Package 'ade4'

Description

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").

Author(s)

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.

References

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/

See Also

ade4TkGUI, adegenet, adehabitat, adegraphics

Description

This data set gathers three phylogenies with three sets of traits as reported by Abouheif (1999).

Usage

data(abouheif.eg)

Format

abouheif.eg is a list containing the 6 following objects :

tre1

is a character string giving the first phylogenetic tree made up of 8 leaves.

vec1

is a numeric vector with 8 values.

tre2

is a character string giving the second phylogenetic tree made up of 7 leaves.

vec2

is a numeric vector with 7 values.

tre3

is a character string giving the third phylogenetic tree made up of 15 leaves.

vec3

is a numeric vector with 15 values.

Source

Data taken from the phylogenetic independence program developed by Ehab Abouheif

References

Abouheif, E. (1999) A method for testing the assumption of phylogenetic independence in comparative data. Evolutionary Ecology Research, 1, 895–909.

Examples

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)

Description

Counts of individuals of Acacia ehrenbergiana from five parallel transects of 32 quadrats.

Usage

data(acacia)

Format

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 $m^{2}$ 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 $m^{2}$ in canopy) of transects 1 to 5 respectively.

Source

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.

References

Hill, M.O. (1973) The intensity of spatial pattern in plant communities. Journal of Ecology, 61, 225–235.

Examples

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))
}

Description

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).

Usage

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)

Arguments

Details

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).

Value

The matched call (invisible).

Author(s)

Thibaut Jombart [email protected]

See Also

scatter

Examples

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)
  }

Description

aminoacyl is a list containing the codon counts of 36 genes encoding yeast aminoacyl-tRNA-synthetase(S.Cerevisiae).

Usage

data(aminoacyl)

Format

aminoacyl is a list containing the 5 following objects:

genes

is a vector giving the gene names.

localisation

is a vector giving the cellular localisation of the proteins (M = mitochondrial, C = cytoplasmic, I = indetermined, CI = cyto and mito).

codon

is a vector containing the 64 triplets.

AA

is a factor giving the amino acid names for each codon.

usage.codon

is a dataframe containing the codon counts for each gene.

Source

Data prepared by D. Charif [email protected]

References

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.

Examples

data(aminoacyl)
aminoacyl$genes
aminoacyl$usage.codon
dudi.coa(aminoacyl$usage.codon, scannf = FALSE)

Description

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.

Usage

amova(samples, distances, structures)
## S3 method for class 'amova'
print(x, full = FALSE, ...)

Arguments

Value

Returns a list of class amova

Author(s)

Sandrine Pavoine [email protected]

References

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.

See Also

randtest.amova

Examples

data(humDNAm)
amovahum <- amova(humDNAm$samples, sqrt(humDNAm$distances), humDNAm$structures)
amovahum

Description

This data set gives the occurences for the allelic form on 8 loci in 10 populations of honeybees.

Usage

data(apis108)

Format

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).

Source

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.

Examples

data(apis108)
str(apis108)
names(apis108)

Description

The hierarchical apportionment of quadratic entropy defined by Rao (1982).

Usage

apqe(samples, dis = NULL, structures)
## S3 method for class 'apqe'
print(x, full = FALSE, ...)

Arguments

Value

Returns a list of class apqe

Author(s)

Sandrine Pavoine [email protected]

References

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.

Examples

data(ecomor)
ecomor.phylog <- taxo2phylog(ecomor$taxo)
apqe(ecomor$habitat, ecomor.phylog$Wdist)

Description

This dataset describe the distribution of 82 species of Alpine plants in 75 sites. Species traits and environmental variables are also measured.

Usage

data(aravo)

Format

aravo is a list containing the following objects :

spe

is a data.frame with the abundance values of 82 species (columns) in 75 sites (rows).

env

is a data.frame with the measurements of 6 environmental variables for the sites.

traits

is data.frame with the measurements of 8 traits for the species.

spe.names

is a vector with full species names.

Details

The environmental variables are:

The species traits for the plants are:

Source

Choler, P. (2005) Consistent shifts in Alpine plant traits along a mesotopographical gradient. Arctic, Antarctic, and Alpine Research, 37,444–453.

Examples

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)

Description

This data set gives information about species of benthic macroinvertebrates in different sites and dates.

Usage

data(ardeche)

Format

ardeche is a list with 6 components.

tab

is a data frame containing fauna table with 43 species (rows) and 35 samples (columns).

col.blocks

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.

row.blocks

is a vector containing the repartition of species in the 4 groups defining the species order.

dat.fac

is a date factor for samples (6 dates).

sta.fac

is a site factor for samples (6 sites).

esp.fac

is a species order factor (Ephemeroptera, Plecoptera, Coleoptera, Trichoptera).

Details

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).

Source

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.

Examples

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)
}

Description

'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.

Usage

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)

Arguments

Value

poly2area returns a data frame 'factor,x,y'.
area2poly returns a list of class polylist.

Author(s)

Daniel Chessel

Examples

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)

Description

This data set gives arrival times of 254 patients at an intensive care unit during one day.

Usage

data(arrival)

Format

arrival is a list containing the 2 following objects :

times

is a vector giving the arrival times in the form HH:MM

hours

is a vector giving the number of arrivals per hour for the day considered

Source

Data taken from the Oriana software developed by Warren L. Kovach [email protected] starting from https://www.kovcomp.co.uk/oriana/index.html.

References

Fisher, N. I. (1993) Statistical Analysis of Circular Data. Cambridge University Press.

Examples

data(arrival)
dotcircle(arrival$hours, pi/2 + pi/12)

Description

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.

Usage

as.taxo(df)
dist.taxo(taxo)

Arguments

Value

as.taxo returns a data frame of class taxo. dist.taxo returns a numeric of class dist.

Author(s)

Daniel Chessel
Sébastien Ollier [email protected]

See Also

taxo2phylog to transform an object of class taxo into an object of class phylog

Examples

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)

Description

atlas is a list containing three kinds of information about 23 regions (The French Alps) :
geographical coordinates, meteorology and bird presences.

Usage

data(atlas)

Format

atlas is a list of 9 components:

area

is a convex hull of 23 geographical regions.

xy

are the coordinates of the region centers and altitude (in meters).

names.district

is a vector of region names.

meteo

is a data frame with 7 variables: min and max temperature in january; min and max temperature in july; january, july and total rainfalls.

birds

is a data frame with 15 variables (species).

contour

is a data frame with 4 variables (x1, y1, x2, y2) for the contour display of The French Alps.

alti

is a data frame with 3 variables altitude in percentage [0,800], ]800,1500] and ]1500,5000].

Spatial

is the map of the 23 regions of The French Alps (an object of the class SpatialPolygons of sp).

Spatial.contour

is the contour of the map of the 23 regions of the French Alps (an object of the class SpatialPolygons of sp).

Source

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.

Examples

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))}

Description

This data set contains information about genetic variability of Atya innocous and Atya scabra in Guadeloupe (France).

Usage

data(atya)

Format

atya is a list with the following components:

xy

a data frame with the coordinates of the 31 sites

gen

a data frame with 22 variables collected on 31 sites

nb

a neighborhood object (class nb defined in package spdep)

Source

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.

Examples

## 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)

Description

This data set contains information about spatial distribution of bird species in a zone surrounding the river Rhône near Lyon (France).

Usage

data(avijons)

Format

avijons is a list with the following components:

xy

a data frame with the coordinates of the sites

area

an object of class area

fau

a data frame with the abundance of 64 bird species in 91 sites

spe.names.fr

a vector of strings of character with the species names in french

Spatial

an object of the class SpatialPolygons of sp, containing the map

Source

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.

References

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).

Examples

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)

Description

avimedi is a list containing the information about 302 sites :
frequencies of 51 bird species ; two factors (habitats and Mediterranean origin).

Usage

data(avimedi)

Format

This list contains the following objects:

fau

is a data frame 302 sites - 51 bird species.

plan

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).

nomesp

is a vector 51 latin names.

Source

Blondel, J., Chessel, D., & Frochot, B. (1988) Bird species impoverishment, niche expansion, and density inflation in mediterranean island habitats. Ecology, 69, 1899–1917.

Examples

## 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)

Description

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.

Usage

data(aviurba)

Format

This list contains the following objects:

fau

is a data frame 51 sites 40 bird species.

mil

is a data frame 51 sites 11 environmental variables (see details).

traits

is a data frame 40 species 4 biological traits (see details).

species.names.fr

is a vector of the species names in french.

species.names.la

is a vector of the species names in latin.

species.family

is a factor : the species families.

Details

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).

Source

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.

Examples

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))

Description

bacteria is a list containing 43 species and genomic informations : codons, amino acid and bases.

Usage

data(bacteria)

Format

This list contains the following objects:

code

is a factor with the amino acid names for each codon.

espcodon

is a data frame 43 species 64 codons.

espaa

is a data frame 43 species 21 amino acid.

espbase

is a data frame 43 species 4 bases.

Source

Data prepared by J. Lobry [email protected] starting from https://www.jcvi.org/.

Examples

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")
}

Description

banque gives the results of a bank survey onto 810 customers.

Usage

data(banque)

Format

This data frame contains the following columns:

1. 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

2. 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

3. oppo: "Stopped a check?" a factor with levels

   • non no

   • oui yes

4. 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[

5. sexe: "Customer's gender" a factor with levels

   • hom Male

   • fem Female

6. interdit: "No checkbook allowed" a factor with levels

   • non no

   • oui yes

7. cableue: "Possess a bank card?" a factor with levels

   • non no

   • oui yes

8. assurvi: "Contrat of life insurance?" a factor with levels

   • non no
     

   • oui yes

9. 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

10. eparlog: "Savings and loan association account amount" a factor with levels

    • for > 20000

    • fai >0 and <20000

    • nul nulle

11. eparliv: "Savings bank amount" a factor with levels

    • for > 20000

    • fai >0 and <20000

    • nul nulle

12. credhab: "Home loan owner" a factor with levels

    • non no

    • oui yes

13. credcon: "Consumer credit amount" a factor with levels

    • nul none

    • fai >0 and <20000

    • for > 20000

14. versesp: "Check deposits" a factor with levels

    • oui yes

    • non no

15. retresp: "Cash withdrawals" a factor with levels

    • fai < 2000

    • moy 2000-5000

    • for > 5000

16. remiche: "Endorsed checks amount" a factor with levels

    • for >10000

    • moy 10000-5000

    • fai 1-5000

    • nul none

17. preltre: "Treasury Department tax deductions" a factor with levels

    • nul none

    • fai <1000

    • moy >1000

18. prelfin: "Financial institution deductions" a factor with levels

    • nul none

    • fai <1000

    • moy >1000

19. viredeb: "Debit transfer amount" a factor with levels

    • nul none

    • fai <2500

    • moy 2500-5000

    • for >5000

20. virecre: "Credit transfer amount" a factor with levels

    • for >10000

    • moy 10000-5000

    • fai <5000

    • nul aucun

21. porttit: "Securities portfolio estimations" a factor with levels

    • nul none

    • fai < 20000

    • moy 20000-100000

    • for >100000

Source

anonymous

Examples

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)
}

Description

This data set is a list containing relations between sites and fish species linked to dates.

Usage

data(baran95)

Format

This list contains the following objects:

fau

is a data frame 95 seinings and 33 fish species.

plan

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).

species.names

is a vector of species latin names.

Source

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.

References

See a data description at http://pbil.univ-lyon1.fr/R/pdf/pps027.pdf (in French).

Examples

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))
}

Description

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.

Usage

## S3 method for class 'dudi'
bca(x, fac, scannf = TRUE, nf = 2, ...)

Arguments

Value

Returns a list of class dudi, subclass 'between' containing

Note

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.

Author(s)

Daniel Chessel
Anne-Béatrice Dufour [email protected]

References

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.

Examples

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)

Description

Performs a between-class analysis after a coinertia analysis

Usage

## S3 method for class 'coinertia'
bca(x, fac, scannf = TRUE, nf = 2, ...)

Arguments

Details

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.

Value

An object of the class betcoi. Outputs are described by the print function

Note

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.

Author(s)

Stéphane Dray [email protected] and Jean Thioulouse [email protected]

References

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.

See Also

coinertia, bca

Examples

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)

Description

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.

Usage

## 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, ...)

Arguments

Value

The bca.rlq function returns an object of class 'betrlq' (sub-class of 'dudi'). See the outputs of the print function for more details.

Author(s)

Stéphane Dray [email protected]

References

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.

See Also

rlq, bca, wca.rlq

Examples

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)

Description

Outputs and graphical representations of the results of a between-class analysis.

Usage

## 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, ...)

Arguments

Author(s)

Daniel Chessel
Anne-Béatrice Dufour [email protected]
Stéphane Dray [email protected]

References

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.

See Also

bca.dudi, bca.coinertia

Examples

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)

Description

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.

Usage

data(bf88)

Format

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.

Source

Blondel, J. and Farre, H. (1988) The convergent trajectories of bird communities along ecological successions in european forests. Oecologia (Berlin), 75, 83–93.

Examples

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)
}

Description

This function creates a doubly centred matrix.

Usage

bicenter.wt(X, row.wt = rep(1, nrow(X)), col.wt = rep(1, ncol(X)))

Arguments

Value

returns a doubly centred matrix

Author(s)

Daniel Chessel

Examples

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)

Description

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).

Usage

data(bordeaux)

Format

This data frame has 5 rows (the wines) and 4 columns (the judgements) divided in excellent, good, mediocre and boring.

Source

van Rijckevorsel, J. (1987) The application of fuzzy coding and horseshoes in multiple correspondence analysis. DSWO Press, Leiden (p. 32)

Examples

data(bordeaux)
bordeaux
score(dudi.coa(bordeaux, scan = FALSE))

Description

This data set gives ecological and biological characteristics of 131 species of aquatic insects.

Usage

data(bsetal97)

Format

bsetal97 is a list of 8 components.

species.names

is a vector of the names of aquatic insects.

taxo

is a data frame containing the taxonomy of species: genus, family and order.

biol

is a data frame containing 10 biological traits for a total of 41 modalities.

biol.blo

is a vector of the numbers of items for each biological trait.

biol.blo.names

is a vector of the names of the biological traits.

ecol

is a data frame with 7 ecological traits for a total of 34 modalities.

ecol.blo

is a vector of the numbers of items for each ecological trait.

ecol.blo.names

is a vector of the names of the ecological traits.

Details

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: $\leq 1$, 2, > 2), oviposition period - the length of time during which oviposition occurred (3 modalities: $\leq 2$ months, between 2 and 5 months, > 5 months), incubation time - the time between oviposition and hatching of the larvae (3 modalities: $\leq 4$ 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).

Source

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.

References

See a data description at http://pbil.univ-lyon1.fr/R/pdf/pps029.pdf (in French).

Examples

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))

Description

This data set contains informations about Buech basin characteristics.

Usage

data(buech)

Format

buech is a list with the following components:

tab1

a data frame with 10 environmental variables collected on 31 sites in Juin (1984)

tab2

a data frame with 10 environmental variables collected on 31 sites in September (1984)

xy

a data frame with the coordinates of the sites

contour

a data frame for background map

nb

the neighbouring graph between sites, object of the class nb

Spatial

an object of the class SpatialPolygons of sp, containing the map

Details

Variables of buech$tab1 and buech$tab2 are the following ones:
pH ; Conductivity ($\mu$ 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-))

Source

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.

Examples

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)
  }
}

Description

This data set contains environmental and genetics informations about 16 Euphydryas editha butterfly colonies studied in California and Oregon.

Usage

data(butterfly)

Format

butterfly is a list with the following components:

xy

a data frame with the two coordinates of the 16 Euphydryas editha butterfly colonies

envir

a environmental data frame of 16 sites - 4 variables

genet

a genetics data frame of 16 sites - 6 allele frequencies

contour

a data frame for background map (California map)

Spatial

an object of the class SpatialPolygons of sp, containing the map

Source

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.

References

Manly, B.F. (1994) Multivariate Statistical Methods. A primer. Second edition. Chapman & Hall, London. 1–215.

Examples

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))
}

Description

These functions allow to study the variations in diversity among communities (as in dpcoa) taking into account a partition in classes

Usage

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, ...)

Arguments

Value

Objects of class betdpcoa, witdpcoa or betwit

Author(s)

Stéphane Dray [email protected]

References

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

See Also

dpcoa

Examples

## 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)

Description

This function computes the smallest positive constant that makes Euclidean a distance matrix and applies it.

Usage

cailliez(distmat, print = FALSE, tol = 1e-07, cor.zero = TRUE)

Arguments

Value

an object of class dist containing a Euclidean distance matrix.

Author(s)

Daniel Chessel
Stéphane Dray [email protected]

References

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.

Examples

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

Description

This data set gives the road distances between 15 European capitals and their coordinates.

Usage

data(capitales)

Format

capitales is a list with the following components:

xy

a data frame containing the coordinates of capitals

area

a data frame containing three variables, designed to be used in area.plot function

logo

a list of pixmap objects, each one symbolizing a capital

Spatial

an object of the class SpatialPolygons of sp, containing the map

dist

a dist object the road distances between 15 European capitals

Examples

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))
  }
  }

Description

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.

Usage

data(carni19)

Format

carni19 is a list containing the 2 following objects :

tre

is a character string giving the phylogenetic tree in Newick format.

bm

is a numeric vector which values correspond to the body mass of the 19 species (log scale).

Source

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.

Examples

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))

Description

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.

Usage

data(carni70)

Format

carni70 is a list containing the 2 following objects:

tre

is a character string giving the phylogenetic tree in Newick format. Branch lengths are expressed as divergence times (millions of years)

tab

is a data frame with 70 species and two traits: size (body size (kg)) ; range (geographic range size (km)).

Source

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.

Examples

## 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)