (class nb
defined in package spdep
)
Lebreton, J.D. (1973). Etude des déplacements saisonniers des Sarcelles d'hiver, Anas c. crecca L., hivernant en Camargue à l'aide de l'analyse factorielle des correspondances. Compte rendu hebdomadaire des séances de l'Académie des sciences, Paris, D, III, 277, 2417–2420.
## Not run: if(!adegraphicsLoaded()) { # depends of pixmap if(requireNamespace("pixmap", quietly = TRUE)) { bkgnd.pnm <- pixmap::read.pnm(system.file("pictures/sarcelles.pnm", package = "ade4")) data(sarcelles) par(mfrow = c(4, 3)) for(i in 1:12) { s.distri(sarcelles$xy, sarcelles$tab[, i], pixmap = bkgnd.pnm, sub = sarcelles$col.names[i], clab = 0, csub = 2) s.value(sarcelles$xy, sarcelles$tab[, i], add.plot = TRUE, cleg = 0) } par(mfrow = c(1, 1)) } } ## End(Not run)
## Not run: if(!adegraphicsLoaded()) { # depends of pixmap if(requireNamespace("pixmap", quietly = TRUE)) { bkgnd.pnm <- pixmap::read.pnm(system.file("pictures/sarcelles.pnm", package = "ade4")) data(sarcelles) par(mfrow = c(4, 3)) for(i in 1:12) { s.distri(sarcelles$xy, sarcelles$tab[, i], pixmap = bkgnd.pnm, sub = sarcelles$col.names[i], clab = 0, csub = 2) s.value(sarcelles$xy, sarcelles$tab[, i], add.plot = TRUE, cleg = 0) } par(mfrow = c(1, 1)) } } ## End(Not run)
These utility functions compute (weighted) means, variances and covariances for dataframe partitioned by a factor. The scale transforms a numeric matrix in a centred and scaled matrix for any weighting.
covwt(x, wt, na.rm = FALSE) varwt(x, wt, na.rm = FALSE) scalewt(df, wt = rep(1/nrow(df), nrow(df)), center = TRUE, scale = TRUE) meanfacwt(df, fac = NULL, wt = rep(1/nrow(df), nrow(df)), drop = FALSE) varfacwt(df, fac = NULL, wt = rep(1/nrow(df), nrow(df)), drop = FALSE) covfacwt(df, fac = NULL, wt = rep(1/nrow(df), nrow(df)), drop = FALSE) scalefacwt(df, fac = NULL, wt = rep(1/nrow(df), nrow(df)), scale = TRUE, drop = FALSE)
covwt(x, wt, na.rm = FALSE) varwt(x, wt, na.rm = FALSE) scalewt(df, wt = rep(1/nrow(df), nrow(df)), center = TRUE, scale = TRUE) meanfacwt(df, fac = NULL, wt = rep(1/nrow(df), nrow(df)), drop = FALSE) varfacwt(df, fac = NULL, wt = rep(1/nrow(df), nrow(df)), drop = FALSE) covfacwt(df, fac = NULL, wt = rep(1/nrow(df), nrow(df)), drop = FALSE) scalefacwt(df, fac = NULL, wt = rep(1/nrow(df), nrow(df)), scale = TRUE, drop = FALSE)
x |
a numeric vector ( |
na.rm |
a logical value indicating whether NA values should be stripped before the computation proceeds. |
df |
a matrix or a dataframe containing the data. |
fac |
a factor partitioning the data. |
wt |
a numeric vector of weights. |
drop |
a logical value indicating whether unused levels should be kept. |
scale |
a logical value indicating whether data should be scaled or not. |
center |
a logical value indicating whether data should be centered or not. |
Functions returns biased estimates of variances and covariances (i.e. divided by n and not n-1)
For varwt
, the weighted variance. For covwt
,
the matrix of weighted co-variances. For scalewt
, the scaled
dataframe. For other function a list (if fac
is not null) of dataframes with approriate values
Stéphane Dray [email protected]
data(meau) w <- rowSums(meau$spe) varwt(meau$env, w) varfacwt(meau$env, wt = w) varfacwt(meau$env, wt = w, fac = meau$design$season) covfacwt(meau$env, wt = w, fac = meau$design$season) scalewt(meau$env, wt = w)
data(meau) w <- rowSums(meau$spe) varwt(meau$env, w) varfacwt(meau$env, wt = w) varfacwt(meau$env, wt = w, fac = meau$design$season) covfacwt(meau$env, wt = w, fac = meau$design$season) scalewt(meau$env, wt = w)
scatter
is a generic function that has methods for the classes
coa
, dudi
, fca
, acm
and pco
.
It plots the outputs of a multivariate analysis by representing
simultaneously the rows and the colums of the original table
(biplot). The function biplot
returns exactly the same
representation.
The function screeplot
represents the amount of inertia (usually
variance) associated to each dimension.
scatter(x, ...) ## S3 method for class 'dudi' biplot(x, ...) ## S3 method for class 'dudi' screeplot(x, npcs = length(x$eig), type = c("barplot", "lines"), main = deparse(substitute(x)), col = c(rep("black", x$nf), rep("grey", npcs - x$nf)), ...)
scatter(x, ...) ## S3 method for class 'dudi' biplot(x, ...) ## S3 method for class 'dudi' screeplot(x, npcs = length(x$eig), type = c("barplot", "lines"), main = deparse(substitute(x)), col = c(rep("black", x$nf), rep("grey", npcs - x$nf)), ...)
x |
an object of the class |
npcs |
the number of components to be plotted |
type |
the type of plot |
main |
the title of the plot |
col |
a vector of colors |
... |
further arguments passed to or from other methods |
Daniel Chessel
Stéphane Dray [email protected]
s.arrow
, s.chull
, s.class
,
s.corcircle
, s.distri
, s.label
,
s.match
, s.traject
, s.value
, add.scatter
data(rpjdl) rpjdl.coa <- dudi.coa(rpjdl$fau, scannf = FALSE, nf = 4) screeplot(rpjdl.coa) biplot(rpjdl.coa)
data(rpjdl) rpjdl.coa <- dudi.coa(rpjdl$fau, scannf = FALSE, nf = 4) screeplot(rpjdl.coa) biplot(rpjdl.coa)
performs the scatter diagrams of a Multiple Correspondence Analysis.
## S3 method for class 'acm' scatter(x, xax = 1, yax = 2, mfrow=NULL, csub = 2, possub = "topleft", ...)
## S3 method for class 'acm' scatter(x, xax = 1, yax = 2, mfrow=NULL, csub = 2, possub = "topleft", ...)
x |
an object of class |
xax |
the column number for the x-axis |
yax |
the column number for the y-axis |
mfrow |
a vector of the form "c(nr,nc)", if NULL (the default) is
computed by |
csub |
a character size for the legend, used with |
possub |
a string of characters indicating the legend position ("topleft", "topright", "bottomleft", "bottomright") in a array of figures |
... |
further arguments passed to or from other methods |
Daniel Chessel
data(lascaux) if(adegraphicsLoaded()) { plot(dudi.acm(lascaux$ornem, sca = FALSE)) } else { scatter(dudi.acm(lascaux$ornem, sca = FALSE), csub = 3) }
data(lascaux) if(adegraphicsLoaded()) { plot(dudi.acm(lascaux$ornem, sca = FALSE)) } else { scatter(dudi.acm(lascaux$ornem, sca = FALSE), csub = 3) }
performs the scatter diagrams of a correspondence analysis.
## S3 method for class 'coa' scatter(x, xax = 1, yax = 2, method = 1:3, clab.row = 0.75, clab.col = 1.25, posieig = "top", sub = NULL, csub = 2, ...)
## S3 method for class 'coa' scatter(x, xax = 1, yax = 2, method = 1:3, clab.row = 0.75, clab.col = 1.25, posieig = "top", sub = NULL, csub = 2, ...)
x |
an object of class |
xax |
the column number for the x-axis |
yax |
the column number for the y-axis |
method |
an integer between 1 and 3 |
clab.row |
a character size for the rows |
clab.col |
a character size for the columns |
posieig |
if "top", the eigenvalues bar plot is upside; if "bottom", it is downside; if "none", no plot |
sub |
a string of characters to be inserted as legend |
csub |
a character size for the legend, used with |
... |
further arguments passed to or from other methods |
Daniel Chessel
Oksanen, J. (1987) Problems of joint display of species and site scores in correspondence analysis. Vegetatio, 72, 51–57.
data(housetasks) w <- dudi.coa(housetasks, scan = FALSE) if(adegraphicsLoaded()) { g1 <- scatter(w, method = 1, psub.text = "1 / Standard", posieig = "none", plot = FALSE) g2 <- scatter(w, method = 2, psub.text = "2 / Columns -> averaging -> Rows", posieig = "none", plot = FALSE) g3 <- scatter(w, method = 3, psub.text = "3 / Rows -> averaging -> Columns ", posieig = "none", plot = FALSE) G <- ADEgS(list(g1, g2, g3), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) scatter(w, method = 1, sub = "1 / Standard", posieig = "none") scatter(w, method = 2, sub = "2 / Columns -> averaging -> Rows", posieig = "none") scatter(w, method = 3, sub = "3 / Rows -> averaging -> Columns ", posieig = "none") par(mfrow = c(1, 1)) }
data(housetasks) w <- dudi.coa(housetasks, scan = FALSE) if(adegraphicsLoaded()) { g1 <- scatter(w, method = 1, psub.text = "1 / Standard", posieig = "none", plot = FALSE) g2 <- scatter(w, method = 2, psub.text = "2 / Columns -> averaging -> Rows", posieig = "none", plot = FALSE) g3 <- scatter(w, method = 3, psub.text = "3 / Rows -> averaging -> Columns ", posieig = "none", plot = FALSE) G <- ADEgS(list(g1, g2, g3), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) scatter(w, method = 1, sub = "1 / Standard", posieig = "none") scatter(w, method = 2, sub = "2 / Columns -> averaging -> Rows", posieig = "none") scatter(w, method = 3, sub = "3 / Rows -> averaging -> Columns ", posieig = "none") par(mfrow = c(1, 1)) }
performs the scatter diagrams of objects of class dudi
.
## S3 method for class 'dudi' scatter(x, xax = 1, yax = 2, clab.row = 0.75, clab.col = 1, permute = FALSE, posieig = "top", sub = NULL, ...)
## S3 method for class 'dudi' scatter(x, xax = 1, yax = 2, clab.row = 0.75, clab.col = 1, permute = FALSE, posieig = "top", sub = NULL, ...)
x |
an object of class |
xax |
the column number for the x-axis |
yax |
the column number for the y-axis |
clab.row |
a character size for the rows |
clab.col |
a character size for the columns |
permute |
if FALSE, the rows are plotted by points and the columns by arrows. If TRUE it is the opposite. |
posieig |
if "top" the eigenvalues bar plot is upside, if "bottom" it is downside, if "none" no plot |
sub |
a string of characters to be inserted as legend |
... |
further arguments passed to or from other methods |
scatter.dudi
is a factorial map of individuals and the projection of the vectors of the canonical basis multiplied by a constante of rescaling. In the eigenvalues bar plot,the used axes for the plot are in black, the other kept axes in grey and the other in white.
The permute
argument can be used to choose between the distance biplot (default) and the correlation biplot (permute = TRUE).
Daniel Chessel
data(deug) scatter(dd1 <- dudi.pca(deug$tab, scannf = FALSE, nf = 4), posieig = "bottomright") data(rhone) dd1 <- dudi.pca(rhone$tab, nf = 4, scann = FALSE) if(adegraphicsLoaded()) { scatter(dd1, row.psub.text = "Principal component analysis") } else { scatter(dd1, sub = "Principal component analysis") }
data(deug) scatter(dd1 <- dudi.pca(deug$tab, scannf = FALSE, nf = 4), posieig = "bottomright") data(rhone) dd1 <- dudi.pca(rhone$tab, nf = 4, scann = FALSE) if(adegraphicsLoaded()) { scatter(dd1, row.psub.text = "Principal component analysis") } else { scatter(dd1, sub = "Principal component analysis") }
performs the scatter diagrams of a fuzzy correspondence analysis.
## S3 method for class 'fca' scatter(x, xax = 1, yax = 2, clab.moda = 1, labels = names(x$tab), sub = NULL, csub = 2, ...)
## S3 method for class 'fca' scatter(x, xax = 1, yax = 2, clab.moda = 1, labels = names(x$tab), sub = NULL, csub = 2, ...)
x |
an object of class |
xax |
the column number for the x-axis |
yax |
the column number for the y-axis |
clab.moda |
the character size to write the modalities |
labels |
a vector of strings of characters for the labels of the modalities |
sub |
a vector of strings of characters to be inserted as legend in each figure |
csub |
a character size for the legend, used with |
... |
further arguments passed to or from other methods |
Daniel Chessel
Chevenet, F., Dolédec, S. and Chessel, D. (1994) A fuzzy coding approach for the analysis of long-term ecological data. Freshwater Biology, 31, 295–309.
data(coleo) coleo.fuzzy <- prep.fuzzy.var(coleo$tab, coleo$col.blocks) fca1 <- dudi.fca(coleo.fuzzy, sca = FALSE, nf = 3) if(adegraphicsLoaded()) { plot(fca1) } else { scatter(fca1, labels = coleo$moda.names, clab.moda = 1.5, sub = names(coleo$col.blocks), csub = 3) }
data(coleo) coleo.fuzzy <- prep.fuzzy.var(coleo$tab, coleo$col.blocks) fca1 <- dudi.fca(coleo.fuzzy, sca = FALSE, nf = 3) if(adegraphicsLoaded()) { plot(fca1) } else { scatter(fca1, labels = coleo$moda.names, clab.moda = 1.5, sub = names(coleo$col.blocks), csub = 3) }
These are utilities used in graphical functions.
The functions scatter use some utilities functions :
defines the layer of the plot for all scatters
defines the layer of the plot for sco functions
plots the polygons of the external contour
plots the eigenvalues bar plot
plots an inertia ellipse for a weighting distribution
puts labels on a correlation circle
puts labels centred on the points
plots a grid and adds a legend
puts a legend of values by square size
puts a legend by squares and grey levels
adds a legend of grey levels for the areas
to fit a plot on a background bipmap
plots a star for a weighting distribution
adds a string of characters in sub-title of a graph
is used to rotate labels
Daniel Chessel, Stéphane Dray [email protected]
s.arrow
, s.chull
, s.class
,
s.corcircle
, s.distri
, s.label
,
s.match
, s.traject
, s.value
, add.scatter
par(mfrow = c(3,3)) plot.new() ade4:::scatterutil.legendgris(1:20, 4, 1.6) plot.new() ade4:::scatterutil.sub("lkn5555555555lkn", csub = 2, possub = "bottomleft") ade4:::scatterutil.sub("lkn5555555555lkn", csub = 1, possub = "topleft") ade4:::scatterutil.sub("jdjjl", csub = 3, possub = "topright") ade4:::scatterutil.sub("**", csub = 2, possub = "bottomright") x <- c(0.5,0.2,-0.5,-0.2) ; y <- c(0.2,0.5,-0.2,-0.5) eti <- c("toto", "kjbk", "gdgiglgl", "sdfg") plot(x, y, xlim = c(-1,1), ylim = c(-1,1)) ade4:::scatterutil.eti.circ(x, y, eti, 2.5) abline(0, 1, lty = 2) ; abline(0, -1, lty = 2) x <- c(0.5,0.2,-0.5,-0.2) ; y <- c(0.2,0.5,-0.2,-0.5) eti <- c("toto", "kjbk", "gdgiglgl", "sdfg") plot(x, y, xlim = c(-1,1), ylim = c(-1,1)) ade4:::scatterutil.eti(x, y, eti, 1.5) plot(runif(10,-3,5), runif(10,-1,1), asp = 1) ade4:::scatterutil.grid(2) abline(h = 0, v = 0, lwd = 3) x <- runif(10,0,1) ; y <- rnorm(10) ; z <- rep(1,10) plot(x,y) ; ade4:::scatterutil.star(x, y, z, 0.5) plot(x,y) ; ade4:::scatterutil.star(x, y, z, 1) x <- c(runif(10,0,0.5), runif(10,0.5,1)) y <- runif(20) plot(x, y, asp = 1) # asp=1 is essential to have perpendicular axes ade4:::scatterutil.ellipse(x, y, rep(c(1,0), c(10,10)), cell = 1.5, ax = TRUE) ade4:::scatterutil.ellipse(x, y, rep(c(0,1), c(10,10)), cell = 1.5, ax = TRUE) x <- c(runif(100,0,0.75), runif(100,0.25,1)) y <- c(runif(100,0,0.75), runif(100,0.25,1)) z <- factor(rep(c(1,2), c(100,100))) plot(x, y, pch = rep(c(1,20), c(100,100))) ade4:::scatterutil.chull(x, y, z, opt = c(0.25,0.50,0.75,1)) par(mfrow = c(1,1))
par(mfrow = c(3,3)) plot.new() ade4:::scatterutil.legendgris(1:20, 4, 1.6) plot.new() ade4:::scatterutil.sub("lkn5555555555lkn", csub = 2, possub = "bottomleft") ade4:::scatterutil.sub("lkn5555555555lkn", csub = 1, possub = "topleft") ade4:::scatterutil.sub("jdjjl", csub = 3, possub = "topright") ade4:::scatterutil.sub("**", csub = 2, possub = "bottomright") x <- c(0.5,0.2,-0.5,-0.2) ; y <- c(0.2,0.5,-0.2,-0.5) eti <- c("toto", "kjbk", "gdgiglgl", "sdfg") plot(x, y, xlim = c(-1,1), ylim = c(-1,1)) ade4:::scatterutil.eti.circ(x, y, eti, 2.5) abline(0, 1, lty = 2) ; abline(0, -1, lty = 2) x <- c(0.5,0.2,-0.5,-0.2) ; y <- c(0.2,0.5,-0.2,-0.5) eti <- c("toto", "kjbk", "gdgiglgl", "sdfg") plot(x, y, xlim = c(-1,1), ylim = c(-1,1)) ade4:::scatterutil.eti(x, y, eti, 1.5) plot(runif(10,-3,5), runif(10,-1,1), asp = 1) ade4:::scatterutil.grid(2) abline(h = 0, v = 0, lwd = 3) x <- runif(10,0,1) ; y <- rnorm(10) ; z <- rep(1,10) plot(x,y) ; ade4:::scatterutil.star(x, y, z, 0.5) plot(x,y) ; ade4:::scatterutil.star(x, y, z, 1) x <- c(runif(10,0,0.5), runif(10,0.5,1)) y <- runif(20) plot(x, y, asp = 1) # asp=1 is essential to have perpendicular axes ade4:::scatterutil.ellipse(x, y, rep(c(1,0), c(10,10)), cell = 1.5, ax = TRUE) ade4:::scatterutil.ellipse(x, y, rep(c(0,1), c(10,10)), cell = 1.5, ax = TRUE) x <- c(runif(100,0,0.75), runif(100,0.25,1)) y <- c(runif(100,0,0.75), runif(100,0.25,1)) z <- factor(rep(c(1,2), c(100,100))) plot(x, y, pch = rep(c(1,20), c(100,100))) ade4:::scatterutil.chull(x, y, z, opt = c(0.25,0.50,0.75,1)) par(mfrow = c(1,1))
represents the link between a variable and a set of qualitative variables.
sco.boxplot(score, df, labels = names(df), clabel = 1, xlim = NULL, grid = TRUE, cgrid = 0.75, include.origin = TRUE, origin = 0, sub = NULL, csub = 1)
sco.boxplot(score, df, labels = names(df), clabel = 1, xlim = NULL, grid = TRUE, cgrid = 0.75, include.origin = TRUE, origin = 0, sub = NULL, csub = 1)
score |
a numeric vector |
df |
a data frame with only factors |
labels |
a vector of strings of characters for the labels of variables |
clabel |
if not NULL, a character size for the labels, used with |
xlim |
the ranges to be encompassed by the x axis, if NULL they are computed |
grid |
a logical value indicating whether the scale vertical lines should be drawn |
cgrid |
a character size, parameter used with |
include.origin |
a logical value indicating whether the point "origin" should be belonged to the graph space |
origin |
the fixed point in the graph space, for example 0 the origin axis |
sub |
a string of characters to be inserted as legend |
csub |
a character size for the legend, used with |
Daniel Chessel
w1 <- rnorm(100,-1) w2 <- rnorm(100) w3 <- rnorm(100,1) f1 <- gl(3,100) f2 <- gl(30,10) sco.boxplot(c(w1,w2,w3), data.frame(f1,f2)) data(banque) banque.acm <- dudi.acm(banque, scan = FALSE, nf = 4) par(mfrow = c(1,3)) sco.boxplot(banque.acm$l1[,1], banque[,1:7], clab = 1.8) sco.boxplot(banque.acm$l1[,1], banque[,8:14], clab = 1.8) sco.boxplot(banque.acm$l1[,1], banque[,15:21], clab = 1.8) par(mfrow = c(1,1))
w1 <- rnorm(100,-1) w2 <- rnorm(100) w3 <- rnorm(100,1) f1 <- gl(3,100) f2 <- gl(30,10) sco.boxplot(c(w1,w2,w3), data.frame(f1,f2)) data(banque) banque.acm <- dudi.acm(banque, scan = FALSE, nf = 4) par(mfrow = c(1,3)) sco.boxplot(banque.acm$l1[,1], banque[,1:7], clab = 1.8) sco.boxplot(banque.acm$l1[,1], banque[,8:14], clab = 1.8) sco.boxplot(banque.acm$l1[,1], banque[,15:21], clab = 1.8) par(mfrow = c(1,1))
Draws evenly spaced labels, each label linked to the corresponding values of the levels of a factor.
sco.class(score, fac, label = levels(fac), clabel = 1, horizontal = TRUE, reverse = FALSE, pos.lab = 0.5, pch = 20, cpoint = 1, boxes = TRUE, col = rep(1, length(levels(fac))), lim = NULL, grid = TRUE, cgrid = 1, include.origin = TRUE, origin = c(0, 0), sub = "", csub = 1.25, possub = "bottomleft")
sco.class(score, fac, label = levels(fac), clabel = 1, horizontal = TRUE, reverse = FALSE, pos.lab = 0.5, pch = 20, cpoint = 1, boxes = TRUE, col = rep(1, length(levels(fac))), lim = NULL, grid = TRUE, cgrid = 1, include.origin = TRUE, origin = c(0, 0), sub = "", csub = 1.25, possub = "bottomleft")
score |
a numeric vector |
fac |
a factor |
label |
labels for the levels of the factor |
clabel |
a character size for the labels, used with
|
horizontal |
logical. If TRUE, the plot is horizontal |
reverse |
logical. If horizontal = TRUE and reverse=TRUE, the plot is at the bottom, if reverse = FALSE, the plot is at the top. If horizontal = FALSE, the plot is at the right (TRUE) or at the left (FALSE). |
pos.lab |
a values between 0 and 1 to manage the position of the labels. |
pch |
an integer specifying the symbol or the single character to be used in plotting points |
cpoint |
a character size for plotting the points, used with |
boxes |
if TRUE, labels are framed |
col |
a vector of colors used to draw each class in a different color |
lim |
the range for the x axis or y axis (if horizontal = FALSE), if NULL, they are computed |
grid |
a logical value indicating whether a grid in the background of the plot should be drawn |
cgrid |
a character size, parameter used with par("cex")* |
include.origin |
a logical value indicating whether the point "origin" should belong to the plot |
origin |
the fixed point in the graph space, for example c(0,0) the origin axes |
sub |
a string of characters to be inserted as legend |
csub |
a character size for the legend, used with |
possub |
a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", "bottomright") |
The matched call.
Stéphane Dray [email protected]
data(meau) envpca <- dudi.pca(meau$env, scannf=FALSE) par(mfrow=c(2,1)) sco.class(envpca$li[,1],meau$design$season, col = 1:6) sco.class(envpca$li[,1],meau$design$season, col = 1:4, reverse = TRUE)
data(meau) envpca <- dudi.pca(meau$env, scannf=FALSE) par(mfrow=c(2,1)) sco.class(envpca$li[,1],meau$design$season, col = 1:6) sco.class(envpca$li[,1],meau$design$season, col = 1:4, reverse = TRUE)
represents the mean- standard deviation of a set of weight distributions on a numeric score.
sco.distri(score, df, y.rank = TRUE, csize = 1, labels = names(df), clabel = 1, xlim = NULL, grid = TRUE, cgrid = 0.75, include.origin = TRUE, origin = 0, sub = NULL, csub = 1)
sco.distri(score, df, y.rank = TRUE, csize = 1, labels = names(df), clabel = 1, xlim = NULL, grid = TRUE, cgrid = 0.75, include.origin = TRUE, origin = 0, sub = NULL, csub = 1)
score |
a numeric vector |
df |
a data frame with only positive or null values |
y.rank |
a logical value indicating whether the means should be classified in ascending order |
csize |
an integer indicating the size segment |
labels |
a vector of strings of characters for the labels of the variables |
clabel |
if not NULL, a character size for the labels, used with |
xlim |
the ranges to be encompassed by the x axis, if NULL they are computed |
grid |
a logical value indicating whether the scale vertical lines should be drawn |
cgrid |
a character size, parameter used with |
include.origin |
a logical value indicating whether the point "origin" should be belonged to the graph space |
origin |
the fixed point in the graph space, for example c(0,0) the origin axes |
sub |
a string of characters to be inserted as legend |
csub |
a character size for the legend, used with |
returns an invisible data.frame with means and variances
Daniel Chessel
if(!adegraphicsLoaded()) { w <- seq(-1, 1, le = 200) distri <- data.frame(lapply(1:50, function(x) sample((200:1)) * ((w >= (- x / 50)) & (w <= x / 50)))) names(distri) <- paste("w", 1:50, sep = "") par(mfrow = c(1, 2)) sco.distri(w, distri, csi = 1.5) sco.distri(w, distri, y.rank = FALSE, csi = 1.5) par(mfrow = c(1, 1)) data(rpjdl) coa2 <- dudi.coa(rpjdl$fau, FALSE) sco.distri(coa2$li[, 1], rpjdl$fau, lab = rpjdl$frlab, clab = 0.8) data(doubs) par(mfrow = c(2, 2)) poi.coa <- dudi.coa(doubs$fish, scann = FALSE) sco.distri(poi.coa$l1[, 1], doubs$fish) poi.nsc <- dudi.nsc(doubs$fish, scann = FALSE) sco.distri(poi.nsc$l1[, 1], doubs$fish) s.label(poi.coa$l1) s.label(poi.nsc$l1) data(rpjdl) fau.coa <- dudi.coa(rpjdl$fau, scann = FALSE) sco.distri(fau.coa$l1[,1], rpjdl$fau) fau.nsc <- dudi.nsc(rpjdl$fau, scann = FALSE) sco.distri(fau.nsc$l1[,1], rpjdl$fau) s.label(fau.coa$l1) s.label(fau.nsc$l1) par(mfrow = c(1, 1)) }
if(!adegraphicsLoaded()) { w <- seq(-1, 1, le = 200) distri <- data.frame(lapply(1:50, function(x) sample((200:1)) * ((w >= (- x / 50)) & (w <= x / 50)))) names(distri) <- paste("w", 1:50, sep = "") par(mfrow = c(1, 2)) sco.distri(w, distri, csi = 1.5) sco.distri(w, distri, y.rank = FALSE, csi = 1.5) par(mfrow = c(1, 1)) data(rpjdl) coa2 <- dudi.coa(rpjdl$fau, FALSE) sco.distri(coa2$li[, 1], rpjdl$fau, lab = rpjdl$frlab, clab = 0.8) data(doubs) par(mfrow = c(2, 2)) poi.coa <- dudi.coa(doubs$fish, scann = FALSE) sco.distri(poi.coa$l1[, 1], doubs$fish) poi.nsc <- dudi.nsc(doubs$fish, scann = FALSE) sco.distri(poi.nsc$l1[, 1], doubs$fish) s.label(poi.coa$l1) s.label(poi.nsc$l1) data(rpjdl) fau.coa <- dudi.coa(rpjdl$fau, scann = FALSE) sco.distri(fau.coa$l1[,1], rpjdl$fau) fau.nsc <- dudi.nsc(rpjdl$fau, scann = FALSE) sco.distri(fau.nsc$l1[,1], rpjdl$fau) s.label(fau.coa$l1) s.label(fau.nsc$l1) par(mfrow = c(1, 1)) }
Draws Gauss curves with the same mean and variance as the scores of indivivuals belonging to categories of several qualitative variables.
sco.gauss(score, df, xlim = NULL, steps = 200, ymax = NULL, sub = names(df), csub = 1.25, possub = "topleft", legen =TRUE, label = row.names(df), clabel = 1, grid = TRUE, cgrid = 1, include.origin = TRUE, origin = c(0, 0))
sco.gauss(score, df, xlim = NULL, steps = 200, ymax = NULL, sub = names(df), csub = 1.25, possub = "topleft", legen =TRUE, label = row.names(df), clabel = 1, grid = TRUE, cgrid = 1, include.origin = TRUE, origin = c(0, 0))
score |
a numeric vector |
df |
a dataframe containing only factors, number of rows equal to the length of the score vector |
xlim |
starting point and end point for drawing the Gauss curves |
steps |
number of segments for drawing the Gauss curves |
ymax |
max ordinate for all Gauss curves. If NULL, ymax is computed and different for each factor |
sub |
vector of strings of characters for the lables of qualitative variables |
csub |
character size for the legend |
possub |
a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", "bottomright") |
legen |
if TRUE, the first graphic of the series displays the score with evenly spaced labels (see |
label |
labels for the score |
clabel |
a character size for the labels, used with
|
grid |
a logical value indicating whether a grid in the background of the plot should be drawn |
cgrid |
a character size, parameter used with par("cex")* |
include.origin |
a logical value indicating whether the point "origin" should belong to the plot |
origin |
the fixed point in the graph space, for example c(0,0) the origin axes |
Takes one vector containing quantitative values (score) and one dataframe containing only factors that give categories to wich the quantitative values belong. Computes the mean and variance of the values in each category of each factor, and draws a Gauss curve with the same mean and variance for each category of each factor. Can optionaly set the start and end point of the curves and the number of segments. The max ordinate (ymax) can also be set arbitrarily to set a common max for all factors (else the max is different for each factor).
The matched call.
Jean Thioulouse, Stéphane Dray [email protected]
data(meau) envpca <- dudi.pca(meau$env, scannf=FALSE) dffac <- cbind.data.frame(meau$design$season, meau$design$site) sco.gauss(envpca$li[,1], dffac, clabel = 2, csub = 2)
data(meau) envpca <- dudi.pca(meau$env, scannf=FALSE) dffac <- cbind.data.frame(meau$design$season, meau$design$site) sco.gauss(envpca$li[,1], dffac, clabel = 2, csub = 2)
Draws evenly spaced labels, each label linked to the corresponding value of a numeric score.
sco.label(score, label = names(score), clabel = 1, horizontal = TRUE, reverse = FALSE, pos.lab = 0.5, pch = 20, cpoint = 1, boxes = TRUE, lim = NULL, grid = TRUE, cgrid = 1, include.origin = TRUE, origin = c(0, 0), sub = "", csub = 1.25, possub = "bottomleft")
sco.label(score, label = names(score), clabel = 1, horizontal = TRUE, reverse = FALSE, pos.lab = 0.5, pch = 20, cpoint = 1, boxes = TRUE, lim = NULL, grid = TRUE, cgrid = 1, include.origin = TRUE, origin = c(0, 0), sub = "", csub = 1.25, possub = "bottomleft")
score |
a numeric vector |
label |
labels for the score |
clabel |
a character size for the labels, used with
|
horizontal |
logical. If TRUE, the plot is horizontal |
reverse |
logical. If horizontal = TRUE and reverse=TRUE, the plot is at the bottom, if reverse = FALSE, the plot is at the top. If horizontal = FALSE, the plot is at the right (TRUE) or at the left (FALSE). |
pos.lab |
a values between 0 and 1 to manage the position of the labels. |
pch |
an integer specifying the symbol or the single character to be used in plotting points |
cpoint |
a character size for plotting the points, used with |
boxes |
if TRUE, labels are framed |
lim |
the range for the x axis or y axis (if horizontal = FALSE), if NULL, they are computed |
grid |
a logical value indicating whether a grid in the background of the plot should be drawn |
cgrid |
a character size, parameter used with par("cex")* |
include.origin |
a logical value indicating whether the point "origin" should belong to the plot |
origin |
the fixed point in the graph space, for example c(0,0) the origin axes |
sub |
a string of characters to be inserted as legend |
csub |
a character size for the legend, used with |
possub |
a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", "bottomright") |
The matched call.
Stéphane Dray [email protected], Jean Thioulouse
data(meau) envpca <- dudi.pca(meau$env, scannf=FALSE) par(mfrow=c(2,1)) sco.label(envpca$l1[,1], row.names(envpca$l1), lim=c(-1,3.5)) sco.label(envpca$co[,1], row.names(envpca$co), reverse = TRUE, lim=c(-1,3.5))
data(meau) envpca <- dudi.pca(meau$env, scannf=FALSE) par(mfrow=c(2,1)) sco.label(envpca$l1[,1], row.names(envpca$l1), lim=c(-1,3.5)) sco.label(envpca$co[,1], row.names(envpca$co), reverse = TRUE, lim=c(-1,3.5))
Draws evenly spaced labels, each label linked to the corresponding values of two numeric score.
sco.match(score1, score2, label = names(score1), clabel = 1, horizontal = TRUE, reverse = FALSE, pos.lab = 0.5, wmatch = 3, pch = 20, cpoint = 1, boxes = TRUE, lim = NULL, grid = TRUE, cgrid = 1, include.origin = TRUE, origin = c(0, 0), sub = "", csub = 1.25, possub = "bottomleft")
sco.match(score1, score2, label = names(score1), clabel = 1, horizontal = TRUE, reverse = FALSE, pos.lab = 0.5, wmatch = 3, pch = 20, cpoint = 1, boxes = TRUE, lim = NULL, grid = TRUE, cgrid = 1, include.origin = TRUE, origin = c(0, 0), sub = "", csub = 1.25, possub = "bottomleft")
score1 |
a numeric vector |
score2 |
a numeric vector |
label |
labels for the score |
clabel |
a character size for the labels, used with
|
horizontal |
logical. If TRUE, the plot is horizontal |
reverse |
logical. If horizontal = TRUE and reverse=TRUE, the plot is at the bottom, if reverse = FALSE, the plot is at the top. If horizontal = FALSE, the plot is at the right (TRUE) or at the left (FALSE). |
pos.lab |
a values between 0 and 1 to manage the position of the labels. |
wmatch |
a numeric values to specify the width of the matching region in the plot. The width is equal to wmatch * the height of character |
pch |
an integer specifying the symbol or the single character to be used in plotting points |
cpoint |
a character size for plotting the points, used with |
boxes |
if TRUE, labels are framed |
lim |
the range for the x axis or y axis (if horizontal = FALSE), if NULL, they are computed |
grid |
a logical value indicating whether a grid in the background of the plot should be drawn |
cgrid |
a character size, parameter used with par("cex")* |
include.origin |
a logical value indicating whether the point "origin" should belong to the plot |
origin |
the fixed point in the graph space, for example c(0,0) the origin axes |
sub |
a string of characters to be inserted as legend |
csub |
a character size for the legend, used with |
possub |
a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", "bottomright") |
The matched call.
Stéphane Dray [email protected]
sco.match(-5:5,2*(-5:5))
sco.match(-5:5,2*(-5:5))
represents the graphs to analyse the relation between a score and quantitative variables.
sco.quant (score, df, fac = NULL, clabel = 1, abline = FALSE, sub = names(df), csub = 2, possub = "topleft")
sco.quant (score, df, fac = NULL, clabel = 1, abline = FALSE, sub = names(df), csub = 2, possub = "topleft")
score |
a numeric vector |
df |
a data frame which rows equal to the score length |
fac |
a factor with the same length than the score |
clabel |
character size for the class labels (if any) used with |
abline |
a logical value indicating whether a regression line should be added |
sub |
a vector of strings of characters for the labels of variables |
csub |
a character size for the legend, used with |
possub |
a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", "bottomright") |
Daniel Chessel
w <- runif(100, -5, 10) fw <- cut (w, 5) levels(fw) <- LETTERS[1:5] wX <- data.frame(matrix(w + rnorm(900, sd = (1:900) / 100), 100, 9)) sco.quant(w, wX, fac = fw, abline = TRUE, clab = 2, csub = 3)
w <- runif(100, -5, 10) fw <- cut (w, 5) levels(fw) <- LETTERS[1:5] wX <- data.frame(matrix(w + rnorm(900, sd = (1:900) / 100), 100, 9)) sco.quant(w, wX, fac = fw, abline = TRUE, clab = 2, csub = 3)
score is a generic function. It proposes methods for the objects 'coa', 'acm', 'mix', 'pca'.
score(x, ...) scoreutil.base(y, xlim, grid, cgrid, include.origin, origin, sub, csub)
score(x, ...) scoreutil.base(y, xlim, grid, cgrid, include.origin, origin, sub, csub)
x |
an object used to select a method |
... |
further arguments passed to or from other methods |
y |
a numeric vector |
xlim |
the ranges to be encompassed by the x axis, if NULL they are computed |
grid |
a logical value indicating whether the scale vertical lines should be drawn |
cgrid |
a character size, parameter used with |
include.origin |
a logical value indicating whether the point "origin" should be belonged to the graph space |
origin |
the fixed point in the graph space, for example 0 the origin axis |
sub |
a string of characters to be inserted as legend |
csub |
a character size for the legend, used with |
scoreutil.base
is a utility function - not for the user - to define the bottom of the layout of all score
.
Daniel Chessel
sco.boxplot
, sco.distri
, sco.quant
## Not run: par(mar = c(1, 1, 1, 1)) ade4:::scoreutil.base (runif(20, 3, 7), xlim = NULL, grid = TRUE, cgrid = 0.8, include.origin = TRUE, origin = 0, sub = "Uniform", csub = 1) ## End(Not run) # returns the value of the user coordinate of the low line. # The user window id defined with c(0,1) in ordinate. # box()
## Not run: par(mar = c(1, 1, 1, 1)) ade4:::scoreutil.base (runif(20, 3, 7), xlim = NULL, grid = TRUE, cgrid = 0.8, include.origin = TRUE, origin = 0, sub = "Uniform", csub = 1) ## End(Not run) # returns the value of the user coordinate of the low line. # The user window id defined with c(0,1) in ordinate. # box()
performs the canonical graph of a Multiple Correspondence Analysis.
## S3 method for class 'acm' score(x, xax = 1, which.var = NULL, mfrow = NULL, sub = names(oritab), csub = 2, possub = "topleft", ...)
## S3 method for class 'acm' score(x, xax = 1, which.var = NULL, mfrow = NULL, sub = names(oritab), csub = 2, possub = "topleft", ...)
x |
an object of class |
xax |
the column number for the used axis |
which.var |
the numbers of the kept columns for the analysis, otherwise all columns |
mfrow |
a vector of the form "c(nr,nc)", otherwise computed by a special own function |
sub |
a vector of strings of characters to be inserted as sub-titles, otherwise the variable names of the initial array |
csub |
a character size for the sub-titles |
possub |
a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", "bottomright") |
... |
further arguments passed to or from other methods |
Daniel Chessel
data(banque) banque.acm <- dudi.acm(banque, scann = FALSE, nf = 3) score(banque.acm, which = which(banque.acm$cr[, 1] > 0.2))
data(banque) banque.acm <- dudi.acm(banque, scann = FALSE, nf = 3) score(banque.acm, which = which(banque.acm$cr[, 1] > 0.2))
performs the canonical graph of a correspondence analysis.
## S3 method for class 'coa' score(x, xax = 1, dotchart = FALSE, clab.r = 1, clab.c = 1, csub = 1, cpoi = 1.5, cet = 1.5, ...) reciprocal.coa(x)
## S3 method for class 'coa' score(x, xax = 1, dotchart = FALSE, clab.r = 1, clab.c = 1, csub = 1, cpoi = 1.5, cet = 1.5, ...) reciprocal.coa(x)
x |
an object of class |
xax |
the column number for the used axis |
dotchart |
if TRUE the graph gives a "dual scaling", if FALSE a "reciprocal scaling" |
clab.r |
a character size for row labels |
clab.c |
a character size for column labels |
csub |
a character size for the sub-titles, used with |
cpoi |
a character size for the points |
cet |
a coefficient for the size of segments in standard deviation |
... |
further arguments passed to or from other methods |
In a "reciprocal scaling", the reference score is a numeric code centred and normalized of the non zero cells of the array which both maximizes the variance of means by row and by column. The bars are drawn with half the length of this standard deviation.
return a data.frame with the scores, weights and factors of correspondences (non zero cells)
Daniel Chessel
Thioulouse, J. and Chessel D. (1992) A method for reciprocal scaling of species tolerance and sample diversity. Ecology, 73, 670–680.
layout(matrix(c(1,1,2,3), 2, 2), resp = FALSE) data(aviurba) dd1 <- dudi.coa(aviurba$fau, scan = FALSE) score(dd1, clab.r = 0, clab.c = 0.75) recscal <- reciprocal.coa(dd1) head(recscal) abline(v = 1, lty = 2, lwd = 3) sco.distri(dd1$l1[,1], aviurba$fau) sco.distri(dd1$c1[,1], data.frame(t(aviurba$fau))) # 1 reciprocal scaling correspondence score -> species amplitude + sample diversity # 2 sample score -> averaging -> species amplitude # 3 species score -> averaging -> sample diversity layout(matrix(c(1,1,2,3), 2, 2), resp = FALSE) data(rpjdl) rpjdl1 <- dudi.coa(rpjdl$fau, scan = FALSE) score(rpjdl1, clab.r = 0, clab.c = 0.75) if (requireNamespace("MASS", quietly = TRUE)) { data(caith, package = "MASS") score(dudi.coa(caith, scan = FALSE), clab.r = 1.5, clab.c = 1.5, cpoi = 3) data(housetasks) score(dudi.coa(housetasks, scan = FALSE), clab.r = 1.25, clab.c = 1.25, csub = 0, cpoi = 3) } par(mfrow = c(1,1)) score(rpjdl1, dotchart = TRUE, clab.r = 0)
layout(matrix(c(1,1,2,3), 2, 2), resp = FALSE) data(aviurba) dd1 <- dudi.coa(aviurba$fau, scan = FALSE) score(dd1, clab.r = 0, clab.c = 0.75) recscal <- reciprocal.coa(dd1) head(recscal) abline(v = 1, lty = 2, lwd = 3) sco.distri(dd1$l1[,1], aviurba$fau) sco.distri(dd1$c1[,1], data.frame(t(aviurba$fau))) # 1 reciprocal scaling correspondence score -> species amplitude + sample diversity # 2 sample score -> averaging -> species amplitude # 3 species score -> averaging -> sample diversity layout(matrix(c(1,1,2,3), 2, 2), resp = FALSE) data(rpjdl) rpjdl1 <- dudi.coa(rpjdl$fau, scan = FALSE) score(rpjdl1, clab.r = 0, clab.c = 0.75) if (requireNamespace("MASS", quietly = TRUE)) { data(caith, package = "MASS") score(dudi.coa(caith, scan = FALSE), clab.r = 1.5, clab.c = 1.5, cpoi = 3) data(housetasks) score(dudi.coa(housetasks, scan = FALSE), clab.r = 1.25, clab.c = 1.25, csub = 0, cpoi = 3) } par(mfrow = c(1,1)) score(rpjdl1, dotchart = TRUE, clab.r = 0)
performs the canonical graph of a mixed analysis.
## S3 method for class 'mix' score(x, xax = 1, csub = 2, mfrow = NULL, which.var = NULL, ...)
## S3 method for class 'mix' score(x, xax = 1, csub = 2, mfrow = NULL, which.var = NULL, ...)
x |
an object of class |
xax |
the column number for the used axis |
csub |
a character size for the sub-titles, used with |
mfrow |
a vector of the form "c(nr,nc)", otherwise computed by a special own function |
which.var |
the numbers of the kept columns for the analysis, otherwise all columns |
... |
further arguments passed to or from other methods |
Daniel Chessel
data(lascaux) w <- cbind.data.frame(lascaux$colo, lascaux$ornem) dd <- dudi.mix(w, scan = FALSE, nf = 4, add = TRUE) score(dd, which = which(dd$cr[,1] > 0.3))
data(lascaux) w <- cbind.data.frame(lascaux$colo, lascaux$ornem) dd <- dudi.mix(w, scan = FALSE, nf = 4, add = TRUE) score(dd, which = which(dd$cr[,1] > 0.3))
performs the canonical graph of a Principal Component Analysis.
## S3 method for class 'pca' score(x, xax = 1, which.var = NULL, mfrow = NULL, csub = 2, sub = names(x$tab), abline = TRUE, ...)
## S3 method for class 'pca' score(x, xax = 1, which.var = NULL, mfrow = NULL, csub = 2, sub = names(x$tab), abline = TRUE, ...)
x |
an object of class |
xax |
the column number for the used axis |
which.var |
the numbers of the kept columns for the analysis, otherwise all columns |
mfrow |
a vector of the form "c(nr,nc)", otherwise computed by a special own function |
csub |
a character size for sub-titles, used with |
sub |
a vector of string of characters to be inserted as sub-titles, otherwise the names of the variables |
abline |
a logical value indicating whether a regression line should be added |
... |
further arguments passed to or from other methods |
Daniel Chessel
data(deug) dd1 <- dudi.pca(deug$tab, scan = FALSE) score(dd1) # The correlations are : dd1$co[,1] # [1] 0.7925 0.6532 0.7410 0.5287 0.5539 0.7416 0.3336 0.2755 0.4172
data(deug) dd1 <- dudi.pca(deug$tab, scan = FALSE) score(dd1) # The correlations are : dd1$co[,1] # [1] 0.7925 0.6532 0.7410 0.5287 0.5539 0.7416 0.3336 0.2755 0.4172
The seconde
data frame gives the marks of 22 students for 8 subjects.
data(seconde)
data(seconde)
This data frame (22,8) contains the following columns: - HGEO: History and Geography - FRAN: French literature - PHYS: Physics - MATH: Mathematics - BIOL: Biology - ECON: Economy - ANGL: English language - ESPA: Spanish language
Personal communication
data(seconde) if(adegraphicsLoaded()) { scatter(dudi.pca(seconde, scan = FALSE), row.plab.cex = 1, col.plab.cex = 1.5) } else { scatter(dudi.pca(seconde, scan = FALSE), clab.r = 1, clab.c = 1.5) }
data(seconde) if(adegraphicsLoaded()) { scatter(dudi.pca(seconde, scan = FALSE), row.plab.cex = 1, col.plab.cex = 1.5) } else { scatter(dudi.pca(seconde, scan = FALSE), clab.r = 1, clab.c = 1.5) }
performs K separated multivariate analyses of an object of class ktab
containing K tables.
sepan(X, nf = 2) ## S3 method for class 'sepan' plot(x, mfrow = NULL, csub = 2, ...) ## S3 method for class 'sepan' summary(object, ...) ## S3 method for class 'sepan' print(x, ...)
sepan(X, nf = 2) ## S3 method for class 'sepan' plot(x, mfrow = NULL, csub = 2, ...) ## S3 method for class 'sepan' summary(object, ...) ## S3 method for class 'sepan' print(x, ...)
X |
an object of class |
nf |
an integer indicating the number of kept axes for each separated analysis |
x , object
|
an object of class 'sepan' |
mfrow |
a vector of the form "c(nr,nc)", otherwise computed by a special own function |
csub |
a character size for the sub-titles, used with |
... |
further arguments passed to or from other methods |
The function plot on a sepan
object allows to compare inertias and structures between arrays.
In black, the eigenvalues of kept axes in the object 'sepan'.
returns a list of class 'sepan' containing :
call |
a call order |
tab.names |
a vector of characters with the names of tables |
blo |
a numeric vector with the numbers of columns for each table |
rank |
a numeric vector with the rank of the studied matrix for each table |
Eig |
a numeric vector with all the eigenvalues |
Li |
a data frame with the row coordinates |
L1 |
a data frame with the row normed scores |
Co |
a data frame with the column coordinates |
C1 |
a data frame with the column normed coordinates |
TL |
a data frame with the factors for Li L1 |
TC |
a data frame with the factors for Co C1 |
Daniel Chessel
data(escopage) w <- data.frame(scale(escopage$tab)) w <- ktab.data.frame(w, escopage$blo, tabnames = escopage$tab.names) sep1 <- sepan(w) sep1 summary(sep1) plot(sep1)
data(escopage) w <- data.frame(scale(escopage$tab)) w <- ktab.data.frame(w, escopage$blo, tabnames = escopage$tab.names) sep1 <- sepan(w) sep1 summary(sep1) plot(sep1)
This data set gives four anthropometric measures of 150 Egyptean skulls belonging to five different historical periods.
data(skulls)
data(skulls)
The skulls
data frame has 150 rows (egyptean skulls) and 4 columns (anthropometric measures).
The four variables are the maximum breadth (V1), the basibregmatic height (V2), the basialveolar length (V3)
and the nasal height (V4). All measurements were taken in millimeters.
The measurements are made on 5 groups and 30 Egyptian skulls. The groups are defined as follows :
1 - the early predynastic period (circa 4000 BC)
2 - the late predynastic period (circa 3300 BC)
3 - the 12th and 13th dynasties (circa 1850 BC)
4 - the Ptolemiac period (circa 200 BC)
5 - the Roman period (circa 150 BC)
Thompson, A. and Randall-Maciver, R. (1905) Ancient races of the Thebaid, Oxford University Press.
Manly, B.F. (1994) Multivariate Statistical Methods. A primer,
Second edition. Chapman & Hall, London. 1–215.
The example is treated pp. 6, 13, 51, 64, 72, 107, 112 and 117.
data(skulls) pca1 <- dudi.pca(skulls, scan = FALSE) fac <- gl(5, 30) levels(fac) <- c("-4000", "-3300", "-1850", "-200", "+150") dis.skulls <- discrimin(pca1, fac, scan = FALSE) if(!adegraphicsLoaded()) plot(dis.skulls)
data(skulls) pca1 <- dudi.pca(skulls, scan = FALSE) fac <- gl(5, 30) levels(fac) <- c("-4000", "-3300", "-1850", "-200", "+150") dis.skulls <- discrimin(pca1, fac, scan = FALSE) if(!adegraphicsLoaded()) plot(dis.skulls)
Does the analysis of a series of pairs of ecological tables. This function uses Partial Triadic Analysis (pta) and ktab.match2ktabs to do the computations.
statico(KTX, KTY, scannf = TRUE)
statico(KTX, KTY, scannf = TRUE)
KTX |
an objet of class ktab |
KTY |
an objet of class ktab |
scannf |
a logical value indicating whether the eigenvalues bar plot should be displayed |
This function takes 2 ktabs and crosses each pair of tables of these ktabs with the function ktab.match2ktabs. It then does a partial triadic analysis on this new ktab with pta.
a list of class ktab, subclass kcoinertia. See ktab
IMPORTANT : KTX and KTY must have the same k-tables structure, the same number of columns, and the same column weights.
Jean Thioulouse [email protected]
Thioulouse J. (2011). Simultaneous analysis of a sequence of paired ecological tables: a comparison of several methods. Annals of Applied Statistics, 5, 2300-2325. Thioulouse J., Simier M. and Chessel D. (2004). Simultaneous analysis of a sequence of paired ecological tables. Ecology 85, 272-283. Simier, M., Blanc L., Pellegrin F., and Nandris D. (1999). Approche simultanée de K couples de tableaux : Application a l'étude des relations pathologie végétale - environnement. Revue de Statistique Appliquée, 47, 31-46.
data(meau) wit1 <- withinpca(meau$env, meau$design$season, scan = FALSE, scal = "total") spepca <- dudi.pca(meau$spe, scale = FALSE, scan = FALSE, nf = 2) wit2 <- wca(spepca, meau$design$season, scan = FALSE, nf = 2) kta1 <- ktab.within(wit1, colnames = rep(c("S1","S2","S3","S4","S5","S6"), 4)) kta2 <- ktab.within(wit2, colnames = rep(c("S1","S2","S3","S4","S5","S6"), 4)) statico1 <- statico(kta1, kta2, scan = FALSE) plot(statico1) kplot(statico1)
data(meau) wit1 <- withinpca(meau$env, meau$design$season, scan = FALSE, scal = "total") spepca <- dudi.pca(meau$spe, scale = FALSE, scan = FALSE, nf = 2) wit2 <- wca(spepca, meau$design$season, scan = FALSE, nf = 2) kta1 <- ktab.within(wit1, colnames = rep(c("S1","S2","S3","S4","S5","S6"), 4)) kta2 <- ktab.within(wit2, colnames = rep(c("S1","S2","S3","S4","S5","S6"), 4)) statico1 <- statico(kta1, kta2, scan = FALSE) plot(statico1) kplot(statico1)
Performs the series of Monte-Carlo coinertia tests of a Statico analysis (one for each couple of tables).
statico.krandtest(KTX, KTY, nrepet = 999, ...)
statico.krandtest(KTX, KTY, nrepet = 999, ...)
KTX |
an objet of class ktab containing the environmental data |
KTY |
an objet of class ktab containing the species data |
nrepet |
the number of permutations |
... |
further arguments passed to or from other methods |
This function takes 2 ktabs and does a coinertia analysis with coinertia on each pair of tables. It then uses the randtest function to do a permutation test on each of these coinertia analyses.
krandtest, a list of randtest objects. See krandtest
IMPORTANT : KTX and KTY must have the same k-tables structure, the same number of columns, and the same column weights.
Jean Thioulouse [email protected]
Thioulouse J. (2011). Simultaneous analysis of a sequence of paired ecological tables: a comparison of several methods. Annals of Applied Statistics, 5, 2300-2325.
data(meau) wit1 <- withinpca(meau$env, meau$design$season, scan = FALSE, scal = "total") spepca <- dudi.pca(meau$spe, scale = FALSE, scan = FALSE, nf = 2) wit2 <- wca(spepca, meau$design$season, scan = FALSE, nf = 2) kta1 <- ktab.within(wit1, colnames = rep(c("S1","S2","S3","S4","S5","S6"), 4)) kta2 <- ktab.within(wit2, colnames = rep(c("S1","S2","S3","S4","S5","S6"), 4)) statico1 <- statico(kta1, kta2, scan = FALSE) kr1 <- statico.krandtest(kta1, kta2) plot(kr1)
data(meau) wit1 <- withinpca(meau$env, meau$design$season, scan = FALSE, scal = "total") spepca <- dudi.pca(meau$spe, scale = FALSE, scan = FALSE, nf = 2) wit2 <- wca(spepca, meau$design$season, scan = FALSE, nf = 2) kta1 <- ktab.within(wit1, colnames = rep(c("S1","S2","S3","S4","S5","S6"), 4)) kta2 <- ktab.within(wit2, colnames = rep(c("S1","S2","S3","S4","S5","S6"), 4)) statico1 <- statico(kta1, kta2, scan = FALSE) kr1 <- statico.krandtest(kta1, kta2) plot(kr1)
performs a STATIS analysis of a ktab
object.
statis(X, scannf = TRUE, nf = 3, tol = 1e-07) ## S3 method for class 'statis' plot(x, xax = 1, yax = 2, option = 1:4, ...) ## S3 method for class 'statis' print(x, ...)
statis(X, scannf = TRUE, nf = 3, tol = 1e-07) ## S3 method for class 'statis' plot(x, xax = 1, yax = 2, option = 1:4, ...) ## S3 method for class 'statis' print(x, ...)
X |
an object of class 'ktab' |
scannf |
a logical value indicating whether the number of kept axes for the compromise should be asked |
nf |
if |
tol |
a tolerance threshold to test whether the distance matrix is Euclidean : an eigenvalue is considered positive if it is larger than |
x |
an object of class 'statis' |
xax , yax
|
the numbers of the x-axis and the y-axis |
option |
an integer between 1 and 4, otherwise the 4 components of the plot are dispayed |
... |
further arguments passed to or from other methods |
statis
returns a list of class 'statis' containing :
RV |
a matrix with the all RV coefficients |
RV.eig |
a numeric vector with all the eigenvalues |
RV.coo |
a data frame with the array scores |
tab.names |
a vector of characters with the names of the arrays |
RV.tabw |
a numeric vector with the array weigths |
C.nf |
an integer indicating the number of kept axes |
C.rank |
an integer indicating the rank of the analysis |
C.li |
a data frame with the row coordinates |
C.Co |
a data frame with the column coordinates |
C.T4 |
a data frame with the principal vectors (for each table) |
TL |
a data frame with the factors (not used) |
TC |
a data frame with the factors for Co |
T4 |
a data frame with the factors for T4 |
Daniel Chessel
Lavit, C. (1988) Analyse conjointe de tableaux quantitatifs, Masson, Paris.
Lavit, C., Escoufier, Y., Sabatier, R. and Traissac, P. (1994) The ACT (Statis method). Computational Statistics and Data Analysis, 18, 97–119.
data(jv73) kta1 <- ktab.within(withinpca(jv73$morpho, jv73$fac.riv, scann = FALSE)) statis1 <- statis(kta1, scann = FALSE) plot(statis1) dudi1 <- dudi.pca(jv73$poi, scann = FALSE, scal = FALSE) wit1 <- wca(dudi1, jv73$fac.riv, scann = FALSE) kta3 <- ktab.within(wit1) data(jv73) statis3 <- statis(kta3, scann = FALSE) plot(statis3) if(adegraphicsLoaded()) { s.arrow(statis3$C.li, pgrid.text.cex = 0) kplot(statis3, traj = TRUE, arrow = FALSE, plab.cex = 0, psub.cex = 3, ppoi.cex = 3) } else { s.arrow(statis3$C.li, cgrid = 0) kplot(statis3, traj = TRUE, arrow = FALSE, unique = TRUE, clab = 0, csub = 3, cpoi = 3) } statis3
data(jv73) kta1 <- ktab.within(withinpca(jv73$morpho, jv73$fac.riv, scann = FALSE)) statis1 <- statis(kta1, scann = FALSE) plot(statis1) dudi1 <- dudi.pca(jv73$poi, scann = FALSE, scal = FALSE) wit1 <- wca(dudi1, jv73$fac.riv, scann = FALSE) kta3 <- ktab.within(wit1) data(jv73) statis3 <- statis(kta3, scann = FALSE) plot(statis3) if(adegraphicsLoaded()) { s.arrow(statis3$C.li, pgrid.text.cex = 0) kplot(statis3, traj = TRUE, arrow = FALSE, plab.cex = 0, psub.cex = 3, ppoi.cex = 3) } else { s.arrow(statis3$C.li, cgrid = 0) kplot(statis3, traj = TRUE, arrow = FALSE, unique = TRUE, clab = 0, csub = 3, cpoi = 3) } statis3
This data set gives the presence-absence of 37 species on 515 sites.
data(steppe)
data(steppe)
steppe
is a list of 2 components.
is a data frame with 512 rows (sites) and 37 variables (species) in presence-absence.
is a vector of the species names.
Estève, J. (1978) Les méthodes d'ordination : éléments pour une discussion. in J. M. Legay and R. Tomassone, editors. Biométrie et Ecologie, Société Française de Biométrie, Paris, 223–250.
par(mfrow = c(3,1)) data(steppe) w1 <- col(as.matrix(steppe$tab[,1:15])) w1 <- as.numeric(w1[steppe$tab[,1:15] > 0]) w2 <- row(as.matrix(steppe$tab[,1:15])) w2 <- as.numeric(w2[steppe$tab[,1:15] > 0]) plot(w2, w1, pch = 20) plot(dudi.pca(steppe$tab, scan = FALSE, scale = FALSE)$li[,1], pch = 20, ylab = "PCA", xlab = "", type = "b") plot(dudi.coa(steppe$tab, scan = FALSE)$li[,1], pch = 20, ylab = "COA", xlab = "", type = "b") par(mfrow = c(1,1))
par(mfrow = c(3,1)) data(steppe) w1 <- col(as.matrix(steppe$tab[,1:15])) w1 <- as.numeric(w1[steppe$tab[,1:15] > 0]) w2 <- row(as.matrix(steppe$tab[,1:15])) w2 <- as.numeric(w2[steppe$tab[,1:15] > 0]) plot(w2, w1, pch = 20) plot(dudi.pca(steppe$tab, scan = FALSE, scale = FALSE)$li[,1], pch = 20, ylab = "PCA", xlab = "", type = "b") plot(dudi.coa(steppe$tab, scan = FALSE)$li[,1], pch = 20, ylab = "COA", xlab = "", type = "b") par(mfrow = c(1,1))
performs projections of supplementary columns.
supcol(x, ...) ## S3 method for class 'dudi' supcol(x, Xsup, ...) ## S3 method for class 'coa' supcol(x, Xsup, ...)
supcol(x, ...) ## S3 method for class 'dudi' supcol(x, Xsup, ...) ## S3 method for class 'coa' supcol(x, Xsup, ...)
x |
an object used to select a method |
Xsup |
an array with the supplementary columns ( |
... |
further arguments passed to or from other methods |
If supcol.dudi
is used, the column vectors of Xsup
are projected without prior modification onto the principal components of dudi with the scalar product associated to the row weightings of dudi.
A list of two components:
tabsup |
data frame containing the array with the supplementary columns transformed or not |
cosup |
data frame containing the coordinates of the supplementary projections |
Daniel Chessel
Anne-Béatrice Dufour [email protected]
data(rpjdl) rpjdl.coa <- dudi.coa(rpjdl$fau, scan = FALSE, nf = 4) rpjdl.coa$co[1:3, ] supcol(rpjdl.coa, rpjdl$fau[, 1:3])$cosup #the same data(doubs) dudi1 <- dudi.pca(doubs$fish, scal = FALSE, scan = FALSE) if(adegraphicsLoaded()) { g1 <- s.arrow(dudi1$co, plot = FALSE) g2 <- s.arrow(supcol(dudi1, data.frame(scalewt(doubs$env)))$cosup, plab.cex = 2, plot = FALSE) G <- superpose(g1, g2, plot = TRUE) } else { s.arrow(dudi1$co) s.arrow(supcol(dudi1, data.frame(scalewt(doubs$env)))$cosup, add.p = TRUE, clab = 2) symbols(0, 0, circles = 1, inches = FALSE, add = TRUE) }
data(rpjdl) rpjdl.coa <- dudi.coa(rpjdl$fau, scan = FALSE, nf = 4) rpjdl.coa$co[1:3, ] supcol(rpjdl.coa, rpjdl$fau[, 1:3])$cosup #the same data(doubs) dudi1 <- dudi.pca(doubs$fish, scal = FALSE, scan = FALSE) if(adegraphicsLoaded()) { g1 <- s.arrow(dudi1$co, plot = FALSE) g2 <- s.arrow(supcol(dudi1, data.frame(scalewt(doubs$env)))$cosup, plab.cex = 2, plot = FALSE) G <- superpose(g1, g2, plot = TRUE) } else { s.arrow(dudi1$co) s.arrow(supcol(dudi1, data.frame(scalewt(doubs$env)))$cosup, add.p = TRUE, clab = 2) symbols(0, 0, circles = 1, inches = FALSE, add = TRUE) }
This function takes the grand distance matrix between all items (Active + Supplementary). It computes the PCO of the distance matrix between Active items, and projects the distance matrix of Supplementary items in this PCO.
supdist(d, fsup, tol = 1e-07)
supdist(d, fsup, tol = 1e-07)
d |
Grand distance matrix between all (Active + Supplementary) items |
fsup |
A factor with two levels giving the Active (level ‘A’) or Supplementary (level ‘S’) status for each item in the distance matrix. |
tol |
Numeric tolerance used to evaluate zero eigenvalues |
coordSup |
Coordinates of Supplementary items projected in the PCO of Active items |
coordAct |
Coordinates of Active item |
coordTot |
Coordinates of Active plus Supplementary items |
Jean Thioulouse
Computations based on the Methods section of the following paper: Pele J, Abdi H, Moreau M, Thybert D, Chabbert M (2011) Multidimensional Scaling Reveals the Main Evolutionary Pathways of Class A G-Protein-Coupled Receptors. PLoS ONE 6(4): e19094. doi:10.1371/journal.pone.0019094
data(meau) ## Case 1: Supplementary items = subset of Active items ## Supplementary coordinates should be equal to Active coordinates ## PCO of active items (meau dataset has 6 sites and 10 variables) envpca1 <- dudi.pca(meau$env, scannf = FALSE) dAct <- dist(envpca1$tab) pco1 <- dudi.pco(dAct, scannf = FALSE) ## Projection of rows 19:24 (winter season for the 6 sites) ## Supplementary items must be normalized f1 <- function(w) (w - envpca1$cent) / envpca1$norm envSup <- t(apply(meau$env[19:24, ], 1, f1)) envTot <- rbind.data.frame(envpca1$tab, envSup) dTot <- dist(envTot) fSA1 <- as.factor(rep(c("A", "S"), c(24, 6))) cSup1 <- supdist(dTot, fSA1) ## Comparison (coordinates should be equal) cSup1$coordSup[, 1:2] pco1$li[19:24, ] data(meaudret) ## Case 2: Supplementary items = new items ## PCO of active items (meaudret dataset has only 5 sites and 9 variables) envpca2 <- dudi.pca(meaudret$env, scannf = FALSE) dAct <- dist(envpca2$tab) pco2 <- dudi.pco(dAct, scannf = FALSE) ## Projection of site 6 (four seasons, without Oxyg variable) ## Supplementary items must be normalized f1 <- function(w) (w - envpca2$cent) / envpca2$norm envSup <- t(apply(meau$env[seq(6, 24, 6), -5], 1, f1)) envTot <- rbind.data.frame(envpca2$tab, envSup) dTot <- dist(envTot) fSA2 <- as.factor(rep(c("A", "S"), c(20, 4))) cSup2 <- supdist(dTot, fSA2) ## Supplementary items vs. real items (both in red) if(!adegraphicsLoaded()) { par(mfrow = c(2, 2)) s.label(pco1$li, boxes = FALSE) s.label(rbind.data.frame(pco2$li, cSup2$coordSup[, 1:2]), boxes = FALSE) } else { gl1 <- s.label(pco1$li, plabels.optim = TRUE, plabels.col=rep(c(rep("black", 5),"red"), 4)) gl2 <- s.label(rbind.data.frame(pco2$li, cSup2$coordSup[, 1:2]), plabels.optim = TRUE, plabels.col=rep(c("black","red"),c(20, 4))) ADEgS(list(gl1, gl2)) }
data(meau) ## Case 1: Supplementary items = subset of Active items ## Supplementary coordinates should be equal to Active coordinates ## PCO of active items (meau dataset has 6 sites and 10 variables) envpca1 <- dudi.pca(meau$env, scannf = FALSE) dAct <- dist(envpca1$tab) pco1 <- dudi.pco(dAct, scannf = FALSE) ## Projection of rows 19:24 (winter season for the 6 sites) ## Supplementary items must be normalized f1 <- function(w) (w - envpca1$cent) / envpca1$norm envSup <- t(apply(meau$env[19:24, ], 1, f1)) envTot <- rbind.data.frame(envpca1$tab, envSup) dTot <- dist(envTot) fSA1 <- as.factor(rep(c("A", "S"), c(24, 6))) cSup1 <- supdist(dTot, fSA1) ## Comparison (coordinates should be equal) cSup1$coordSup[, 1:2] pco1$li[19:24, ] data(meaudret) ## Case 2: Supplementary items = new items ## PCO of active items (meaudret dataset has only 5 sites and 9 variables) envpca2 <- dudi.pca(meaudret$env, scannf = FALSE) dAct <- dist(envpca2$tab) pco2 <- dudi.pco(dAct, scannf = FALSE) ## Projection of site 6 (four seasons, without Oxyg variable) ## Supplementary items must be normalized f1 <- function(w) (w - envpca2$cent) / envpca2$norm envSup <- t(apply(meau$env[seq(6, 24, 6), -5], 1, f1)) envTot <- rbind.data.frame(envpca2$tab, envSup) dTot <- dist(envTot) fSA2 <- as.factor(rep(c("A", "S"), c(20, 4))) cSup2 <- supdist(dTot, fSA2) ## Supplementary items vs. real items (both in red) if(!adegraphicsLoaded()) { par(mfrow = c(2, 2)) s.label(pco1$li, boxes = FALSE) s.label(rbind.data.frame(pco2$li, cSup2$coordSup[, 1:2]), boxes = FALSE) } else { gl1 <- s.label(pco1$li, plabels.optim = TRUE, plabels.col=rep(c(rep("black", 5),"red"), 4)) gl2 <- s.label(rbind.data.frame(pco2$li, cSup2$coordSup[, 1:2]), plabels.optim = TRUE, plabels.col=rep(c("black","red"),c(20, 4))) ADEgS(list(gl1, gl2)) }
This function performs a projection of supplementary rows (i.e. supplementary individuals).
## S3 method for class 'coa' suprow(x, Xsup, ...) ## S3 method for class 'dudi' suprow(x, Xsup, ...) ## S3 method for class 'dudi' predict(object, newdata, ...) ## S3 method for class 'pca' suprow(x, Xsup, ...) ## S3 method for class 'acm' suprow(x, Xsup, ...) ## S3 method for class 'mix' suprow(x, Xsup, ...) ## S3 method for class 'fca' suprow(x, Xsup, ...)
## S3 method for class 'coa' suprow(x, Xsup, ...) ## S3 method for class 'dudi' suprow(x, Xsup, ...) ## S3 method for class 'dudi' predict(object, newdata, ...) ## S3 method for class 'pca' suprow(x, Xsup, ...) ## S3 method for class 'acm' suprow(x, Xsup, ...) ## S3 method for class 'mix' suprow(x, Xsup, ...) ## S3 method for class 'fca' suprow(x, Xsup, ...)
x , object
|
an object of class |
Xsup , newdata
|
an array with the supplementary rows |
... |
further arguments passed to or from other methods |
If suprow.dudi
is used, the column vectors of Xsup are projected without prior modifications onto the principal components of dudi with the scalar product associated to the row weightings of dudi.
predict
returns a data frame containing the coordinates of the supplementary rows. suprow
returns a list with the transformed table Xsup
in tabsup
and the coordinates of the supplementary rows in lisup
.
Daniel Chessel
Anne-Béatrice Dufour [email protected]
Gower, J. C. (1967) Multivariate analysis and multivariate geometry. The statistician, 17, 13–28.
data(euro123) par(mfrow = c(2, 2)) w <- euro123[[2]] dudi1 <- dudi.pca(w, scal = FALSE, scan = FALSE) if(adegraphicsLoaded()) { g11 <- s.arrow(dudi1$c1, psub.text = "Classical", psub.posi = "bottomright", plot = FALSE) g12 <- s.label(suprow(dudi1, w)$tabsup, plab.cex = 0.75, plot = FALSE) g1 <- superpose(g11, g12) g21 <- s.arrow(dudi1$c1, psub.text = "Without centring", psub.posi = "bottomright", plot = FALSE) g22 <- s.label(suprow(dudi1, w)$tabsup, plab.cex = 0.75, plot = FALSE) g2 <- superpose(g21, g22) g3 <- triangle.label(w, plab.cex = 0.75, label = row.names(w), adjust = FALSE, plot = FALSE) g4 <- triangle.label(w, plab.cex = 0.75, label = row.names(w), adjust = TRUE, plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { s.arrow(dudi1$c1, sub = "Classical", possub = "bottomright", csub = 2.5) s.label(suprow(dudi1, w), add.plot = TRUE, clab = 0.75) s.arrow(dudi1$c1, sub = "Without centring", possub = "bottomright", csub = 2.5) s.label(suprow(dudi1, w), clab = 0.75, add.plot = TRUE) triangle.plot(w, clab = 0.75, label = row.names(w), scal = FALSE) triangle.plot(w, clab = 0.75, label = row.names(w), scal = TRUE) } data(rpjdl) rpjdl.coa <- dudi.coa(rpjdl$fau, scann = FALSE, nf = 4) rpjdl.coa$li[1:3, ] suprow(rpjdl.coa,rpjdl$fau[1:3, ])$lisup #the same data(deug) deug.dudi <- dudi.pca(df = deug$tab, center = deug$cent, scale = FALSE, scannf = FALSE) suprow(deug.dudi, deug$tab[1:3, ])$lisup #the supplementary individuals are centered deug.dudi$li[1:3, ] # the same
data(euro123) par(mfrow = c(2, 2)) w <- euro123[[2]] dudi1 <- dudi.pca(w, scal = FALSE, scan = FALSE) if(adegraphicsLoaded()) { g11 <- s.arrow(dudi1$c1, psub.text = "Classical", psub.posi = "bottomright", plot = FALSE) g12 <- s.label(suprow(dudi1, w)$tabsup, plab.cex = 0.75, plot = FALSE) g1 <- superpose(g11, g12) g21 <- s.arrow(dudi1$c1, psub.text = "Without centring", psub.posi = "bottomright", plot = FALSE) g22 <- s.label(suprow(dudi1, w)$tabsup, plab.cex = 0.75, plot = FALSE) g2 <- superpose(g21, g22) g3 <- triangle.label(w, plab.cex = 0.75, label = row.names(w), adjust = FALSE, plot = FALSE) g4 <- triangle.label(w, plab.cex = 0.75, label = row.names(w), adjust = TRUE, plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { s.arrow(dudi1$c1, sub = "Classical", possub = "bottomright", csub = 2.5) s.label(suprow(dudi1, w), add.plot = TRUE, clab = 0.75) s.arrow(dudi1$c1, sub = "Without centring", possub = "bottomright", csub = 2.5) s.label(suprow(dudi1, w), clab = 0.75, add.plot = TRUE) triangle.plot(w, clab = 0.75, label = row.names(w), scal = FALSE) triangle.plot(w, clab = 0.75, label = row.names(w), scal = TRUE) } data(rpjdl) rpjdl.coa <- dudi.coa(rpjdl$fau, scann = FALSE, nf = 4) rpjdl.coa$li[1:3, ] suprow(rpjdl.coa,rpjdl$fau[1:3, ])$lisup #the same data(deug) deug.dudi <- dudi.pca(df = deug$tab, center = deug$cent, scale = FALSE, scannf = FALSE) suprow(deug.dudi, deug$tab[1:3, ])$lisup #the supplementary individuals are centered deug.dudi$li[1:3, ] # the same
This function performs a projection of supplementary rows (i.e. supplementary individuals) for a Partial Triadic Analysis (pta
) of K-tables.
Computations are valid ONLY if the pta
has been done on a K-Tables obtained by the withinpca
function, followed by calls to the ktab.within
and t
functions.
## S3 method for class 'pta' suprow(x, Xsup, facSup, ...)
## S3 method for class 'pta' suprow(x, Xsup, facSup, ...)
x |
an object of class |
Xsup |
a table with the supplementary rows |
facSup |
a factor partitioning the rows of |
... |
further arguments passed to or from other methods |
This function computes the coordinates of the supplementary rows for a K-tables.
The table of supplementary rows is standardized according to the 'Bouroche' standardization used in the Within Analysis of the original pta
.
In a first step, the table of supplementary rows is standardized (centred and normed) with the mean and variance of the original table of active individuals (i.e. the K-tables used in pta
). Then, according to the withinpca
procedure, a second transformation is applied.
For "partial", supplementary rows are standardized in each sub-table (corresponding to each level of the factor) by the mean and variance of each corresponding sub-sample in the table of active individuals. Hence, supplementary rows have null mean and unit variance in each sub-table.
For "total", supplementary rows are centred in each sub-table with the mean of each coresponding sub-sample in the table of active individuals and then normed with the global variance ot the table of active individuals. Hence, supplementary rows have a null mean in each sub-table and a global variance equal to one.
Returns a list with the transformed table Xsup
in tabsup
and the coordinates of the supplementary rows in lisup
.
Benjamin Alric [email protected]
Jean Thioulouse [email protected]
Bouroche, J. M. (1975) Analyse des données ternaires: la double analyse en composantes principales. Thèse de 3ème cycle, Université de Paris VI.
data(meau) # Active rows actenv <- meau$env[meau$design$site != "S6", -c(5)] actfac <- meau$design$season[meau$design$site != "S6"] # Suplementary rows supenv <- meau$env[meau$design$site == "S6", -c(5)] supfac <- meau$design$season[meau$design$site == "S6"] # Total = active + suplementary rows totenv <- meau$env[, -c(5)] totfac <- meau$design$season # PTA with 6 sampling sites wittot <- withinpca(df = totenv, fac = totfac, scannf = FALSE, scaling = "partial") kta1tot <- ktab.within(wittot, colnames = rep(c("S1", "S2", "S3", "S4", "S5", "S6"), 4)) kta2tot <- t(kta1tot) pta1tot <- pta(kta2tot, scann = FALSE) # PTA with 5 sampling sites and site 6 added as supplementary element wit1 <- withinpca(df = actenv, fac = actfac, scannf = FALSE, scaling = "partial") kta1 <- ktab.within(wit1, colnames = rep(c("S1", "S2", "S3", "S4", "S5"), 4)) kta2 <- t(kta1) pta1 <- pta(kta2, scann = FALSE) supenv.pta <- suprow(x = pta1, Xsup = supenv, facSup = supfac) if (adegraphicsLoaded()) { # g1t = active + suplementary rows g1t <- s.label(pta1tot$Tli, labels = rownames(totenv), plabels = list(box = list(draw = FALSE), optim = TRUE), xlim = c(-6, 5), ylim = c(-5, 5), psub = list(text="Total", position="topleft"), plot = FALSE) # g1 = Active rows g1 <- s.label(pta1$Tli, labels = rownames(actenv), plabels = list(box = list(draw = FALSE), optim =TRUE), xlim = c(-6, 5), ylim = c(-5, 5), psub = list(text="Active", position="topleft"), pgrid = list(text=list(cex = 0)), plot = FALSE) # g2 = Supplementary rows g2 <- s.label(supenv.pta$lisup, plabels = list(box = list(draw = FALSE), optim = TRUE), ppoints = list(col = "red"), psub = list(text="Supplementary", position="topright"), pgrid = list(text=list(cex = 0)), plot = FALSE) # g3 = superposition of active and suplementary rows g3 <- g1 + g2 # Comparison of the total analysis and the analysis with supplementary rows ADEgS(list(g1t,g3)) } else { par(mfrow=c(2,2)) # g1t = active + suplementary rows g1t <- s.label(pta1tot$Tli, label = rownames(totenv), xlim = c(-6, 5), ylim = c(-5, 5), sub="Total") # g1 = Active rows g1 <- s.label(pta1$Tli, label = rownames(actenv), clabel = 1, xlim = c(-6, 5), ylim = c(-5, 5), sub="Active+Supplementary") # g2 = Supplementary rows g2 <- s.label(supenv.pta$lisup, clabel = 1.5, xlim = c(-6, 5), ylim = c(-5, 5), add.plot = TRUE) }
data(meau) # Active rows actenv <- meau$env[meau$design$site != "S6", -c(5)] actfac <- meau$design$season[meau$design$site != "S6"] # Suplementary rows supenv <- meau$env[meau$design$site == "S6", -c(5)] supfac <- meau$design$season[meau$design$site == "S6"] # Total = active + suplementary rows totenv <- meau$env[, -c(5)] totfac <- meau$design$season # PTA with 6 sampling sites wittot <- withinpca(df = totenv, fac = totfac, scannf = FALSE, scaling = "partial") kta1tot <- ktab.within(wittot, colnames = rep(c("S1", "S2", "S3", "S4", "S5", "S6"), 4)) kta2tot <- t(kta1tot) pta1tot <- pta(kta2tot, scann = FALSE) # PTA with 5 sampling sites and site 6 added as supplementary element wit1 <- withinpca(df = actenv, fac = actfac, scannf = FALSE, scaling = "partial") kta1 <- ktab.within(wit1, colnames = rep(c("S1", "S2", "S3", "S4", "S5"), 4)) kta2 <- t(kta1) pta1 <- pta(kta2, scann = FALSE) supenv.pta <- suprow(x = pta1, Xsup = supenv, facSup = supfac) if (adegraphicsLoaded()) { # g1t = active + suplementary rows g1t <- s.label(pta1tot$Tli, labels = rownames(totenv), plabels = list(box = list(draw = FALSE), optim = TRUE), xlim = c(-6, 5), ylim = c(-5, 5), psub = list(text="Total", position="topleft"), plot = FALSE) # g1 = Active rows g1 <- s.label(pta1$Tli, labels = rownames(actenv), plabels = list(box = list(draw = FALSE), optim =TRUE), xlim = c(-6, 5), ylim = c(-5, 5), psub = list(text="Active", position="topleft"), pgrid = list(text=list(cex = 0)), plot = FALSE) # g2 = Supplementary rows g2 <- s.label(supenv.pta$lisup, plabels = list(box = list(draw = FALSE), optim = TRUE), ppoints = list(col = "red"), psub = list(text="Supplementary", position="topright"), pgrid = list(text=list(cex = 0)), plot = FALSE) # g3 = superposition of active and suplementary rows g3 <- g1 + g2 # Comparison of the total analysis and the analysis with supplementary rows ADEgS(list(g1t,g3)) } else { par(mfrow=c(2,2)) # g1t = active + suplementary rows g1t <- s.label(pta1tot$Tli, label = rownames(totenv), xlim = c(-6, 5), ylim = c(-5, 5), sub="Total") # g1 = Active rows g1 <- s.label(pta1$Tli, label = rownames(actenv), clabel = 1, xlim = c(-6, 5), ylim = c(-5, 5), sub="Active+Supplementary") # g2 = Supplementary rows g2 <- s.label(supenv.pta$lisup, clabel = 1.5, xlim = c(-6, 5), ylim = c(-5, 5), add.plot = TRUE) }
symbols.phylog
draws the phylogenetic tree and represents the values of
the variable by symbols (squares or circles) which size is proportional to value.
White symbols correspond to values which are below the mean, and black symbols
correspond to values which are over.
symbols.phylog(phylog, circles, squares, csize = 1, clegend = 1, sub = "", csub = 1, possub = "topleft")
symbols.phylog(phylog, circles, squares, csize = 1, clegend = 1, sub = "", csub = 1, possub = "topleft")
phylog |
an object of class |
circles |
a vector giving the radii of the circles |
squares |
a vector giving the length of the sides of the squares |
csize |
a size coefficient for symbols |
clegend |
a character size for the legend used by |
sub |
a string of characters to be inserted as legend |
csub |
a character size for the legend, used with |
possub |
a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", "bottomright") |
Daniel Chessel
Sébastien Ollier [email protected]
table.phylog
and dotchart.phylog
for many variables
data(mjrochet) mjrochet.phy <- newick2phylog(mjrochet$tre) tab0 <- data.frame(scalewt(log(mjrochet$tab))) par(mfrow=c(3,2)) for (j in 1:6) { w <- tab0[,j] symbols.phylog(phylog = mjrochet.phy, w, csi = 1.5, cleg = 1.5, sub = names(tab0)[j], csub = 3) } par(mfrow=c(1,1))
data(mjrochet) mjrochet.phy <- newick2phylog(mjrochet$tre) tab0 <- data.frame(scalewt(log(mjrochet$tab))) par(mfrow=c(3,2)) for (j in 1:6) { w <- tab0[,j] symbols.phylog(phylog = mjrochet.phy, w, csi = 1.5, cleg = 1.5, sub = names(tab0)[j], csub = 3) } par(mfrow=c(1,1))
This data set is extracted from an opinion poll (period 1970-1980) on 1000 respondents.
data(syndicats)
data(syndicats)
The syndicats
data frame has 5 rows and 4 columns.
"Which politic family are you agreeing about?" has 5 response items :
extgauche
(extreme left) left
center
right
and extdroite
(extreme right)
"What do you think of the trade importance?" has 4 response items :
trop
(too important) adequate
insufficient
nesaispas
(no opinion)
unknown
data(syndicats) par(mfrow = c(1,2)) dudi1 <- dudi.coa(syndicats, scan = FALSE) score (dudi1, 1, TRUE) score (dudi1, 1, FALSE)
data(syndicats) par(mfrow = c(1,2)) dudi1 <- dudi.coa(syndicats, scan = FALSE) score (dudi1, 1, TRUE) score (dudi1, 1, FALSE)
This data set gives the average temperatures of 30 French cities during 12 months.
data(t3012)
data(t3012)
t3012
is a list with the following components:
a data frame with 30 rows (cities) and 2 coordinates (x, y)
a data frame with 30 rows (cities) and 12 columns (months). Each column contains the average temperature in tenth of degree Celsius.
a data frame with 4 columns (x1, y1, x2, y2) for the contour display of France
an object of the class SpatialPolygons
of sp
,
containing the map
Besse, P. (1979) Etude descriptive d'un processus; approximation, interpolation. Thèse de troisième cycle, Université Paul Sabatier, Toulouse.
data(t3012) data(elec88) if(adegraphicsLoaded()) { if(requireNamespace("sp", quietly = TRUE)) { s.arrow(t3012$xy, pori.ori = as.numeric(t3012$xy["Paris", ]), Sp = t3012$Spatial, pSp.col = "white", pgrid.draw = FALSE) } } else { area.plot(elec88$area) s.arrow(t3012$xy, ori = as.numeric(t3012$xy["Paris", ]), add.p = TRUE) }
data(t3012) data(elec88) if(adegraphicsLoaded()) { if(requireNamespace("sp", quietly = TRUE)) { s.arrow(t3012$xy, pori.ori = as.numeric(t3012$xy["Paris", ]), Sp = t3012$Spatial, pSp.col = "white", pgrid.draw = FALSE) } } else { area.plot(elec88$area) s.arrow(t3012$xy, ori = as.numeric(t3012$xy["Paris", ]), add.p = TRUE) }
presents a graph for viewing contingency tables.
table.cont(df, x = 1:ncol(df), y = 1:nrow(df), row.labels = row.names(df), col.labels = names(df), clabel.row = 1, clabel.col = 1, abmean.x = FALSE, abline.x = FALSE, abmean.y = FALSE, abline.y = FALSE, csize = 1, clegend = 0, grid = TRUE)
table.cont(df, x = 1:ncol(df), y = 1:nrow(df), row.labels = row.names(df), col.labels = names(df), clabel.row = 1, clabel.col = 1, abmean.x = FALSE, abline.x = FALSE, abmean.y = FALSE, abline.y = FALSE, csize = 1, clegend = 0, grid = TRUE)
df |
a data frame with only positive or null values |
x |
a vector of values to position the columns |
y |
a vector of values to position the rows |
row.labels |
a character vector for the row labels |
col.labels |
a character vetor for the column labels |
clabel.row |
a character size for the row labels |
clabel.col |
a character size for the column labels |
abmean.x |
a logical value indicating whether the column conditional means should be drawn |
abline.x |
a logical value indicating whether the regression line of y onto x should be plotted |
abmean.y |
a logical value indicating whether the row conditional means should be drawn |
abline.y |
a logical value indicating whether the regression line of x onto y should be plotted |
csize |
a coefficient for the square size of the values |
clegend |
if not NULL, a character size for the legend used with |
grid |
a logical value indicating whether a grid in the background of the plot should be drawn |
Daniel Chessel
data(chats) chatsw <- data.frame(t(chats)) chatscoa <- dudi.coa(chatsw, scann = FALSE) par(mfrow = c(2,2)) table.cont(chatsw, abmean.x = TRUE, csi = 2, abline.x = TRUE, clabel.r = 1.5, clabel.c = 1.5) table.cont(chatsw, abmean.y = TRUE, csi = 2, abline.y = TRUE, clabel.r = 1.5, clabel.c = 1.5) table.cont(chatsw, x = chatscoa$c1[,1], y = chatscoa$l1[,1], abmean.x = TRUE, csi = 2, abline.x = TRUE, clabel.r = 1.5, clabel.c = 1.5) table.cont(chatsw, x = chatscoa$c1[,1], y = chatscoa$l1[,1], abmean.y = TRUE, csi = 2, abline.y = TRUE, clabel.r = 1.5, clabel.c = 1.5) par(mfrow = c(1,1)) ## Not run: data(rpjdl) w <- data.frame(t(rpjdl$fau)) wcoa <- dudi.coa(w, scann = FALSE) table.cont(w, abmean.y = TRUE, x = wcoa$c1[,1], y = rank(wcoa$l1[,1]), csi = 0.2, clabel.c = 0, row.labels = rpjdl$lalab, clabel.r = 0.75) ## End(Not run)
data(chats) chatsw <- data.frame(t(chats)) chatscoa <- dudi.coa(chatsw, scann = FALSE) par(mfrow = c(2,2)) table.cont(chatsw, abmean.x = TRUE, csi = 2, abline.x = TRUE, clabel.r = 1.5, clabel.c = 1.5) table.cont(chatsw, abmean.y = TRUE, csi = 2, abline.y = TRUE, clabel.r = 1.5, clabel.c = 1.5) table.cont(chatsw, x = chatscoa$c1[,1], y = chatscoa$l1[,1], abmean.x = TRUE, csi = 2, abline.x = TRUE, clabel.r = 1.5, clabel.c = 1.5) table.cont(chatsw, x = chatscoa$c1[,1], y = chatscoa$l1[,1], abmean.y = TRUE, csi = 2, abline.y = TRUE, clabel.r = 1.5, clabel.c = 1.5) par(mfrow = c(1,1)) ## Not run: data(rpjdl) w <- data.frame(t(rpjdl$fau)) wcoa <- dudi.coa(w, scann = FALSE) table.cont(w, abmean.y = TRUE, x = wcoa$c1[,1], y = rank(wcoa$l1[,1]), csi = 0.2, clabel.c = 0, row.labels = rpjdl$lalab, clabel.r = 0.75) ## End(Not run)
presents a graph for viewing distance matrices.
table.dist(d, x = 1:(attr(d, "Size")), labels = as.character(x), clabel = 1, csize = 1, grid = TRUE)
table.dist(d, x = 1:(attr(d, "Size")), labels = as.character(x), clabel = 1, csize = 1, grid = TRUE)
d |
an object of class |
x |
a vector of the row and column positions |
labels |
a vector of strings of characters for the labels |
clabel |
a character size for the labels |
csize |
a coefficient for the circle size |
grid |
a logical value indicating whether a grid in the background of the plot should be drawn |
Daniel Chessel
data(eurodist) table.dist(eurodist, labels = attr(eurodist, "Labels"))
data(eurodist) table.dist(eurodist, labels = attr(eurodist, "Labels"))
presents a graph for viewing the numbers of a table by grey levels.
table.paint(df, x = 1:ncol(df), y = nrow(df):1, row.labels = row.names(df), col.labels = names(df), clabel.row = 1, clabel.col = 1, csize = 1, clegend = 1)
table.paint(df, x = 1:ncol(df), y = nrow(df):1, row.labels = row.names(df), col.labels = names(df), clabel.row = 1, clabel.col = 1, csize = 1, clegend = 1)
df |
a data frame |
x |
a vector of values to position the columns, used only for the ordered values |
y |
a vector of values to position the rows, used only for the ordered values |
row.labels |
a character vector for the row labels |
col.labels |
a character vector for the column labels |
clabel.row |
a character size for the row labels |
clabel.col |
a character size for the column labels |
csize |
if 'clegend' not NULL, a coefficient for the legend size |
clegend |
a character size for the legend, otherwise no legend |
Daniel Chessel
data(rpjdl) X <- data.frame(t(rpjdl$fau)) Y <- data.frame(t(rpjdl$mil)) layout(matrix(c(1,2,2,2,1,2,2,2,1,2,2,2,1,2,2,2), 4, 4)) coa1 <- dudi.coa(X, scan = FALSE) x <- rank(coa1$co[,1]) y <- rank(coa1$li[,1]) table.paint(Y, x = x, y = 1:8, clabel.c = 0, cleg = 0) abline(v = 114.9, lwd = 3, col = "red") abline(v = 66.4, lwd = 3, col = "red") table.paint(X, x = x, y = y, clabel.c = 0, cleg = 0, row.lab = paste(" ", row.names(X), sep = "")) abline(v = 114.9, lwd = 3, col = "red") abline(v = 66.4, lwd = 3, col = "red") par(mfrow = c(1, 1))
data(rpjdl) X <- data.frame(t(rpjdl$fau)) Y <- data.frame(t(rpjdl$mil)) layout(matrix(c(1,2,2,2,1,2,2,2,1,2,2,2,1,2,2,2), 4, 4)) coa1 <- dudi.coa(X, scan = FALSE) x <- rank(coa1$co[,1]) y <- rank(coa1$li[,1]) table.paint(Y, x = x, y = 1:8, clabel.c = 0, cleg = 0) abline(v = 114.9, lwd = 3, col = "red") abline(v = 66.4, lwd = 3, col = "red") table.paint(X, x = x, y = y, clabel.c = 0, cleg = 0, row.lab = paste(" ", row.names(X), sep = "")) abline(v = 114.9, lwd = 3, col = "red") abline(v = 66.4, lwd = 3, col = "red") par(mfrow = c(1, 1))
This function gives a graphical display for viewing the numbers of a table by square sizes in front of the corresponding phylogenetic tree.
table.phylog(df, phylog, x = 1:ncol(df), f.phylog = 0.5, labels.row = gsub("[_]", " ", row.names(df)), clabel.row = 1, labels.col = names(df), clabel.col = 1, labels.nod = names(phylog$nodes), clabel.nod = 0, cleaves = 1, cnodes = 1, csize = 1, grid = TRUE, clegend = 0.75)
table.phylog(df, phylog, x = 1:ncol(df), f.phylog = 0.5, labels.row = gsub("[_]", " ", row.names(df)), clabel.row = 1, labels.col = names(df), clabel.col = 1, labels.nod = names(phylog$nodes), clabel.nod = 0, cleaves = 1, cnodes = 1, csize = 1, grid = TRUE, clegend = 0.75)
df |
: a data frame or a matrix |
phylog |
: an object of class |
x |
: a vector of values to position the columns |
f.phylog |
: a size coefficient for tree size (a parameter to draw the tree in proportion to leaves labels) |
labels.row |
: a vector of strings of characters for row labels |
clabel.row |
: a character size for the leaves labels, used with |
labels.col |
: a vector of strings of characters for columns labels |
clabel.col |
: a character size for the leaves labels, used with |
labels.nod |
: a vector of strings of characters for the nodes labels |
clabel.nod |
: a character size for the nodes labels, used with |
cleaves |
: a character size for plotting the points that represent the leaves, used with |
cnodes |
: a character size for plotting the points that represent the nodes, used with |
csize |
: a size coefficient for symbols |
grid |
: a logical value indicating whether the grid should be plotted |
clegend |
: a character size for the legend (if 0, no legend) |
The function verifies that sort(row.names(df))==sort(names(phylog$leaves))
.
If df
is a matrix the function uses as.data.frame(df)
.
Daniel Chessel
Sébastien Ollier [email protected]
symbols.phylog
for one variable
## Not run: data(newick.eg) w.phy <- newick2phylog(newick.eg[[9]]) w.tab <- data.frame(matrix(rnorm(620), 31, 20)) row.names(w.tab) <- sort(names(w.phy$leaves)) table.phylog(w.tab, w.phy, csi = 1.5, f = 0.5, clabel.n = 0.75, clabel.c = 0.5) ## End(Not run)
## Not run: data(newick.eg) w.phy <- newick2phylog(newick.eg[[9]]) w.tab <- data.frame(matrix(rnorm(620), 31, 20)) row.names(w.tab) <- sort(names(w.phy$leaves)) table.phylog(w.tab, w.phy, csi = 1.5, f = 0.5, clabel.n = 0.75, clabel.c = 0.5) ## End(Not run)
presents a graph for viewing the numbers of a table by square sizes.
table.value(df, x = 1:ncol(df), y = nrow(df):1, row.labels = row.names(df), col.labels = names(df), clabel.row = 1, clabel.col = 1, csize = 1, clegend = 1, grid = TRUE)
table.value(df, x = 1:ncol(df), y = nrow(df):1, row.labels = row.names(df), col.labels = names(df), clabel.row = 1, clabel.col = 1, csize = 1, clegend = 1, grid = TRUE)
df |
a data frame |
x |
a vector of values to position the columns |
y |
a vector of values to position the rows |
row.labels |
a character vector for the row labels |
col.labels |
a character vector for the column labels |
clabel.row |
a character size for the row labels |
clabel.col |
a character size for the column labels |
csize |
a coefficient for the square size of the values |
clegend |
a character size for the legend (if 0, no legend) |
grid |
a logical value indicating whether the grid should be plotted |
Daniel Chessel
if(!adegraphicsLoaded()) { data(olympic) w <- olympic$tab w <- data.frame(scale(w)) wpca <- dudi.pca(w, scann = FALSE) par(mfrow = c(1, 3)) table.value(w, csi = 2, clabel.r = 2, clabel.c = 2) table.value(w, y = rank(wpca$li[, 1]), x = rank(wpca$co[, 1]), csi = 2, clabel.r = 2, clabel.c = 2) table.value(w, y = wpca$li[, 1], x = wpca$co[, 1], csi = 2, clabel.r = 2, clabel.c = 2) par(mfrow = c(1, 1)) }
if(!adegraphicsLoaded()) { data(olympic) w <- olympic$tab w <- data.frame(scale(w)) wpca <- dudi.pca(w, scann = FALSE) par(mfrow = c(1, 3)) table.value(w, csi = 2, clabel.r = 2, clabel.c = 2) table.value(w, y = rank(wpca$li[, 1]), x = rank(wpca$co[, 1]), csi = 2, clabel.r = 2, clabel.c = 2) table.value(w, y = wpca$li[, 1], x = wpca$co[, 1], csi = 2, clabel.r = 2, clabel.c = 2) par(mfrow = c(1, 1)) }
This data set gives informations between sites, species, environmental and biolgoical variables.
data(tarentaise)
data(tarentaise)
tarentaise
is a list of 5 components.
is a data frame with 376 sites and 98 bird species.
is a vector of the 98 French names of the species.
is a vector giving the altitude of the 376 sites in m.
is a data frame with 14 environmental variables.
is a data frame with 29 biological variables of the 98 species.
The attribute col.blocks
of the data frame tarentaise$traits
indicates it is composed of 6 units of variables.
Original data from Hubert Tournier, University of Savoie and Philippe Lebreton, University of Lyon 1.
Lebreton, P., Tournier H. and Lebreton J. D. (1976) Etude de l'avifaune du Parc National de la Vanoise VI Recherches d'ordre quantitatif sur les Oiseaux forestiers de Vanoise. Travaux Scientifiques du parc National de la vanoise, 7, 163–243.
Lebreton, Ph. and Martinot, J.P. (1998) Oiseaux de Vanoise. Guide de l'ornithologue en montagne. Libris, Grenoble. 1–240.
Lebreton, Ph., Lebrun, Ph., Martinot, J.P., Miquet, A. and Tournier, H. (1999) Approche écologique de l'avifaune de la Vanoise. Travaux scientifiques du Parc national de la Vanoise, 21, 7–304.
See a data description at http://pbil.univ-lyon1.fr/R/pdf/pps038.pdf (in French).
data(tarentaise) coa1 <- dudi.coa(tarentaise$ecol, sca = FALSE, nf = 2) s.class(coa1$li, tarentaise$envir$alti, wt = coa1$lw) ## Not run: acm1 <- dudi.acm(tarentaise$envir, sca = FALSE, nf = 2) s.class(acm1$li, tarentaise$envir$alti) ## End(Not run)
data(tarentaise) coa1 <- dudi.coa(tarentaise$ecol, sca = FALSE, nf = 2) s.class(coa1$li, tarentaise$envir$alti, wt = coa1$lw) ## Not run: acm1 <- dudi.acm(tarentaise$envir, sca = FALSE, nf = 2) s.class(acm1$li, tarentaise$envir$alti) ## End(Not run)
This data sets contains two taxonomies.
data(taxo.eg)
data(taxo.eg)
taxo.eg
is a list containing the 2 following objects:
is a data frame with 15 species and 3 columns.
is a data frame with 40 species and 2 columns.
Variables of the first data frame are : genre (a factor genre with 8 levels),
famille (a factor familiy with 5 levels) and ordre (a factor order with 2 levels).
Variables of the second data frame are : gen(a factor genre with 29 levels), fam (a factor family with 19 levels).
data(taxo.eg) taxo.eg[[1]] as.taxo(taxo.eg[[1]]) class(taxo.eg[[1]]) class(as.taxo(taxo.eg[[1]])) tax.phy <- taxo2phylog(as.taxo(taxo.eg[[1]]), add.tools = TRUE) plot(tax.phy,clabel.l=1) par(mfrow = c(1,2)) table.phylog(tax.phy$Bindica,tax.phy) table.phylog(tax.phy$Bscores,tax.phy) par(mfrow = c(1,1)) radial.phylog(taxo2phylog(as.taxo(taxo.eg[[2]])))
data(taxo.eg) taxo.eg[[1]] as.taxo(taxo.eg[[1]]) class(taxo.eg[[1]]) class(as.taxo(taxo.eg[[1]])) tax.phy <- taxo2phylog(as.taxo(taxo.eg[[1]]), add.tools = TRUE) plot(tax.phy,clabel.l=1) par(mfrow = c(1,2)) table.phylog(tax.phy$Bindica,tax.phy) table.phylog(tax.phy$Bscores,tax.phy) par(mfrow = c(1,1)) radial.phylog(taxo2phylog(as.taxo(taxo.eg[[2]])))
This functions allow to test for the number of axes in multivariate analysis. The
procedure testdim.pca
implements a method for principal component analysis on
correlation matrix. The procedure is based on the computation of the RV coefficient.
testdim(object, ...) ## S3 method for class 'pca' testdim(object, nrepet = 99, nbax = object$rank, alpha = 0.05, ...)
testdim(object, ...) ## S3 method for class 'pca' testdim(object, nrepet = 99, nbax = object$rank, alpha = 0.05, ...)
object |
an object corresponding to an analysis (e.g. duality diagram, an object of class |
nrepet |
the number of repetitions for the permutation procedure |
nbax |
the number of axes to be tested, by default all axes |
alpha |
the significance level |
... |
other arguments |
An object of the class krandtest
. It contains also:
nb |
The estimated number of axes to keep |
nb.cor |
The number of axes to keep estimated using a sequential Bonferroni procedure |
Stéphane Dray [email protected]
Dray, S. (2008) On the number of principal components: A test of dimensionality based on measurements of similarity between matrices. Computational Statistics and Data Analysis, Volume 52, 2228–2237. doi:10.1016/j.csda.2007.07.015
dudi.pca
, RV.rtest
,testdim.multiblock
tab <- data.frame(matrix(rnorm(200),20,10)) pca1 <- dudi.pca(tab,scannf=FALSE) test1 <- testdim(pca1) test1 test1$nb test1$nb.cor data(doubs) pca2 <- dudi.pca(doubs$env,scannf=FALSE) test2 <- testdim(pca2) test2 test2$nb test2$nb.cor
tab <- data.frame(matrix(rnorm(200),20,10)) pca1 <- dudi.pca(tab,scannf=FALSE) test1 <- testdim(pca1) test1 test1$nb test1$nb.cor data(doubs) pca2 <- dudi.pca(doubs$env,scannf=FALSE) test2 <- testdim(pca2) test2 test2$nb test2$nb.cor
Function to perform a two-fold cross-validation to select the optimal number of dimensions of multiblock methods, i.e., multiblock principal component analysis with instrumental Variables or multiblock partial least squares
## S3 method for class 'multiblock' testdim(object, nrepet = 100, quantiles = c(0.25, 0.75), ...)
## S3 method for class 'multiblock' testdim(object, nrepet = 100, quantiles = c(0.25, 0.75), ...)
object |
|
nrepet |
integer indicating the number of repetitions |
quantiles |
a vector indicating the lower and upper quantiles to compute |
... |
other arguments to be passed to methods |
An object of class krandxval
Stéphanie Bougeard ([email protected]) and Stéphane Dray ([email protected])
Stone M. (1974) Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, 36, 111-147.
Bougeard, S. and Dray S. (2018) Supervised Multiblock Analysis in R with the ade4 Package. Journal of Statistical Software, 86 (1), 1-17. doi:10.18637/jss.v086.i01
mbpcaiv
, mbpls
,
randboot.multiblock
, as.krandxval
data(chickenk) Mortality <- chickenk[[1]] dudiY.chick <- dudi.pca(Mortality, center = TRUE, scale = TRUE, scannf = FALSE) ktabX.chick <- ktab.list.df(chickenk[2:5]) resmbpcaiv.chick <- mbpcaiv(dudiY.chick, ktabX.chick, scale = TRUE, option = "uniform", scannf = FALSE) ## nrepet should be higher for a real analysis test <- testdim(resmbpcaiv.chick, nrepet = 10) test if(adegraphicsLoaded()) plot(test)
data(chickenk) Mortality <- chickenk[[1]] dudiY.chick <- dudi.pca(Mortality, center = TRUE, scale = TRUE, scannf = FALSE) ktabX.chick <- ktab.list.df(chickenk[2:5]) resmbpcaiv.chick <- mbpcaiv(dudiY.chick, ktabX.chick, scale = TRUE, option = "uniform", scannf = FALSE) ## nrepet should be higher for a real analysis test <- testdim(resmbpcaiv.chick, nrepet = 10) test if(adegraphicsLoaded()) plot(test)
This data set contains informations about geochemical characteristics of heavy metal pollution in surface sediments of the Tinto and Odiel river estuary (south-western Spain).
data(tintoodiel)
data(tintoodiel)
tintoodiel
is a list with the following components:
a data frame that contains spatial coordinates of the 52 sites
a data frame with 12 columns (concentration of heavy metals) and 52 rows (sites)
the neighbourhood graph of the 52 sites (an object of class nb
)
Borrego, J., Morales, J.A., de la Torre, M.L. and Grande, J.A. (2002) Geochemical characteristics of heavy metal pollution in surface sediments of the Tinto and Odiel river estuary (south-western Spain). Environmental Geology, 41, 785–796.
data(tintoodiel) estuary.pca <- dudi.pca(tintoodiel$tab, scan = FALSE, nf = 4) if(requireNamespace("spdep", quietly = TRUE) & requireNamespace("adespatial", quietly = TRUE)) { estuary.listw <- spdep::nb2listw(tintoodiel$nb) estuary.pca.ms <- adespatial::multispati(estuary.pca, estuary.listw, scan = FALSE, nfposi = 3, nfnega = 2) summary(estuary.pca.ms) par(mfrow = c(1, 2)) barplot(estuary.pca$eig) barplot(estuary.pca.ms$eig) par(mfrow = c(1, 1)) }
data(tintoodiel) estuary.pca <- dudi.pca(tintoodiel$tab, scan = FALSE, nf = 4) if(requireNamespace("spdep", quietly = TRUE) & requireNamespace("adespatial", quietly = TRUE)) { estuary.listw <- spdep::nb2listw(tintoodiel$nb) estuary.pca.ms <- adespatial::multispati(estuary.pca, estuary.listw, scan = FALSE, nfposi = 3, nfnega = 2) summary(estuary.pca.ms) par(mfrow = c(1, 2)) barplot(estuary.pca$eig) barplot(estuary.pca.ms$eig) par(mfrow = c(1, 1)) }
This data set describes the phylogeny of 11 flowers as reported by Morales (2000). It also gives morphologic and demographic traits corresponding to these 11 species.
data(tithonia)
data(tithonia)
tithonia
is a list containing the 2 following objects :
is a character string giving the phylogenetic tree in Newick format.
is a data frame with 11 species and 14 traits (6 morphologic traits and 8 demographic).
Variables of tithonia$tab
are the following ones :
morho1: is a numeric vector that describes the seed size (mm)
morho2: is a numeric vector that describes the flower size (mm)
morho3: is a numeric vector that describes the female leaf size (cm)
morho4: is a numeric vector that describes the head size (mm)
morho5: is a integer vector that describes the number of flowers per head
morho6: is a integer vector that describes the number of seeds per head
demo7: is a numeric vector that describes the seedling height (cm)
demo8: is a numeric vector that describes the growth rate (cm/day)
demo9: is a numeric vector that describes the germination time
demo10: is a numeric vector that describes the establishment (per cent)
demo11: is a numeric vector that describes the viability (per cent)
demo12: is a numeric vector that describes the germination (per cent)
demo13: is a integer vector that describes the resource allocation
demo14: is a numeric vector that describes the adult height (m)
Data were obtained from Morales, E. (2000) Estimating phylogenetic inertia in Tithonia (Asteraceae) : a comparative approach. Evolution, 54, 2, 475–484.
data(tithonia) phy <- newick2phylog(tithonia$tre) tab <- log(tithonia$tab + 1) table.phylog(scalewt(tab), phy) gearymoran(phy$Wmat, tab) gearymoran(phy$Amat, tab)
data(tithonia) phy <- newick2phylog(tithonia$tre) tab <- log(tithonia$tab + 1) table.phylog(scalewt(tab), phy) gearymoran(phy$Wmat, tab) gearymoran(phy$Amat, tab)
This data set gives a morphological description (4 characters) of 48 turtles.
data(tortues)
data(tortues)
a data frame with 48 rows and 4 columns (length (mm), maximum width(mm), height (mm), gender).
Jolicoeur, P. and Mosimann, J. E. (1960) Size and shape variation in the painted turtle. A principal component analysis. Growth, 24, 339–354.
data(tortues) xyz <- as.matrix(tortues[, 1:3]) ref <- -svd(xyz)$u[, 1] pch0 <- c(1, 20)[as.numeric(tortues$sexe)] plot(ref, xyz[, 1], ylim = c(40, 180), pch = pch0) abline(lm(xyz[, 1] ~ -1 + ref)) points(ref,xyz[, 2], pch = pch0) abline(lm(xyz[, 2] ~ -1 + ref)) points(ref,xyz[, 3], pch = pch0) abline(lm(xyz[, 3] ~ -1 + ref))
data(tortues) xyz <- as.matrix(tortues[, 1:3]) ref <- -svd(xyz)$u[, 1] pch0 <- c(1, 20)[as.numeric(tortues$sexe)] plot(ref, xyz[, 1], ylim = c(40, 180), pch = pch0) abline(lm(xyz[, 1] ~ -1 + ref)) points(ref,xyz[, 2], pch = pch0) abline(lm(xyz[, 2] ~ -1 + ref)) points(ref,xyz[, 3], pch = pch0) abline(lm(xyz[, 3] ~ -1 + ref))
This data set gives the toxicity of 7 molecules on 17 targets expressed in -log(mol/liter)
data(toxicity)
data(toxicity)
toxicity
is a list of 3 components.
is a data frame with 7 columns and 17 rows
is a vector of the names of the species in the 17 targets
is a vector of the names of the 7 molecules
Devillers, J., Thioulouse, J. and Karcher W. (1993) Chemometrical Evaluation of Multispecies-Multichemical Data by Means of Graphical Techniques Combined with Multivariate Analyses. Ecotoxicology and Environnemental Safety, 26, 333–345.
data(toxicity) if(adegraphicsLoaded()) { table.image(toxicity$tab, labelsy = toxicity$species, labelsx = toxicity$chemicals, nclass = 7, ptable.margin = list(b = 5, l = 25, t = 25, r = 5), ptable.y.pos = "left", pgrid.draw = TRUE) table.value(toxicity$tab, labelsy = toxicity$species, labelsx = toxicity$chemicals, ptable.margin = list(b = 5, l = 5, t = 25, r = 26)) } else { table.paint(toxicity$tab, row.lab = toxicity$species, col.lab = toxicity$chemicals) table.value(toxicity$tab, row.lab = toxicity$species, col.lab = toxicity$chemicals) }
data(toxicity) if(adegraphicsLoaded()) { table.image(toxicity$tab, labelsy = toxicity$species, labelsx = toxicity$chemicals, nclass = 7, ptable.margin = list(b = 5, l = 25, t = 25, r = 5), ptable.y.pos = "left", pgrid.draw = TRUE) table.value(toxicity$tab, labelsy = toxicity$species, labelsx = toxicity$chemicals, ptable.margin = list(b = 5, l = 5, t = 25, r = 26)) } else { table.paint(toxicity$tab, row.lab = toxicity$species, col.lab = toxicity$chemicals) table.value(toxicity$tab, row.lab = toxicity$species, col.lab = toxicity$chemicals) }
Function to plot triangular data (i.e. dataframe with 3 columns of
positive or null values) and a partition
triangle.class(ta, fac, col = rep(1, length(levels(fac))), wt = rep(1, length(fac)), cstar = 1, cellipse = 0, axesell = TRUE, label = levels(fac), clabel = 1, cpoint = 1, pch = 20, draw.line = TRUE, addaxes = FALSE, addmean = FALSE, labeltriangle = TRUE, sub = "", csub = 1, possub = "bottomright", show.position = TRUE, scale = TRUE, min3 = NULL, max3 = NULL)
triangle.class(ta, fac, col = rep(1, length(levels(fac))), wt = rep(1, length(fac)), cstar = 1, cellipse = 0, axesell = TRUE, label = levels(fac), clabel = 1, cpoint = 1, pch = 20, draw.line = TRUE, addaxes = FALSE, addmean = FALSE, labeltriangle = TRUE, sub = "", csub = 1, possub = "bottomright", show.position = TRUE, scale = TRUE, min3 = NULL, max3 = NULL)
ta |
a data frame with 3 columns of null or positive numbers |
fac |
a factor of length the row number of |
col |
a vector of color for showing the groups |
wt |
a vector of row weighting for the computation of the gravity centers by class |
cstar |
a character size for plotting the stars between 0 (no stars) and 1 (complete star) for a line linking a point to the gravity center of its belonging class. |
cellipse |
a positive coefficient for the inertia ellipse size |
axesell |
a logical value indicating whether the ellipse axes should be drawn |
label |
a vector of strings of characters for the labels of gravity centers |
clabel |
if not NULL, a character size for the labels, used with |
cpoint |
a character size for plotting the points, used with |
pch |
if |
draw.line |
a logical value indicating whether the triangular lines should be drawn |
addaxes |
a logical value indicating whether the axes should be plotted |
addmean |
a logical value indicating whether the mean point should be plotted |
labeltriangle |
a logical value indicating whether the varliable labels of |
sub |
a string of characters for the graph title |
csub |
a character size for plotting the graph title |
possub |
a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", "bottomright") |
show.position |
a logical value indicating whether the sub-triangle containing the data should be put back in the total triangle |
scale |
a logical value for the graph representation : the total triangle (FALSE) or the sub-triangle (TRUE) |
min3 |
if not NULL, a vector with 3 numbers between 0 and 1 |
max3 |
if not NULL, a vector with 3 numbers between 0 and 1. Let notice that |
Daniel Chessel
if(!adegraphicsLoaded()) { data(euro123) par(mfrow = c(2, 2)) x <- rbind.data.frame(euro123$in78, euro123$in86, euro123$in97) triangle.plot(x) triangle.class(x, as.factor(rep("G", 36)), csta = 0.5, cell = 1) triangle.class(x, euro123$plan$an) triangle.class(x, euro123$plan$pays) triangle.class(x, euro123$plan$an, cell = 1, axesell = TRUE) triangle.class(x, euro123$plan$an, cell = 0, csta = 0, col = c("red", "green", "blue"), axesell = TRUE, clab = 2, cpoi = 2) triangle.class(x, euro123$plan$an, cell = 2, csta = 0.5, axesell = TRUE, clab = 1.5) triangle.class(x, euro123$plan$an, cell = 0, csta = 1, scale = FALSE, draw.line = FALSE, show.posi = FALSE) par(mfrow = c(2, 2)) }
if(!adegraphicsLoaded()) { data(euro123) par(mfrow = c(2, 2)) x <- rbind.data.frame(euro123$in78, euro123$in86, euro123$in97) triangle.plot(x) triangle.class(x, as.factor(rep("G", 36)), csta = 0.5, cell = 1) triangle.class(x, euro123$plan$an) triangle.class(x, euro123$plan$pays) triangle.class(x, euro123$plan$an, cell = 1, axesell = TRUE) triangle.class(x, euro123$plan$an, cell = 0, csta = 0, col = c("red", "green", "blue"), axesell = TRUE, clab = 2, cpoi = 2) triangle.class(x, euro123$plan$an, cell = 2, csta = 0.5, axesell = TRUE, clab = 1.5) triangle.class(x, euro123$plan$an, cell = 0, csta = 1, scale = FALSE, draw.line = FALSE, show.posi = FALSE) par(mfrow = c(2, 2)) }
Graphs for a dataframe with 3 columns of positive or null valuestriangle.plot
is a scatterplottriangle.biplot
is a paired scatterplotstriangle.posipoint
, triangle.param
, add.position.triangle
are utilitaries functions.
triangle.plot(ta, label = as.character(1:nrow(ta)), clabel = 0, cpoint = 1, draw.line = TRUE, addaxes = FALSE, addmean = FALSE, labeltriangle = TRUE, sub = "", csub = 0, possub = "topright", show.position = TRUE, scale = TRUE, min3 = NULL, max3 = NULL, box = FALSE) triangle.biplot (ta1, ta2, label = as.character(1:nrow(ta1)), draw.line = TRUE, show.position = TRUE, scale = TRUE)
triangle.plot(ta, label = as.character(1:nrow(ta)), clabel = 0, cpoint = 1, draw.line = TRUE, addaxes = FALSE, addmean = FALSE, labeltriangle = TRUE, sub = "", csub = 0, possub = "topright", show.position = TRUE, scale = TRUE, min3 = NULL, max3 = NULL, box = FALSE) triangle.biplot (ta1, ta2, label = as.character(1:nrow(ta1)), draw.line = TRUE, show.position = TRUE, scale = TRUE)
ta , ta1 , ta2
|
data frame with three columns, will be transformed in percentages by rows |
label |
a vector of strings of characters for the point labels |
clabel |
if not NULL, a character size for the labels, used with |
cpoint |
a character size for plotting the points, used with |
draw.line |
a logical value indicating whether the lines into the triangle should be drawn |
addaxes |
a logical value indicating whether the principal axes should be drawn |
addmean |
a logical value indicating whether the mean should be plotted |
labeltriangle |
a logical value indicating whether the variable names should be wrote |
sub |
a string of characters to be inserted as legend |
csub |
a character size for the legend, used with |
possub |
a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", "bottomright") |
show.position |
a logical value indicating whether the used triangle should be shown in the complete one |
scale |
a logical value indicating whether the smaller equilateral triangle containing the plot should be used |
min3 |
If scale is FALSE, a vector of three values for the minima e.g. c(0.1,0.1,0.1) can be used |
max3 |
If scale is FALSE a vector of three values for the maxima e.g. c(0.9,0.9,0.9) can be used |
box |
a logical value indicating whether a box around the current plot should be drawn |
triangle.plot
returns an invisible matrix containing the coordinates used for the plot. The graph can be supplemented in various ways.
Daniel Chessel
data(euro123) tot <- rbind.data.frame(euro123$in78, euro123$in86, euro123$in97) row.names(tot) <- paste(row.names(euro123$in78), rep(c(1, 2, 3), rep(12, 3)), sep = "") triangle.plot(tot, label = row.names(tot), clab = 1) par(mfrow = c(2, 2)) triangle.plot(euro123$in78, clab = 0, cpoi = 2, addmean = TRUE, show = FALSE) triangle.plot(euro123$in86, label = row.names(euro123$in78), clab = 0.8) triangle.biplot(euro123$in78, euro123$in86) triangle.plot(rbind.data.frame(euro123$in78, euro123$in86), clab = 1, addaxes = TRUE, sub = "Principal axis", csub = 2, possub = "topright") triangle.plot(euro123[[1]], min3 = c(0, 0.2, 0.3), max3 = c(0.5, 0.7, 0.8), clab = 1, label = row.names(euro123[[1]]), addax = TRUE) triangle.plot(euro123[[2]], min3 = c(0, 0.2, 0.3), max3 = c(0.5, 0.7, 0.8), clab = 1, label = row.names(euro123[[1]]), addax = TRUE) triangle.plot(euro123[[3]], min3 = c(0, 0.2, 0.3), max3 = c(0.5, 0.7, 0.8), clab = 1, label = row.names(euro123[[1]]), addax = TRUE) triangle.plot(rbind.data.frame(euro123[[1]], euro123[[2]], euro123[[3]])) par(mfrow = c(1, 1)) wtriangleplot <- cbind.data.frame(a = runif(100), b = runif(100), c = runif(100, 4, 5)) wtriangleplot <- triangle.plot(wtriangleplot) points(wtriangleplot, col = "blue", cex = 2) wtriangleplot <- colMeans(wtriangleplot) points(wtriangleplot[1], wtriangleplot[2], pch = 20, cex = 3, col = "red") rm(wtriangleplot)
data(euro123) tot <- rbind.data.frame(euro123$in78, euro123$in86, euro123$in97) row.names(tot) <- paste(row.names(euro123$in78), rep(c(1, 2, 3), rep(12, 3)), sep = "") triangle.plot(tot, label = row.names(tot), clab = 1) par(mfrow = c(2, 2)) triangle.plot(euro123$in78, clab = 0, cpoi = 2, addmean = TRUE, show = FALSE) triangle.plot(euro123$in86, label = row.names(euro123$in78), clab = 0.8) triangle.biplot(euro123$in78, euro123$in86) triangle.plot(rbind.data.frame(euro123$in78, euro123$in86), clab = 1, addaxes = TRUE, sub = "Principal axis", csub = 2, possub = "topright") triangle.plot(euro123[[1]], min3 = c(0, 0.2, 0.3), max3 = c(0.5, 0.7, 0.8), clab = 1, label = row.names(euro123[[1]]), addax = TRUE) triangle.plot(euro123[[2]], min3 = c(0, 0.2, 0.3), max3 = c(0.5, 0.7, 0.8), clab = 1, label = row.names(euro123[[1]]), addax = TRUE) triangle.plot(euro123[[3]], min3 = c(0, 0.2, 0.3), max3 = c(0.5, 0.7, 0.8), clab = 1, label = row.names(euro123[[1]]), addax = TRUE) triangle.plot(rbind.data.frame(euro123[[1]], euro123[[2]], euro123[[3]])) par(mfrow = c(1, 1)) wtriangleplot <- cbind.data.frame(a = runif(100), b = runif(100), c = runif(100, 4, 5)) wtriangleplot <- triangle.plot(wtriangleplot) points(wtriangleplot, col = "blue", cex = 2) wtriangleplot <- colMeans(wtriangleplot) points(wtriangleplot[1], wtriangleplot[2], pch = 20, cex = 3, col = "red") rm(wtriangleplot)
This data set gives for trappong nights informations about species and meteorological variables.
data(trichometeo)
data(trichometeo)
trichometeo
is a list of 3 components.
is a data frame with 49 rows (trapping nights) and 17 species.
is a data frame with 49 rows and 11 meteorological variables.
is a factor of 12 levels for the definition of the consecutive night groups
Data from P. Usseglio-Polatera
Usseglio-Polatera, P. and Auda, Y. (1987) Influence des facteurs météorologiques sur les résultats de piégeage lumineux. Annales de Limnologie, 23, 65–79. (code des espèces p. 76)
See a data description at http://pbil.univ-lyon1.fr/R/pdf/pps034.pdf (in French).
data(trichometeo) faulog <- log(trichometeo$fau + 1) pca1 <- dudi.pca(trichometeo$meteo, scan = FALSE) niche1 <- niche(pca1, faulog, scan = FALSE) if(adegraphicsLoaded()) { g1 <- s.distri(niche1$ls, faulog, plab.cex = 0.6, ellipseSize = 0, starSize = 0.3, plot = FALSE) g2 <- s.arrow(7 * niche1$c1, plab.cex = 1, plot = FALSE) G <- superpose(g1, g2, plot = TRUE) } else { s.label(niche1$ls, clab = 0) s.distri(niche1$ls, faulog, clab = 0.6, add.p = TRUE, cell = 0, csta = 0.3) s.arrow(7 * niche1$c1, clab = 1, add.p = TRUE) }
data(trichometeo) faulog <- log(trichometeo$fau + 1) pca1 <- dudi.pca(trichometeo$meteo, scan = FALSE) niche1 <- niche(pca1, faulog, scan = FALSE) if(adegraphicsLoaded()) { g1 <- s.distri(niche1$ls, faulog, plab.cex = 0.6, ellipseSize = 0, starSize = 0.3, plot = FALSE) g2 <- s.arrow(7 * niche1$c1, plab.cex = 1, plot = FALSE) G <- superpose(g1, g2, plot = TRUE) } else { s.label(niche1$ls, clab = 0) s.distri(niche1$ls, faulog, clab = 0.6, add.p = TRUE, cell = 0, csta = 0.3) s.arrow(7 * niche1$c1, clab = 1, add.p = TRUE) }
This data set describes the phylogeny of 18 ungulates as reported by Pélabon et al. (1995). It also gives 4 traits corresponding to these 18 species.
data(ungulates)
data(ungulates)
fission
is a list containing the 2 following objects :
is a character string giving the phylogenetic tree in Newick format.
is a data frame with 18 species and 4 traits
Variables of ungulates$tab
are the following ones :
afbw: is a numeric vector that describes the adult female body weight (g)
mnw: is a numeric vector that describes the male neonatal weight (g)
fnw: is a numeric vector that describes the female neonatal weight (g)
ls: is a numeric vector that describes the litter size
Data were obtained from Pélabon, C., Gaillard, J.M., Loison, A. and Portier, A. (1995) Is sex-biased maternal care limited by total maternal expenditure in polygynous ungulates? Behavioral Ecology and Sociobiology, 37, 311–319.
data(ungulates) ung.phy <- newick2phylog(ungulates$tre) plot(ung.phy,clabel.l=1.25,clabel.n=0.75) ung.x <- log(ungulates$tab[,1]) ung.y <- log((ungulates$tab[,2]+ungulates$tab[,3])/2) names(ung.x) <- names(ung.phy$leaves) names(ung.y) <- names(ung.x) plot(ung.x,ung.y) abline(lm(ung.y~ung.x)) symbols.phylog(ung.phy,ung.x-mean(ung.x)) dotchart.phylog(ung.phy,ung.x,cle=1.5,cno=1.5,cdot=1) if (requireNamespace("adephylo", quietly = TRUE) & requireNamespace("ape", quietly = TRUE)) { tre <- ape::read.tree(text = ungulates$tre) adephylo::orthogram(ung.x, tre) ung.z <- residuals(lm(ung.y~ung.x)) names(ung.z) <- names(ung.phy$leaves) dotchart.phylog(ung.phy,ung.z,cle=1.5,cno=1.5,cdot=1,ceti=0.75) adephylo::orthogram(ung.z, tre) }
data(ungulates) ung.phy <- newick2phylog(ungulates$tre) plot(ung.phy,clabel.l=1.25,clabel.n=0.75) ung.x <- log(ungulates$tab[,1]) ung.y <- log((ungulates$tab[,2]+ungulates$tab[,3])/2) names(ung.x) <- names(ung.phy$leaves) names(ung.y) <- names(ung.x) plot(ung.x,ung.y) abline(lm(ung.y~ung.x)) symbols.phylog(ung.phy,ung.x-mean(ung.x)) dotchart.phylog(ung.phy,ung.x,cle=1.5,cno=1.5,cdot=1) if (requireNamespace("adephylo", quietly = TRUE) & requireNamespace("ape", quietly = TRUE)) { tre <- ape::read.tree(text = ungulates$tre) adephylo::orthogram(ung.x, tre) ung.z <- residuals(lm(ung.y~ung.x)) names(ung.z) <- names(ung.phy$leaves) dotchart.phylog(ung.phy,ung.z,cle=1.5,cno=1.5,cdot=1,ceti=0.75) adephylo::orthogram(ung.z, tre) }
An utility function to eliminate the duplicated rows in a array.
uniquewt.df(x)
uniquewt.df(x)
x |
a data frame which contains duplicated rows |
The function returns a y
which contains once each duplicated row of x
.y
is an attribut 'factor' which gives the number of the row of y
in which each row of x
is foundy
is an attribut 'length.class' which gives the number of duplicates in x
with an attribut of each row of y
with an attribut
Daniel Chessel
data(ecomor) forsub.r <- uniquewt.df(ecomor$forsub) attr(forsub.r, "factor") forsub.r[1,] ecomor$forsub[126,] #idem dudi.pca(ecomor$forsub, scale = FALSE, scann = FALSE)$eig # [1] 0.36845 0.24340 0.15855 0.09052 0.07970 0.04490 w1 <- attr(forsub.r, "len.class") / sum(attr(forsub.r,"len.class")) dudi.pca(forsub.r, row.w = w1, scale = FALSE, scann = FALSE)$eig # [1] 0.36845 0.24340 0.15855 0.09052 0.07970 0.04490
data(ecomor) forsub.r <- uniquewt.df(ecomor$forsub) attr(forsub.r, "factor") forsub.r[1,] ecomor$forsub[126,] #idem dudi.pca(ecomor$forsub, scale = FALSE, scann = FALSE)$eig # [1] 0.36845 0.24340 0.15855 0.09052 0.07970 0.04490 w1 <- attr(forsub.r, "len.class") / sum(attr(forsub.r,"len.class")) dudi.pca(forsub.r, row.w = w1, scale = FALSE, scann = FALSE)$eig # [1] 0.36845 0.24340 0.15855 0.09052 0.07970 0.04490
This function performs the variance analysis of a trait on eigenvectors associated to a phylogenetic tree.
variance.phylog(phylog, z, bynames = TRUE, na.action = c("fail", "mean"))
variance.phylog(phylog, z, bynames = TRUE, na.action = c("fail", "mean"))
phylog |
: an object of class |
z |
: a numeric vector of the values corresponding to the variable |
bynames |
: if TRUE checks if |
na.action |
: if 'fail' stops the execution of the current expression when |
phylog$Amat
defines a set of orthonormal vectors associated the each nodes of the phylogenetic tree. phylog$Adim
defines the dimension of the subspace A defined by
the first phylog$Adim
vectors of phylog$Amat
that corresponds to phylogenetic inertia. variance.phylog
performs the linear regression of z
on A.
Returns a list containing
lm |
: an object of class |
anova |
: an object of class |
smry |
: an object of class |
Sébastien Ollier [email protected]
Daniel Chessel
Grafen, A. (1989) The phylogenetic regression. Philosophical Transactions of the Royal Society London B, 326, 119–156.
Diniz-Filho, J. A. F., Sant'Ana, C.E.R. and Bini, L.M. (1998) An eigenvector method for estimating phylogenetic inertia. Evolution, 52, 1247–1262.
data(njplot) njplot.phy <- newick2phylog(njplot$tre) variance.phylog(njplot.phy,njplot$tauxcg) par(mfrow = c(1,2)) table.phylog(njplot.phy$Ascores, njplot.phy, clabel.row = 0, clabel.col = 0.1, clabel.nod = 0.6, csize = 1) dotchart.phylog(njplot.phy, njplot$tauxcg, clabel.nodes = 0.6) if (requireNamespace("adephylo", quietly = TRUE) & requireNamespace("ape", quietly = TRUE)) { tre <- ape::read.tree(text = njplot$tre) adephylo::orthogram(njplot$tauxcg, tre = tre) }
data(njplot) njplot.phy <- newick2phylog(njplot$tre) variance.phylog(njplot.phy,njplot$tauxcg) par(mfrow = c(1,2)) table.phylog(njplot.phy$Ascores, njplot.phy, clabel.row = 0, clabel.col = 0.1, clabel.nod = 0.6, csize = 1) dotchart.phylog(njplot.phy, njplot$tauxcg, clabel.nodes = 0.6) if (requireNamespace("adephylo", quietly = TRUE) & requireNamespace("ape", quietly = TRUE)) { tre <- ape::read.tree(text = njplot$tre) adephylo::orthogram(njplot$tauxcg, tre = tre) }
The function partitions the variation of a response table (usually community data) with respect to two
explanatory tables. The function performs the variation partitioning based on
redundancy analysis (RDA, if dudiY
is obtained by dudi.pca
) or canonical correspondance analysis (CCA, if dudiY
is obtained by dudi.coa
)
and computes
unadjusted and adjusted R-squared.
The significance of R-squared are evaluated by a randomization procedure
where the rows of the explanatory tables are permuted.
varipart(Y, X, W = NULL, nrepet = 999, type = c("simulated", "parametric"), scale = FALSE, ...) ## S3 method for class 'varipart' print(x, ...)
varipart(Y, X, W = NULL, nrepet = 999, type = c("simulated", "parametric"), scale = FALSE, ...) ## S3 method for class 'varipart' print(x, ...)
Y |
a vector, matrix or data frame or an object of class |
X , W
|
dataframes or matrices of explanatory (co)variables (numeric and/or factor
variables). By default, no covariables are considered ( |
nrepet |
an integer indicating the number of permutations. |
type |
a character specifying the algorithm which should be used to adjust R-squared (either |
scale |
If |
... |
further arguments passed to |
x |
an object of class |
Two types of algorithm are provided to adjust R-squared. The "simulated" procedure estimates the unadjusted R-squared expected under the null hypothesis H0 and uses it to adjust the observed R-squared as follows: R2.adj = 1 - (1 - R2) / (1 - E(R2|H0)) with R2.adj the adjusted R-squared and R2 the unadjusted R-squared. The "parametric" procedure performs the Ezequiel's adjustement on the unadjusted R-squared as: R2.adj = 1 - (1 - R2) / (1 - p / (n - 1)) where n is the number of sites, and p the number of predictors.
It returns an object of class varipart
. It is a list
with:
test
the significance test of fractions [ab], [bc], and [abc] based on randomization procedure. An object of class krandtest
R2
unadjusted estimations of fractions [a], [b], [c], and [d]
R2.adj
adjusted estimations of fractions [a], [b], [c], and [d]
call
the matched call
Stephane Dray [email protected] and Sylvie Clappe [email protected]
Borcard, D., P. Legendre, and P. Drapeau. 1992. Partialling out the spatial component of ecological variation. Ecology 73:1045.
Peres-Neto, P. R., P. Legendre, S. Dray, and D. Borcard. 2006. Variation partitioning of species data matrices: estimation and comparison of fractions. Ecology 87:2614-2625.
data(mafragh) # PCA on response table Y Y <- mafragh$flo dudiY <- dudi.pca(Y, scannf = FALSE, scale = FALSE) # Variation partitioning based on RDA # without covariables vprda <- varipart(dudiY, mafragh$env) vprda # Variation partitioning based on RDA # with covariables and parametric estimation vprda <- varipart(dudiY, mafragh$env, mafragh$xy, type = "parametric") vprda names(vprda)
data(mafragh) # PCA on response table Y Y <- mafragh$flo dudiY <- dudi.pca(Y, scannf = FALSE, scale = FALSE) # Variation partitioning based on RDA # without covariables vprda <- varipart(dudiY, mafragh$env) vprda # Variation partitioning based on RDA # with covariables and parametric estimation vprda <- varipart(dudiY, mafragh$env, mafragh$xy, type = "parametric") vprda names(vprda)
This data set contains abundance values (Braun-Blanquet scale) of 80 plant species for 337 sites. Data have been collected by Sonia Said and Francois Debias.
data(vegtf)
data(vegtf)
vegtf
is a list with the following components:
a data.frame with the abundance values of 80 species (columns) in 337 sites (rows)
a data.frame with the spatial coordinates of the sites
a data.frame (area) which define the boundaries of the study site
a vector containing the species latin names
a neighborhood object (class nb
defined in package spdep
)
an object of the class SpatialPolygons
of sp
,
containing the map
Dray, S., Said, S. and Debias, F. (2008) Spatial ordination of vegetation data using a generalization of Wartenberg's multivariate spatial correlation. Journal of vegetation science, 19, 45–56.
if(requireNamespace("spdep", quietly = TRUE) & requireNamespace("adespatial", quietly = TRUE)) { data(vegtf) coa1 <- dudi.coa(vegtf$veg, scannf = FALSE) ms.coa1 <- adespatial::multispati(coa1, listw = spdep::nb2listw(vegtf$nb), nfposi = 2, nfnega = 0, scannf = FALSE) summary(ms.coa1) plot(ms.coa1) if(adegraphicsLoaded()) { g1 <- s.value(vegtf$xy, coa1$li[, 1], Sp = vegtf$Spatial, pSp.col = "white", plot = FALSE) g2 <- s.value(vegtf$xy, ms.coa1$li[, 1], Sp = vegtf$Spatial, pSp.col = "white", plot = FALSE) g3 <- s.label(coa1$c1, plot = FALSE) g4 <- s.label(ms.coa1$c1, plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) s.value(vegtf$xy, coa1$li[, 1], area = vegtf$area, include.origin = FALSE) s.value(vegtf$xy, ms.coa1$li[, 1], area = vegtf$area, include.origin = FALSE) s.label(coa1$c1) s.label(ms.coa1$c1) } }
if(requireNamespace("spdep", quietly = TRUE) & requireNamespace("adespatial", quietly = TRUE)) { data(vegtf) coa1 <- dudi.coa(vegtf$veg, scannf = FALSE) ms.coa1 <- adespatial::multispati(coa1, listw = spdep::nb2listw(vegtf$nb), nfposi = 2, nfnega = 0, scannf = FALSE) summary(ms.coa1) plot(ms.coa1) if(adegraphicsLoaded()) { g1 <- s.value(vegtf$xy, coa1$li[, 1], Sp = vegtf$Spatial, pSp.col = "white", plot = FALSE) g2 <- s.value(vegtf$xy, ms.coa1$li[, 1], Sp = vegtf$Spatial, pSp.col = "white", plot = FALSE) g3 <- s.label(coa1$c1, plot = FALSE) g4 <- s.label(ms.coa1$c1, plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) s.value(vegtf$xy, coa1$li[, 1], area = vegtf$area, include.origin = FALSE) s.value(vegtf$xy, ms.coa1$li[, 1], area = vegtf$area, include.origin = FALSE) s.label(coa1$c1) s.label(ms.coa1$c1) } }
The data come from the INSEE (National Institute of Statistics and Economical Studies). It is an array of widower percentages in relation with the age and the socioprofessional category.
data(veuvage)
data(veuvage)
veuvage
is a list of 2 components.
is a data frame with 37 rows (widowers) 6 columns (socio-professional categories)
is a vector of the ages of the 37 widowers.
The columns contain the socioprofessional categories:
1- Farmers, 2- Craftsmen, 3- Executives and higher intellectual professions,
4- Intermediate Professions, 5- Others white-collar workers and 6- Manual workers.
unknown
data(veuvage) par(mfrow = c(3,2)) for (j in 1:6) plot(veuvage$age, veuvage$tab[,j], xlab = "age", ylab = "pourcentage de veufs", type = "b", main = names(veuvage$tab)[j])
data(veuvage) par(mfrow = c(3,2)) for (j in 1:6) plot(veuvage$age, veuvage$tab[,j], xlab = "age", ylab = "pourcentage de veufs", type = "b", main = names(veuvage$tab)[j])
Performs a particular case of an Orthogonal Principal Component Analysis with respect to Instrumental Variables (orthopcaiv), in which there is only a single factor as covariable.
## S3 method for class 'dudi' wca(x, fac, scannf = TRUE, nf = 2, ...)
## S3 method for class 'dudi' wca(x, fac, scannf = TRUE, nf = 2, ...)
x |
a duality diagram, object of class |
fac |
a factor partitioning the rows of |
scannf |
a logical value indicating whether the eigenvalues bar plot should be displayed |
nf |
if scannf FALSE, an integer indicating the number of kept axes |
... |
further arguments passed to or from other methods |
Returns a list of the sub-class within
in the class dudi
tab |
a data frame containing the transformed data (substraction of the class mean) |
call |
the matching call |
nf |
number of kept axes |
rank |
the rank of the analysis |
ratio |
percentage of within-class inertia |
eig |
a numeric vector containing the eigenvalues |
lw |
a numeric vector of row weigths |
cw |
a numeric vector of column weigths |
tabw |
a numeric vector of class weigths |
fac |
the factor defining the classes |
li |
data frame row coordinates |
l1 |
data frame row normed scores |
co |
data frame column coordinates |
c1 |
data frame column normed scores |
ls |
data frame supplementary row coordinates |
as |
data frame inertia axis onto within axis |
To avoid conflict names with the base:::within
function, the
function within
is now deprecated and removed. It
is replaced by the method wca.dudi
of the new generic wca
function.
Daniel Chessel
Anne-Béatrice Dufour [email protected]
Benzécri, J. P. (1983) Analyse de l'inertie intra-classe par l'analyse d'un
tableau de correspondances. Les Cahiers de l'Analyse des données, 8, 351–358.
Dolédec, S. and Chessel, D. (1987) Rythmes saisonniers et composantes stationnelles
en milieu aquatique I- Description d'un plan d'observations complet par projection de
variables. Acta Oecologica, Oecologia Generalis, 8, 3, 403–426.
data(meaudret) pca1 <- dudi.pca(meaudret$env, scan = FALSE, nf = 4) wit1 <- wca(pca1, meaudret$design$site, scan = FALSE, nf = 2) if(adegraphicsLoaded()) { g1 <- s.traject(pca1$li, meaudret$design$site, psub.text = "Principal Component Analysis", plines.lty = 1:nlevels(meaudret$design$site), psub.cex = 1.5, plot = FALSE) g2 <- s.traject(wit1$li, meaudret$design$site, psub.text = "Within site Principal Component Analysis", plines.lty = 1:nlevels(meaudret$design$site), psub.cex = 1.5, plot = FALSE) g3 <- s.corcircle (wit1$as, plot = FALSE) G <- ADEgS(list(g1, g2, g3), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) s.traject(pca1$li, meaudret$design$site, sub = "Principal Component Analysis", csub = 1.5) s.traject(wit1$li, meaudret$design$site, sub = "Within site Principal Component Analysis", csub = 1.5) s.corcircle (wit1$as) par(mfrow = c(1,1)) } plot(wit1)
data(meaudret) pca1 <- dudi.pca(meaudret$env, scan = FALSE, nf = 4) wit1 <- wca(pca1, meaudret$design$site, scan = FALSE, nf = 2) if(adegraphicsLoaded()) { g1 <- s.traject(pca1$li, meaudret$design$site, psub.text = "Principal Component Analysis", plines.lty = 1:nlevels(meaudret$design$site), psub.cex = 1.5, plot = FALSE) g2 <- s.traject(wit1$li, meaudret$design$site, psub.text = "Within site Principal Component Analysis", plines.lty = 1:nlevels(meaudret$design$site), psub.cex = 1.5, plot = FALSE) g3 <- s.corcircle (wit1$as, plot = FALSE) G <- ADEgS(list(g1, g2, g3), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) s.traject(pca1$li, meaudret$design$site, sub = "Principal Component Analysis", csub = 1.5) s.traject(wit1$li, meaudret$design$site, sub = "Within site Principal Component Analysis", csub = 1.5) s.corcircle (wit1$as) par(mfrow = c(1,1)) } plot(wit1)
Performs a within-class analysis after a coinertia analysis
## S3 method for class 'coinertia' wca(x, fac, scannf = TRUE, nf = 2, ...)
## S3 method for class 'coinertia' wca(x, fac, scannf = TRUE, nf = 2, ...)
x |
a coinertia analysis (object of class coinertia) obtained by the function coinertia |
fac |
a factor partitioning the rows in classes |
scannf |
a logical value indicating whether the eigenvalues barplot should be displayed |
nf |
if scannf FALSE, an integer indicating the number of kept axes |
... |
further arguments passed to or from other methods |
This analysis is equivalent to do a within-class analysis on each initial dudi, and a coinertia analysis on the two within analyses. This function returns additional outputs for the interpretation.
An object of the class witcoi
. Outputs are described by the
print
function
To avoid conflict names with the base:::within
function, the
function within
is now deprecated and removed. To be
consistent, the withincoinertia
function is also deprecated and
is replaced by the method wca.coinertia
of the generic wca
function.
Stéphane Dray [email protected] and Jean Thioulouse [email protected]
Franquet E., Doledec S., and Chessel D. (1995) Using multivariate analyses for separating spatial and temporal effects within species-environment relationships. Hydrobiologia, 300, 425–431.
data(meaudret) pca1 <- dudi.pca(meaudret$env, scan = FALSE, nf = 4) pca2 <- dudi.pca(meaudret$spe, scal = FALSE, scan = FALSE, nf = 4) wit1 <- wca(pca1, meaudret$design$site, scan = FALSE, nf = 2) wit2 <- wca(pca2, meaudret$design$site, scan = FALSE, nf = 2) coiw <- coinertia(wit1, wit2, scannf = FALSE) coi <- coinertia(pca1, pca2, scannf = FALSE, nf = 3) coi.w <- wca(coi, meaudret$design$site, scannf = FALSE) ## coiw and coi.w are equivalent plot(coi.w)
data(meaudret) pca1 <- dudi.pca(meaudret$env, scan = FALSE, nf = 4) pca2 <- dudi.pca(meaudret$spe, scal = FALSE, scan = FALSE, nf = 4) wit1 <- wca(pca1, meaudret$design$site, scan = FALSE, nf = 2) wit2 <- wca(pca2, meaudret$design$site, scan = FALSE, nf = 2) coiw <- coinertia(wit1, wit2, scannf = FALSE) coi <- coinertia(pca1, pca2, scannf = FALSE, nf = 3) coi.w <- wca(coi, meaudret$design$site, scannf = FALSE) ## coiw and coi.w are equivalent plot(coi.w)
Performs a particular RLQ analysis where a partition of sites (rows of R) is taken into account. The within-class RLQ analysis search for linear combinations of traits and environmental variables of maximal covariance.
## S3 method for class 'rlq' wca(x, fac, scannf = TRUE, nf = 2, ...) ## S3 method for class 'witrlq' plot(x, xax = 1, yax = 2, ...) ## S3 method for class 'witrlq' print(x, ...)
## S3 method for class 'rlq' wca(x, fac, scannf = TRUE, nf = 2, ...) ## S3 method for class 'witrlq' plot(x, xax = 1, yax = 2, ...) ## S3 method for class 'witrlq' print(x, ...)
x |
an object of class rlq (created by the |
fac |
a factor partitioning the rows of R |
scannf |
a logical value indicating whether the eigenvalues bar plot should be displayed |
nf |
if scannf FALSE, an integer indicating the number of kept axes |
xax |
the column number for the x-axis |
yax |
the column number for the y-axis |
... |
further arguments passed to or from other methods |
The wca.rlq
function returns an object of class 'betrlq'
(sub-class of 'dudi'). See the outputs of the print
function
for more details.
Stéphane Dray [email protected]
Wesuls, D., Oldeland, J. and Dray, S. (2012) Disentangling plant trait responses to livestock grazing from spatio-temporal variation: the partial RLQ approach. Journal of Vegetation Science, 23, 98–113.
data(piosphere) afcL <- dudi.coa(log(piosphere$veg + 1), scannf = FALSE) acpR <- dudi.pca(piosphere$env, scannf = FALSE, row.w = afcL$lw) acpQ <- dudi.hillsmith(piosphere$traits, scannf = FALSE, row.w = afcL$cw) rlq1 <- rlq(acpR, afcL, acpQ, scannf = FALSE) wrlq1 <- wca(rlq1, fac = piosphere$habitat, scannf = FALSE) wrlq1 plot(wrlq1)
data(piosphere) afcL <- dudi.coa(log(piosphere$veg + 1), scannf = FALSE) acpR <- dudi.pca(piosphere$env, scannf = FALSE, row.w = afcL$lw) acpQ <- dudi.hillsmith(piosphere$traits, scannf = FALSE, row.w = afcL$cw) rlq1 <- rlq(acpR, afcL, acpQ, scannf = FALSE) wrlq1 <- wca(rlq1, fac = piosphere$habitat, scannf = FALSE) wrlq1 plot(wrlq1)
This data set contains informations about faunal similarities between river basins in West africa.
data(westafrica)
data(westafrica)
westafrica
is a list containing the following objects :
: a data frame with absence/presence of 268 species (rows) at 33 embouchures (columns)
: a vector of string of characters with the name of species
: a data frame with the genus and species (columns) of the 256 species (rows)
: a vector of string of characters with the name of rivers
: a data frame with the coordinates of a polygon that represents the limits of atlantic (see example)
: a data frame with the coordinates of embouchures
: a data frame with the coordinates of lines to complete the representation (see example)
: a data frame with the coordinates of points used to make the representation (see example)
Data provided by B. Hugueny [email protected].
Paugy, D., Traoré, K. and Diouf, P.F. (1994) Faune ichtyologique des eaux douces d'Afrique de l'Ouest. In Diversité biologique des poissons des eaux douces et saumâtres d'Afrique. Synthèses géographiques, Teugels, G.G., Guégan, J.F. and Albaret, J.J. (Editors). Annales du Musée Royal de l'Afrique Centrale, Zoologie, 275, Tervuren, Belgique, 35–66.
Hugueny, B. (1989) Biogéographie et structure des peuplements de Poissons d'eau douce de l'Afrique de l'ouest : approches quantitatives. Thèse de doctorat, Université Paris 7.
Hugueny, B., and Lévêque, C. (1994) Freshwater fish zoogeography in west Africa: faunal similarities between river basins. Environmental Biology of Fishes, 39, 365–380.
data(westafrica) if(!adegraphicsLoaded()) { s.label(westafrica$cadre, xlim = c(30, 500), ylim = c(50, 290), cpoi = 0, clab = 0, grid = FALSE, addax = 0) old.par <- par(no.readonly = TRUE) par(mar = c(0.1, 0.1, 0.1, 0.1)) rect(30, 0, 500, 290) polygon(westafrica$atlantic, col = "lightblue") points(westafrica$riv.xy, pch = 20, cex = 1.5) apply(westafrica$lines, 1, function(x) segments(x[1], x[2], x[3], x[4], lwd = 1)) apply(westafrica$riv.xy,1, function(x) segments(x[1], x[2], x[3], x[4], lwd = 1)) text(c(175, 260, 460, 420), c(275, 200, 250, 100), c("Senegal", "Niger", "Niger", "Volta")) par(srt = 270) text(westafrica$riv.xy$x2, westafrica$riv.xy$y2-10, westafrica$riv.names, adj = 0, cex = 0.75) par(old.par) rm(old.par) } # multivariate analysis afri.w <- data.frame(t(westafrica$tab)) afri.dist <- dist.binary(afri.w,1) afri.pco <- dudi.pco(afri.dist, scannf = FALSE, nf = 3) if(adegraphicsLoaded()) { G1 <- s1d.barchart(afri.pco$li[, 1:3], p1d.horizontal = FALSE, plabels.cex = 0) } else { par(mfrow = c(3, 1)) barplot(afri.pco$li[, 1]) barplot(afri.pco$li[, 2]) barplot(afri.pco$li[, 3]) } if(requireNamespace("spdep", quietly = TRUE) & requireNamespace("adespatial", quietly = TRUE)) { # multivariate spatial analysis afri.neig <- neig(n.line = 33) afri.nb <- neig2nb(afri.neig) afri.listw <- spdep::nb2listw(afri.nb) afri.ms <- adespatial::multispati(afri.pco, afri.listw, scannf = FALSE, nfposi = 6, nfnega = 0) if(adegraphicsLoaded()) { G2 <- s1d.barchart(afri.ms$li[, 1:3], p1d.horizontal = FALSE, plabels.cex = 0) g31 <- s.label(afri.ms$li, plabels.cex = 0.75, ppoints.cex = 0, nb = afri.nb, plot = FALSE) g32 <- s.value(afri.ms$li, afri.ms$li[, 3], plot = FALSE) g33 <- s.value(afri.ms$li, afri.ms$li[, 4], plot = FALSE) g34 <- s.value(afri.ms$li, afri.ms$li[, 5], plot = FALSE) G3 <- ADEgS(list(g31, g32, g33, g34), layout = c(2, 2)) } else { par(mfrow = c(3, 1)) barplot(afri.ms$li[, 1]) barplot(afri.ms$li[, 2]) barplot(afri.ms$li[, 3]) par(mfrow = c(2, 2)) s.label(afri.ms$li, clab = 0.75, cpoi = 0, neig = afri.neig, cneig = 1.5) s.value(afri.ms$li, afri.ms$li[, 3]) s.value(afri.ms$li, afri.ms$li[, 4]) s.value(afri.ms$li, afri.ms$li[, 5]) } summary(afri.ms) } par(mfrow = c(1, 1)) plot(hclust(afri.dist, "ward.D"), h = -0.2)
data(westafrica) if(!adegraphicsLoaded()) { s.label(westafrica$cadre, xlim = c(30, 500), ylim = c(50, 290), cpoi = 0, clab = 0, grid = FALSE, addax = 0) old.par <- par(no.readonly = TRUE) par(mar = c(0.1, 0.1, 0.1, 0.1)) rect(30, 0, 500, 290) polygon(westafrica$atlantic, col = "lightblue") points(westafrica$riv.xy, pch = 20, cex = 1.5) apply(westafrica$lines, 1, function(x) segments(x[1], x[2], x[3], x[4], lwd = 1)) apply(westafrica$riv.xy,1, function(x) segments(x[1], x[2], x[3], x[4], lwd = 1)) text(c(175, 260, 460, 420), c(275, 200, 250, 100), c("Senegal", "Niger", "Niger", "Volta")) par(srt = 270) text(westafrica$riv.xy$x2, westafrica$riv.xy$y2-10, westafrica$riv.names, adj = 0, cex = 0.75) par(old.par) rm(old.par) } # multivariate analysis afri.w <- data.frame(t(westafrica$tab)) afri.dist <- dist.binary(afri.w,1) afri.pco <- dudi.pco(afri.dist, scannf = FALSE, nf = 3) if(adegraphicsLoaded()) { G1 <- s1d.barchart(afri.pco$li[, 1:3], p1d.horizontal = FALSE, plabels.cex = 0) } else { par(mfrow = c(3, 1)) barplot(afri.pco$li[, 1]) barplot(afri.pco$li[, 2]) barplot(afri.pco$li[, 3]) } if(requireNamespace("spdep", quietly = TRUE) & requireNamespace("adespatial", quietly = TRUE)) { # multivariate spatial analysis afri.neig <- neig(n.line = 33) afri.nb <- neig2nb(afri.neig) afri.listw <- spdep::nb2listw(afri.nb) afri.ms <- adespatial::multispati(afri.pco, afri.listw, scannf = FALSE, nfposi = 6, nfnega = 0) if(adegraphicsLoaded()) { G2 <- s1d.barchart(afri.ms$li[, 1:3], p1d.horizontal = FALSE, plabels.cex = 0) g31 <- s.label(afri.ms$li, plabels.cex = 0.75, ppoints.cex = 0, nb = afri.nb, plot = FALSE) g32 <- s.value(afri.ms$li, afri.ms$li[, 3], plot = FALSE) g33 <- s.value(afri.ms$li, afri.ms$li[, 4], plot = FALSE) g34 <- s.value(afri.ms$li, afri.ms$li[, 5], plot = FALSE) G3 <- ADEgS(list(g31, g32, g33, g34), layout = c(2, 2)) } else { par(mfrow = c(3, 1)) barplot(afri.ms$li[, 1]) barplot(afri.ms$li[, 2]) barplot(afri.ms$li[, 3]) par(mfrow = c(2, 2)) s.label(afri.ms$li, clab = 0.75, cpoi = 0, neig = afri.neig, cneig = 1.5) s.value(afri.ms$li, afri.ms$li[, 3]) s.value(afri.ms$li, afri.ms$li[, 4]) s.value(afri.ms$li, afri.ms$li[, 5]) } summary(afri.ms) } par(mfrow = c(1, 1)) plot(hclust(afri.dist, "ward.D"), h = -0.2)
Outputs and graphical representations of the results of a within-class analysis.
## S3 method for class 'within' plot(x, xax = 1, yax = 2, ...) ## S3 method for class 'within' print(x, ...) ## S3 method for class 'witcoi' plot(x, xax = 1, yax = 2, ...) ## S3 method for class 'witcoi' print(x, ...) ## S3 method for class 'within' summary(object, ...)
## S3 method for class 'within' plot(x, xax = 1, yax = 2, ...) ## S3 method for class 'within' print(x, ...) ## S3 method for class 'witcoi' plot(x, xax = 1, yax = 2, ...) ## S3 method for class 'witcoi' print(x, ...) ## S3 method for class 'within' summary(object, ...)
x , object
|
an object of class |
xax |
the column index for the x-axis |
yax |
the column index for the y-axis |
... |
further arguments passed to or from other methods |
Daniel Chessel
Anne-Béatrice Dufour [email protected]
Stéphane Dray [email protected]
Benzécri, J. P. (1983) Analyse de l'inertie intra-classe par l'analyse d'un tableau de correspondances. Les Cahiers de l'Analyse des données, 8, 351–358.
Dolédec, S. and Chessel, D. (1987) Rythmes saisonniers et composantes stationnelles en milieu aquatique I- Description d'un plan d'observations complet par projection de variables. Acta Oecologica, Oecologia Generalis, 8, 3, 403–426.
data(meaudret) pca1 <- dudi.pca(meaudret$env, scan = FALSE, nf = 4) wit1 <- wca(pca1, meaudret$design$site, scan = FALSE, nf = 2) if(adegraphicsLoaded()) { g1 <- s.traject(pca1$li, meaudret$design$site, psub.text = "Principal Component Analysis", plines.lty = 1:length(levels(meaudret$design$site)), plot = FALSE) g2 <- s.traject(wit1$li, meaudret$design$site, psub.text = "Within site Principal Component Analysis", plines.lty = 1:length(levels(meaudret$design$site)), plot = FALSE) g3 <- s.corcircle (wit1$as, plot = FALSE) G <- ADEgS(list(g1, g2, g3), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) s.traject(pca1$li, meaudret$design$site, sub = "Principal Component Analysis", csub = 1.5) s.traject(wit1$li, meaudret$design$site, sub = "Within site Principal Component Analysis", csub = 1.5) s.corcircle (wit1$as) par(mfrow = c(1, 1)) } plot(wit1)
data(meaudret) pca1 <- dudi.pca(meaudret$env, scan = FALSE, nf = 4) wit1 <- wca(pca1, meaudret$design$site, scan = FALSE, nf = 2) if(adegraphicsLoaded()) { g1 <- s.traject(pca1$li, meaudret$design$site, psub.text = "Principal Component Analysis", plines.lty = 1:length(levels(meaudret$design$site)), plot = FALSE) g2 <- s.traject(wit1$li, meaudret$design$site, psub.text = "Within site Principal Component Analysis", plines.lty = 1:length(levels(meaudret$design$site)), plot = FALSE) g3 <- s.corcircle (wit1$as, plot = FALSE) G <- ADEgS(list(g1, g2, g3), layout = c(2, 2)) } else { par(mfrow = c(2, 2)) s.traject(pca1$li, meaudret$design$site, sub = "Principal Component Analysis", csub = 1.5) s.traject(wit1$li, meaudret$design$site, sub = "Within site Principal Component Analysis", csub = 1.5) s.corcircle (wit1$as) par(mfrow = c(1, 1)) } plot(wit1)
Performs a normed within Principal Component Analysis.
withinpca(df, fac, scaling = c("partial", "total"), scannf = TRUE, nf = 2)
withinpca(df, fac, scaling = c("partial", "total"), scannf = TRUE, nf = 2)
df |
a data frame with quantitative variables |
fac |
a factor partitioning the rows of df in classes |
scaling |
a string of characters as a scaling option : |
scannf |
a logical value indicating whether the eigenvalues bar plot should be displayed |
nf |
if scannf FALSE, an integer indicating the number of kept axes |
This functions implements the 'Bouroche' standardization. In a first
step, the original variables are standardized (centred and normed). Then, a second
transformation is applied according to the value of the scaling
argument. For "partial", variables are standardized in each sub-table
(corresponding to each level of the factor). Hence, variables have null
mean and unit variance in each sub-table. For "total", variables are
centred in each sub-table and then normed globally. Hence, variables
have a null mean in each sub-table and a global variance equal to one.
returns a list of the sub-class within
of class dudi
. See wca
Daniel Chessel
Anne-Béatrice Dufour [email protected]
Bouroche, J. M. (1975) Analyse des données ternaires: la double analyse en composantes principales. Thèse de 3ème cycle, Université de Paris VI.
data(meaudret) wit1 <- withinpca(meaudret$env, meaudret$design$season, scannf = FALSE, scaling = "partial") kta1 <- ktab.within(wit1, colnames = rep(c("S1", "S2", "S3", "S4", "S5"), 4)) unclass(kta1) # See pta plot(wit1)
data(meaudret) wit1 <- withinpca(meaudret$env, meaudret$design$season, scannf = FALSE, scaling = "partial") kta1 <- ktab.within(wit1, colnames = rep(c("S1", "S2", "S3", "S4", "S5"), 4)) unclass(kta1) # See pta plot(wit1)
witwit.coa
performs an Internal Correspondence Analysis.
witwitsepan
gives the computation and the barplot of the eigenvalues
for each separated analysis in an Internal Correspondence Analysis.
witwit.coa(dudi, row.blocks, col.blocks, scannf = TRUE, nf = 2) ## S3 method for class 'witwit' summary(object, ...) witwitsepan(ww, mfrow = NULL, csub = 2, plot = TRUE)
witwit.coa(dudi, row.blocks, col.blocks, scannf = TRUE, nf = 2) ## S3 method for class 'witwit' summary(object, ...) witwitsepan(ww, mfrow = NULL, csub = 2, plot = TRUE)
dudi |
an object of class |
row.blocks |
a numeric vector indicating the row numbers for each block of rows |
col.blocks |
a numeric vector indicating the column numbers for each block of columns |
scannf |
a logical value indicating whether the eigenvalues bar plot should be displayed |
nf |
if scannf FALSE, an integer indicating the number of kept axes |
object |
an object of class |
... |
further arguments passed to or from other methods |
ww |
an object of class |
mfrow |
a vector of the form "c(nr,nc)", otherwise computed by a special own function 'n2mfrow' |
csub |
a character size for the sub-titles, used with |
plot |
if FALSE, numeric results are returned |
returns a list of class witwit
, coa
and dudi
(see as.dudi) containing
rbvar |
a data frame with the within variances of the rows of the factorial coordinates |
lbw |
a data frame with the marginal weighting of the row classes |
cvar |
a data frame with the within variances of the columns of the factorial coordinates |
cbw |
a data frame with the marginal weighting of the column classes |
Daniel Chessel Anne-Béatrice Dufour [email protected] Correction by Campo Elías PARDO [email protected]
Cazes, P., Chessel, D. and Dolédec, S. (1988) L'analyse des correspondances internes d'un tableau partitionné : son usage en hydrobiologie. Revue de Statistique Appliquée, 36, 39–54.
data(ardeche) coa1 <- dudi.coa(ardeche$tab, scann = FALSE, nf = 4) ww <- witwit.coa(coa1, ardeche$row.blocks, ardeche$col.blocks, scann = FALSE) ww summary(ww) if(adegraphicsLoaded()) { g1 <- s.class(ww$co, ardeche$sta.fac, plab.cex = 1.5, ellipseSi = 0, paxes.draw = FALSE, plot = FALSE) g2 <- s.label(ww$co, plab.cex = 0.75, plot = FALSE) G <- superpose(g1, g2, plot = TRUE) } else { s.class(ww$co, ardeche$sta.fac, clab = 1.5, cell = 0, axesell = FALSE) s.label(ww$co, add.p = TRUE, clab = 0.75) } witwitsepan(ww, c(4, 6))
data(ardeche) coa1 <- dudi.coa(ardeche$tab, scann = FALSE, nf = 4) ww <- witwit.coa(coa1, ardeche$row.blocks, ardeche$col.blocks, scann = FALSE) ww summary(ww) if(adegraphicsLoaded()) { g1 <- s.class(ww$co, ardeche$sta.fac, plab.cex = 1.5, ellipseSi = 0, paxes.draw = FALSE, plot = FALSE) g2 <- s.label(ww$co, plab.cex = 0.75, plot = FALSE) G <- superpose(g1, g2, plot = TRUE) } else { s.class(ww$co, ardeche$sta.fac, clab = 1.5, cell = 0, axesell = FALSE) s.label(ww$co, add.p = TRUE, clab = 0.75) } witwitsepan(ww, c(4, 6))
This data set gives the presence of plant species in relevés of woodlands in the conurbation of Angers; and their biological traits.
data(woangers)
data(woangers)
woangers
is a list of 2 components.
flo: is a data frame that contains the presence/absence of species in each sample site. In the codes for the sample sites (first column of the data frame), the first three letters provide the code of the woodland and the numbers represent the 5 quadrats sampled in each site. Codes for the woodlands are based on either their local name when they have one or on the name of the nearest locality.
traits: is a data frame that contains the values of the 13 functional traits considered in the paper. One trait can be encoded by several columns. The codes are as follows:
Column 1: Species names;
Column 2: li
, nominal variable that indicates the presence (y) or absence (n) of
ligneous structures;
Column 3: pr
, nominal variable that indicates the presence (y) or absence (n) of
prickly structures;
Column 4: fo
, circular variable that indicates the month when the flowering period
starts (from 1 January to 9 September);
Column 5: he
, ordinal variable that indicates the maximum height of the leaf
canopy;
Column 6: ae
, ordinal variable that indicates the degree of aerial vegetative
multiplication;
Column 7: un
, ordinal variable that indicates the degree of underground vegetative
multiplication;
Column 8: lp
, nominal variable that represents the leaf position by 3 levels (ros
=
rosette, semiros
= semi-rosette and leafy
= leafy stem);
Column 9: le
, nominal variable that represents the mode of leaf persistence by 5
levels (seasaes
= seasonal aestival, seashib
= seasonal hibernal, seasver
=
seasonal vernal, everalw
= always evergreen, everparti
= partially evergreen);
Columns 10, 11 and 12: fuzzy variable that describes the modes of pollination with 3
levels (auto
= autopollination, insects
= pollination by insects, wind
=
pollination by wind); this fuzzy variable is expressed as proportions, i.e. for each
row, the sum of the three columns equals 1;
Columns 13, 14 and 15: fuzzy variable that describes the life cycle with 3 levels (annual, monocarpic and polycarpic); this fuzzy variable is expressed as proportions, i.e. for each row, the sum of the three column equals 1;
Columns 16 to 20: fuzzy variable that describes the modes of dispersion with 5 levels
(elaio
= dispersion by ants, endozoo
= injection by animals, epizoo
=
external transport by animals, wind
= transport by wind, unsp
= unspecialized
transport); this fuzzy variable is expressed as proportions, i.e. for each row, the
sum of the three columns equals 1;
Column 21: lo
, quantitative variable that provides the seed bank longevity index;
Column 22: lf
, quantitative variable that provides the length of the flowering
period.
Pavoine, S., Vallet, J., Dufour, A.-B., Gachet, S. and Daniel, H. (2009) On the challenge of treating various types of variables: Application for improving the measurement of functional diversity. Oikos, 118, 391–402.
# Loading the data data(woangers) # Preparating of the traits traits <- woangers$traits # Nominal variables 'li', 'pr', 'lp' and 'le' # (see table 1 in the main text for the codes of the variables) tabN <- traits[, c(1:2, 7, 8)] # Circular variable 'fo' tabC <- traits[3] tabCp <- prep.circular(tabC, 1, 12) # The levels of the variable lie between 1 (January) and 12 (December). # Ordinal variables 'he', 'ae' and 'un' tabO <- traits[, 4:6] # Fuzzy variables 'mp', 'pe' and 'di' tabF <- traits[, 9:19] tabFp <- prep.fuzzy(tabF, c(3, 3, 5), labels = c("mp", "pe", "di")) # 'mp' has 3 levels, 'pe' has 3 levels and 'di' has 5 levels. # Quantitative variables 'lo' and 'lf' tabQ <- traits[, 20:21] # Combining the traits ktab1 <- ktab.list.df(list(tabN, tabCp, tabO, tabFp, tabQ)) ## Not run: # Calculating the distances for all traits combined distrait <- dist.ktab(ktab1, c("N", "C", "O", "F", "Q")) is.euclid(distrait) # Calculating the contribution of each trait in the combined distances contrib <- kdist.cor(ktab1, type = c("N", "C", "O", "F", "Q")) contrib dotchart(sort(contrib$glocor), labels = rownames(contrib$glocor)[order(contrib$glocor[, 1])]) ## End(Not run)
# Loading the data data(woangers) # Preparating of the traits traits <- woangers$traits # Nominal variables 'li', 'pr', 'lp' and 'le' # (see table 1 in the main text for the codes of the variables) tabN <- traits[, c(1:2, 7, 8)] # Circular variable 'fo' tabC <- traits[3] tabCp <- prep.circular(tabC, 1, 12) # The levels of the variable lie between 1 (January) and 12 (December). # Ordinal variables 'he', 'ae' and 'un' tabO <- traits[, 4:6] # Fuzzy variables 'mp', 'pe' and 'di' tabF <- traits[, 9:19] tabFp <- prep.fuzzy(tabF, c(3, 3, 5), labels = c("mp", "pe", "di")) # 'mp' has 3 levels, 'pe' has 3 levels and 'di' has 5 levels. # Quantitative variables 'lo' and 'lf' tabQ <- traits[, 20:21] # Combining the traits ktab1 <- ktab.list.df(list(tabN, tabCp, tabO, tabFp, tabQ)) ## Not run: # Calculating the distances for all traits combined distrait <- dist.ktab(ktab1, c("N", "C", "O", "F", "Q")) is.euclid(distrait) # Calculating the contribution of each trait in the combined distances contrib <- kdist.cor(ktab1, type = c("N", "C", "O", "F", "Q")) contrib dotchart(sort(contrib$glocor), labels = rownames(contrib$glocor)[order(contrib$glocor[, 1])]) ## End(Not run)
The worksurv
data frame gives 319 response items and 4 questions
providing from a French Worker Survey.
data(worksurv)
data(worksurv)
This data frame contains the following columns:
pro: Professional elections. In professional elections in your firm, would you rather vote for a list supported by?
CGT
CFDT
FO
CFTC
Auton
Autonomous
Abst
Nonaffi
Not affiliated
NR
No response
una: Union affiliation. At the present time, are you affiliated to a Union, and in the affirmative, which one?
CGT
CFDT
FO
CFTC
Auton
Autonomous
CGC
Notaffi
Not affiliated
NR
No response
pre: Presidential election. On the last presidential election (1969), can you tell me the candidate for whom you havevoted?
Duclos
Deferre
Krivine
Rocard
Poher
Ducatel
Pompidou
NRAbs
No response, abstention
pol: political sympathy. Which political party do you feel closest to, as a rule?
Communist
(PCF)
Socialist
(SFIO+PSU+FGDS)
Left
(Party of workers,...)
Center
MRP+RAD.
RI
Right
INDEP.+CNI
Gaullist
UNR
NR
No response
The data frame worksurv
has the attribute 'counts' giving the number of responses for each item.
Rouanet, H. and Le Roux, B. (1993) Analyse des données multidimensionnelles. Dunod, Paris.
Le Roux, B. and Rouanet, H. (1997) Interpreting axes in multiple correspondence analysis: method of the contributions of points and deviation. Pages 197-220 in B. J. and M. Greenacre, editors. Visualization of categorical data, Acamedic Press, London.
data(worksurv) acm1 <- dudi.acm(worksurv, row.w = attr(worksurv, "counts"), scan = FALSE) if(adegraphicsLoaded()) { s.class(acm1$li, worksurv) } else { par(mfrow = c(2, 2)) apply(worksurv, 2, function(x) s.class(acm1$li, factor(x), attr(worksurv, 'counts'))) par(mfrow = c(1, 1)) }
data(worksurv) acm1 <- dudi.acm(worksurv, row.w = attr(worksurv, "counts"), scan = FALSE) if(adegraphicsLoaded()) { s.class(acm1$li, worksurv) } else { par(mfrow = c(2, 2)) apply(worksurv, 2, function(x) s.class(acm1$li, factor(x), attr(worksurv, 'counts'))) par(mfrow = c(1, 1)) }
This data set gives 3 matrices about geographical, genetic and anthropometric distances.
data(yanomama)
data(yanomama)
yanomama
is a list of 3 components:
is a matrix of 19-19 geographical distances
is a matrix of 19-19 SFA (genetic) distances
is a matrix of 19-19 anthropometric distances
Spielman, R.S. (1973) Differences among Yanomama Indian villages: do the patterns of allele frequencies, anthropometrics and map locations correspond? American Journal of Physical Anthropology, 39, 461–480.
Table 7.2 Distance matrices for 19 villages of Yanomama Indians. All distances are as given by Spielman (1973), multiplied by 100 for convenience in: Manly, B.F.J. (1991) Randomization and Monte Carlo methods in biology Chapman and Hall, London, 1–281.
data(yanomama) gen <- quasieuclid(as.dist(yanomama$gen)) # depends of mva ant <- quasieuclid(as.dist(yanomama$ant)) # depends of mva par(mfrow = c(2,2)) plot(gen, ant) t1 <- mantel.randtest(gen, ant, 99); plot(t1, main = "gen-ant-mantel") ; print(t1) t1 <- procuste.rtest(pcoscaled(gen), pcoscaled(ant), 99) plot(t1, main = "gen-ant-procuste") ; print(t1) t1 <- RV.rtest(pcoscaled(gen), pcoscaled(ant), 99) plot(t1, main = "gen-ant-RV") ; print(t1)
data(yanomama) gen <- quasieuclid(as.dist(yanomama$gen)) # depends of mva ant <- quasieuclid(as.dist(yanomama$ant)) # depends of mva par(mfrow = c(2,2)) plot(gen, ant) t1 <- mantel.randtest(gen, ant, 99); plot(t1, main = "gen-ant-mantel") ; print(t1) t1 <- procuste.rtest(pcoscaled(gen), pcoscaled(ant), 99) plot(t1, main = "gen-ant-procuste") ; print(t1) t1 <- RV.rtest(pcoscaled(gen), pcoscaled(ant), 99) plot(t1, main = "gen-ant-RV") ; print(t1)
This data set gives the road distances between 13 towns in New-Zealand.
data(zealand)
data(zealand)
zealand
is a list with the following components:
a data frame with 13 rows (New Zealand towns) and 13 columns (New Zealand towns) containing the road distances between these towns
a data frame containing the coordinates of the 13 towns
a neighborhood object (class nb
defined in package
spdep
)
Manly, B.F. (1994). Multivariate Statistical Methods. A primer., Second edition, Chapman and Hall, London, 1–215, page 172.
data(zealand) d0 <- as.dist(as.matrix(zealand$road)) d1 <- cailliez (d0) d2 <- lingoes(d0) if(adegraphicsLoaded()) { G1 <- s.label(zealand$xy, lab = as.character(1:13), nb = zealand$nb) g1 <- s.label(cmdscale(dist(zealand$xy)), lab = as.character(1:13), nb = zealand$nb, psub.text = "Distance canonique", plot = FALSE) g2 <- s.label(cmdscale(d0), lab = as.character(1:13), nb = zealand$nb, psub.text = "Distance routiere", plot = FALSE) g3 <- s.label(cmdscale(d1), lab = as.character(1:13), nb = zealand$nb, psub.text = "Distance routiere / Cailliez", plot = FALSE) g4 <- s.label(cmdscale(d2), lab = as.character(1:13), nb = zealand$nb, psub.text = "Distance routiere / Lingoes", plot = FALSE) G2 <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) }
data(zealand) d0 <- as.dist(as.matrix(zealand$road)) d1 <- cailliez (d0) d2 <- lingoes(d0) if(adegraphicsLoaded()) { G1 <- s.label(zealand$xy, lab = as.character(1:13), nb = zealand$nb) g1 <- s.label(cmdscale(dist(zealand$xy)), lab = as.character(1:13), nb = zealand$nb, psub.text = "Distance canonique", plot = FALSE) g2 <- s.label(cmdscale(d0), lab = as.character(1:13), nb = zealand$nb, psub.text = "Distance routiere", plot = FALSE) g3 <- s.label(cmdscale(d1), lab = as.character(1:13), nb = zealand$nb, psub.text = "Distance routiere / Cailliez", plot = FALSE) g4 <- s.label(cmdscale(d2), lab = as.character(1:13), nb = zealand$nb, psub.text = "Distance routiere / Lingoes", plot = FALSE) G2 <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) }