Version:	1.7-23
Title:	Analysis of Ecological Data: Exploratory and Euclidean Methods in Environmental Sciences
Depends:	R (≥ 3.5.0)
Imports:	graphics, grDevices, methods, stats, utils, MASS, pixmap, sp, Rcpp
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	ade4TkGUI, adegraphics, adephylo, adespatial, ape, CircStats, deldir, lattice, spdep, splancs, waveslim, progress, foreach, parallel, doParallel, iterators, knitr, rmarkdown
VignetteBuilder:	knitr
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>.
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://adeverse.github.io/ade4/, http://pbil.univ-lyon1.fr/ADE-4/
BugReports:	https://github.com/adeverse/ade4/issues
Encoding:	UTF-8
NeedsCompilation:	yes
Packaged:	2025-02-14 13:27:05 UTC; siberchicot
Author:	Stéphane Dray [aut], Anne-Béatrice Dufour [aut], Jean Thioulouse [aut], Daniel Chessel [ant], Thibaut Jombart [ctb], Sandrine Pavoine [ctb], Jean R. Lobry [ctb], Sébastien Ollier [ctb], Daniel Borcard [ctb], Pierre Legendre [ctb], Stéphanie Bougeard [ctb], Aurélie Siberchicot [ctb, cre]
Maintainer:	Aurélie Siberchicot <aurelie.siberchicot@univ-lyon1.fr>
Repository:	CRAN
Date/Publication:	2025-02-14 14:20:02 UTC

The ade4 package

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

Deprecated functions in ade4

Description

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

Import data files from Phylogenetic Independance Package

Description

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.

Usage

PI2newick(x)

Arguments

Value

Returns a list containing :

Author(s)

Sébastien Ollier sebastien.ollier@u-psud.fr
Daniel Chessel

References

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

Examples

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)

Monte-Carlo Test on the sum of eigenvalues of a co-inertia analysis (in C++ with Rcpp).

Description

performs a Monte-Carlo Test on the sum of eigenvalues of a co-inertia analysis.

Usage

RV.randtest(df1, df2, nrepet = 999, ...)

Arguments

Value

returns a list of class 'randtest'

Author(s)

Daniel Chessel and Jean Thioulouse

References

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.

Examples

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)

Monte-Carlo Test on the sum of eigenvalues of a co-inertia analysis (in R).

Description

performs a Monte-Carlo Test on the sum of eigenvalues of a co-inertia analysis.

Usage

RV.rtest(df1, df2, nrepet = 99, ...)

Arguments

Value

returns a list of class 'rtest'

Author(s)

Daniel Chessel

References

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.

Examples

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)

Tests of randomization on the correlation between two distance matrices (in R).

Description

performs a RV Test between two distance matrices.

Usage

RVdist.randtest(m1, m2, nrepet = 999, ...)

Arguments

Value

returns a list of class 'randtest'

Author(s)

Daniel Chessel

References

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.

Monte-Carlo Test on the sum of eigenvalues of a within-class co-inertia analysis (in C++ with Rcpp).

Description

performs a Monte-Carlo Test on the sum of eigenvalues of a within-class co-inertia analysis.

Usage

RVintra.randtest(df1, df2, fac, nrepet = 999, ...)

Arguments

Value

returns a list of class 'randtest'

Author(s)

Daniel Chessel and Jean Thioulouse

References

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.

Examples

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)

Phylogenies and quantitative traits from Abouheif

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)

Spatial pattern analysis in plant communities

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))
}

Add graphics to an existing plot

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 t.jombart@imperial.ac.uk

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)
  }

Internal ade4 functions

Description

Internal ade4 functions

Details

These are not to be called by the user.

Utility function to test if the adegraphics package is loaded

Description

This function check if the adegraphics package is loaded, before loading ade4. Mainly used to run examples using either ade4 or adegraphics function.

Usage

adegraphicsLoaded()

Value

A logical

Author(s)

Stéphane Dray (stephane.dray@univ-lyon1.fr)

Codon usage

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 Delphine.Charif@versailles.inra.fr

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)

Analysis of molecular variance

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 pavoine@mnhn.fr

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

Allelic frequencies in ten honeybees populations at eight microsatellites loci

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)

Apportionment of Quadratic Entropy

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 pavoine@mnhn.fr

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)

Distribution of Alpine plants in Aravo (Valloire, France)

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)

Fauna Table with double (row and column) partitioning

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)
}

Graphical Display of Areas

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)

Arrivals at an intensive care unit

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 sales@kovcomp.com 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)

Taxonomy

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 sebastien.ollier@u-psud.fr

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)

Small Ecological Dataset

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))}

Genetic variability of Cacadors

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)

Bird species distribution

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)

Fauna Table for Constrained Ordinations

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)

Ecological Tables Triplet

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))

Genomes of 43 Bacteria

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 Jean.Lobry@univ-lyon1.fr 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")
}

Table of Factors

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)
}

African Estuary Fishes

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))
}

Between-Class Analysis

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 anne-beatrice.dufour@univ-lyon1.fr

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)

Between-class coinertia analysis

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 stephane.dray@univ-lyon1.fr and Jean Thioulouse jean.thioulouse@univ-lyon1.fr

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)

Between-Class RLQ analysis

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 stephane.dray@univ-lyon1.fr

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)

Between-Class Analysis

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 anne-beatrice.dufour@univ-lyon1.fr
Stéphane Dray stephane.dray@univ-lyon1.fr

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)

Cubic Ecological Data

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)
}

Double Weighted Centring

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)

Wine Tasting

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))

Ecological and Biological Traits

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))

Buech basin

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)
  }
}

Genetics-Ecology-Environment Triple

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))
}

Between- and within-class double principal coordinate analysis

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 stephane.dray@univ-lyon1.fr

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)

Transformation to make Euclidean a distance matrix

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 stephane.dray@univ-lyon1.fr

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

Road Distances

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))
  }
  }

Phylogeny and quantative trait of carnivora

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))

Phylogeny and quantitative traits of carnivora

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)

Taxonomy, phylogenies and quantitative traits of carnivora and herbivora

Description

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.

Usage

data(carniherbi49)

Format

carniherbi49 is a list containing the 5 following objects :

taxo

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.

tre1

is a character string giving the phylogenetic tree in Newick format as reported by Garland et al. (1993).

tre2

is a character string giving the phylogenetic tree in Newick format as reported by Garland and Janis (1993).

tab1

is a data frame with 49 species and 2 traits: 'bodymass' (body mass (kg)) and 'homerange' (home range (km)).

tab2

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).

Source

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.

Examples

## 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)

Enzymatic polymorphism in Mus musculus

Description

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'.

Usage

data(casitas)

Format

The 74 individuals of casitas belong to 4 groups:

1

24 mice of the sub-species Mus musculus domesticus

2

11 mice of the sub-species Mus musculus castaneus

3

9 mice of the sub-species Mus musculus musculus

4

30 mice from a population of the lake Casitas (California)

Source

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/

References

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.

Examples

data(casitas)
str(casitas)
names(casitas)

Qualitative Weighted Variables

Description

This data set gives the age, the fecundity and the number of litters for 26 groups of cats.

Usage

data(chatcat)

Format

chatcat is a list of two objects :

tab

is a data frame with 3 factors (age, feco, nport).

eff

is a vector of numbers.

Details

One row of tab corresponds to one group of cats.
The value in eff is the number of cats in this group.

Source

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.

Examples

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

Pair of Variables

Description

This data set is a contingency table of age classes and fecundity classes of cats Felis catus.

Usage

data(chats)

Format

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).

Source

Legay, J.M. and Pontier, D. (1985) Relation âge-fécondité dans les populations de Chats domestiques, Felis catus. Mammalia, 49, 395–402.

Examples

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))
}

Charolais-Zebus

Description

This data set gives six different weights of 23 charolais and zebu oxen.

Usage

data(chazeb)

Format

chazeb is a list of 2 components.

tab

is a data frame with 23 rows and 6 columns.

cla

is a factor with two levels "cha" and "zeb".

Source

Tomassone, R., Danzard, M., Daudin, J. J. and Masson J. P. (1988) Discrimination et classement, Masson, Paris. p. 43

Examples

data(chazeb)
if(!adegraphicsLoaded())
  plot(discrimin(dudi.pca(chazeb$tab, scan = FALSE), 
    chazeb$cla, scan = FALSE))

Enzymatic polymorphism in Leuciscus cephalus

Description

This data set contains a list of three components: spatial map, allellic profiles and sample sizes.

Usage

data(chevaine)

Format

This data set is a list of three components:

tab

a data frame with 27 populations and 9 allelic frequencies (4 locus)

coo

a list containing all the elements to build a spatial map

eff

a numeric containing the numbers of fish samples per station

References

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).

Examples

data(chevaine)
names(chevaine)
str(chevaine)

Veterinary epidemiological study to assess the risk factors for losses in broiler chickens

Description

This data set contains information about potential risk factors for losses in broiler chickens

Usage

data(chickenk)

Format

A list with 5 components:

mortality

a data frame with 351 observations and 4 variables which describe the losses (dependent dataset Y)

FarmStructure

a data frame with 351 observations and 5 variables which describe the farm structure (explanatory dataset)

OnFarmHistory

a data frame with 351 observations and 4 variables which describe the flock characteristics at placement (explanatory dataset)

FlockCharacteristics

a data frame with 351 observations and 6 variables which describe the flock characteristics during the rearing period (explanatory dataset)

CatchingTranspSlaught

a data frame with 351 observations and 5 variables which describe the transport, lairage conditions, slaughterhouse and inspection features (explanatory dataset)

Source

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

Examples

data(chickenk)
kta1 <- ktab.list.df(chickenk)

Fruit Production

Description

The clementines is a data set containing the fruit production of 20 clementine trees during 15 years.

Usage

data(clementines)

Format

A data frame with 15 rows and 20 columns

Source

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.

Examples

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))

Frequenting movie theaters in France in 2003

Description

cnc2003 is a data frame with 94 rows (94 departments from continental Metropolitan France)and 12 variables.

Usage

data(cnc2003)

Format

This data frame contains the following variables:

popu

is the population department in million inhabitants.

entr

is the number of movie theater visitors in million.

rece

is the takings from ticket offices.

sean

is the number of proposed shows in thousands.

comm

is the number of equipped communes in movie theaters (units).

etab

is the number of active movie theaters (units).

salle

is the number of active screens.

faut

is the number of proposed seats.

artes

is the number of movie theaters offering "Art and Essay" movies.

multi

is the number of active multiplexes.

depart

is the name of the department.

reg

is the administrative region of the department.

Source

National Center of Cinematography (CNC), september 2003

See Also

This dataset is compatible with elec88 and presid2002

Examples

data(cnc2003)
sco.quant(cnc2003$popu, cnc2003[,2:10], abline = TRUE, csub = 3)

Coinertia Analysis

Description

The coinertia analysis performs a double inertia analysis of two tables.

Usage

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, ...)

Arguments

Value

Returns a list of class 'coinertia', sub-class 'dudi' containing:

WARNING

IMPORTANT : dudi1 and dudi2 must have identical row weights.

Author(s)

Daniel Chessel
Anne-Béatrice Dufour anne-beatrice.dufour@univ-lyon1.fr

References

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.

Examples

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)
}

Table of Fuzzy Biological Traits

Description

This data set coleo (coleoptera) is a a fuzzy biological traits table.

Usage

data(coleo)

Format

coleo is a list of 5 components.

tab

is a data frame with 110 rows (species) and 32 columns (categories).

species.names

is a vector of species names.

moda.names

is a vector of fuzzy variables names.

families

is a factor species family.

col.blocks

is a vector containing the number of categories of each trait.

Source

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.

Examples

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 3-table methods

Description

Functions to combine and adjust the outputs of the fourthcorner and randtest.rlq functions created using permutational models 2 and 4 (sequential approach).

Usage

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"))

Arguments

Details

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).

Value

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.

Author(s)

Stéphane Dray stephane.dray@univ-lyon1.fr

References

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.

See Also

rlq, fourthcorner, p.adjust.methods

Examples

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")

Tests of randomization between distances applied to 'kdist' objetcs

Description

The mantelkdist and RVkdist functions apply to blocks of distance matrices the mantel.rtest and RV.rtest functions.

Usage

mantelkdist (kd, nrepet = 999, ...)
RVkdist (kd, nrepet = 999, ...)
## S3 method for class 'corkdist'
plot(x, whichinrow = NULL, whichincol = NULL, 
   gap = 4, nclass = 10,...)

Arguments

Details

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.

Value

a list of class corkdist containing for each pair of distances an object of class randtest (permutation tests).

Author(s)

Daniel Chessel
Stéphane Dray stephane.dray@univ-lyon1.fr

Examples

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)

Corvus morphology

Description

This data set gives a morphological description of 28 species of the genus Corvus split in two habitat types and phylogeographic stocks.

Usage

data(corvus)

Format

corvus is data frame with 28 observations (the species) and 4 variables :

wing

: wing length (mm)

bill

: bill length (mm)

habitat

: habitat with two levels clos and open

phylog

: phylogeographic stock with three levels amer(America), orien(Oriental-Australian), pale(Paleoarctic-African)

References

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.

Examples

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)
}

STATIS and Co-Inertia : Analysis of a series of paired ecological tables

Description

Analysis of a series of pairs of ecological tables. This function uses Partial Triadic Analysis (pta) and coinertia to do the computations.

Usage

costatis(KTX, KTY, scannf = TRUE)

Arguments

Details

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.

Value

a list of class coinertia, subclass dudi. See coinertia

WARNING

IMPORTANT : KTX and KTY must have the same k-tables structure, the same number of columns, and the same column weights.

Author(s)

Jean Thioulouse Jean.Thioulouse@univ-lyon1.fr

References

Thioulouse J. (2011). Simultaneous analysis of a sequence of paired ecological tables: a comparison of several methods. Annals of Applied Statistics, 5, 2300-2325.

Examples

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)

Monte-Carlo test on a Costatis analysis (in C).

Description

Performs a Monte-Carlo test on a Costatis analysis.

Usage

costatis.randtest(KTX, KTY, nrepet = 999, ...)

Arguments

Value

a list of the class randtest

Author(s)

Jean Thioulouse Jean.Thioulouse@univ-lyon1.fr

References

Thioulouse J. (2011). Simultaneous analysis of a sequence of paired ecological tables: a comparison of several methods. Annals of Applied Statistics, 5, 2300-2325.

Examples

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)

Dagnelie multinormality test

Description

Compute Dagnelie test of multivariate normality on a data table of n objects (rows) and p variables (columns), with n > (p+1).

Usage

dagnelie.test(x)

Arguments

Details

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.

Value

A list containing the following results:

Author(s)

Daniel Borcard and Pierre Legendre

References

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.

Examples

 # 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)

Exam marks for some students

Description

This data set gives the exam results of 104 students in the second year of a French University onto 9 subjects.

Usage

data(deug)

Format

deug is a list of three components.

tab

is a data frame with 104 students and 9 subjects : Algebra, Analysis, Proba, Informatic, Economy, Option1, Option2, English, Sport.

result

is a factor of 104 components giving the final exam levels (A+, A, B, B-, C-, D).

cent

is a vector of required marks by subject to get exactly 10/20 with a coefficient.

Source

University of Lyon 1

Examples

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)
}

Rao's dissimilarity coefficient

Description

Calculates the root square of Rao's dissimilarity coefficient between samples.

Usage

disc(samples, dis = NULL, structures = NULL)

Arguments

Value

Returns a list of objects of class dist

Author(s)

Sandrine Pavoine pavoine@mnhn.fr

References

Rao, C.R. (1982) Diversity and dissimilarity coefficients: a unified approach. Theoretical Population Biology, 21, 24–43.

Examples

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)

Linear Discriminant Analysis (descriptive statistic)

Description

performs a linear discriminant analysis.

Usage

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, ...) 

Arguments

Value

returns a list of class 'discrimin' containing :

Author(s)

Daniel Chessel
Anne-Béatrice Dufour anne-beatrice.dufour@univ-lyon1.fr

See Also

lda in package MASS

Examples

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))

Discriminant Correspondence Analysis

Description

performs a discriminant correspondence analysis.

Usage

discrimin.coa(df, fac, scannf = TRUE, nf = 2)

Arguments

Value

a list of class discrimin. See discrimin

Author(s)

Daniel Chessel
Anne-Béatrice Dufour anne-beatrice.dufour@univ-lyon1.fr

References

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.

Examples

data(perthi02)
plot(discrimin.coa(perthi02$tab, perthi02$cla, scan = FALSE))

Computation of Distance Matrices for Binary Data

Description

computes for binary data some distance matrice.

Usage

dist.binary(df, method = NULL, diag = FALSE, upper = FALSE)

Arguments

Details

Let be the contingency table of binary data such as n_{11} = a, n_{10} = b, n_{01} = c and n_{00} = d. All these distances are of type d=\sqrt{1-s} with s a similarity coefficient.

1 = Jaccard index (1901)

S3 coefficient of Gower & Legendre s_1 = \frac{a}{a+b+c}

2 = Simple matching coefficient of Sokal & Michener (1958)

S4 coefficient of Gower & Legendre s_2 =\frac{a+d}{a+b+c+d}

3 = Sokal & Sneath(1963)

S5 coefficient of Gower & Legendre s_3 =\frac{a}{a+2(b+c)}

4 = Rogers & Tanimoto (1960)

S6 coefficient of Gower & Legendre s_4 =\frac{a+d}{(a+2(b+c)+d)}

5 = Dice (1945) or Sorensen (1948)

S7 coefficient of Gower & Legendre s_5 =\frac{2a}{2a+b+c}

6 = Hamann coefficient

S9 index of Gower & Legendre (1986) s_6 =\frac{a-(b+c)+d}{a+b+c+d}

7 = Ochiai (1957)

S12 coefficient of Gower & Legendre s_7 =\frac{a}{\sqrt{(a+b)(a+c)}}

8 = Sokal & Sneath (1963)

S13 coefficient of Gower & Legendre s_8 =\frac{ad}{\sqrt{(a+b)(a+c)(d+b)(d+c)}}

9 = Phi of Pearson

S14 coefficient of Gower & Legendre s_9 =\frac{ad-bc}{\sqrt{(a+b)(a+c)(b+d)(d+c)}}

10 = S2 coefficient of Gower & Legendre

s_1 = \frac{a}{a+b+c+d}

Value

returns a distance matrix of class dist between the rows of the data frame

Author(s)

Daniel Chessel
Stéphane Dray stephane.dray@univ-lyon1.fr

References

Gower, J.C. and Legendre, P. (1986) Metric and Euclidean properties of dissimilarity coefficients. Journal of Classification, 3, 5–48.

Examples

data(aviurba)
for (i in 1:10) {
    d <- dist.binary(aviurba$fau, method = i)
    cat(attr(d, "method"), is.euclid(d), "\n")}

Computation of the Distance Matrix from a Statistical Triplet

Description

computes for a statistical triplet a distance matrix.

Usage

dist.dudi(dudi, amongrow = TRUE)

Arguments

Value

an object of class dist

Author(s)

Daniel Chessel
Stéphane Dray stephane.dray@univ-lyon1.fr

Examples

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

Mixed-variables coefficient of distance

Description

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.

Usage

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))

Arguments

Details

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

Value

The functions provide the following results:

Author(s)

Sandrine Pavoine pavoine@mnhn.fr

References

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

See Also

daisy in the case of ratio-scale (quantitative) and nominal variables; and woangers for an application.

Examples

# 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)

Computation of the Distance Matrix associated to a Neighbouring Graph

Description

This distance matrix between two points is the length of the shortest path between these points.

Usage

dist.neig(neig)

Arguments

Value

returns a distance matrix, object of class dist

Author(s)

Daniel Chessel
Stéphane Dray stephane.dray@univ-lyon1.fr

Examples

    data(elec88)
    d0 <- dist.neig(nb2neig(elec88$nb))
    plot(dist(elec88$xy),d0)

Computation of Distance Matrices of Percentage Data

Description

computes for percentage data some distance matrices.

Usage

dist.prop(df, method = NULL, diag = FALSE, upper = FALSE)

Arguments

Details

1 = Manly

d_1=\frac{1}{2} \sum_{i=1}^{K}{|{p_i-q_i}|}

2 = Overlap index Manly

d_2=1-\frac{\sum_{i=1}^{K}{p_i q_i}}{\sqrt{\sum_{i=1}^{K}{p_i^2}}{\sqrt{\sum_{i=1}^{K}{q_i^2}}}}

3 = Rogers 1972 (one locus)

d_3=\sqrt{\frac{1}{2} \sum_{i=1}^{K}{(p_i-q_i)^2}}

4 = Nei 1972 (one locus)

d_4=\ln{\frac{\sum_{i=1}^{K}{p_i q_i}}{\sqrt{\sum_{i=1}^{K}{p_i^2}}{\sqrt{\sum_{i=1}^{K}{q_i^2}}}}}

5 = Edwards 1971 (one locus)

d_5=\sqrt{1-\sum_{i=1}^{K}{\sqrt{p_1 q_i}}}

Value

returns a distance matrix, object of class dist

Author(s)

Daniel Chessel
Stéphane Dray stephane.dray@univ-lyon1.fr

References

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.

Examples

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))
}

Computation of Distance Matrices on Quantitative Variables

Description

computes on quantitative variables, some distance matrices as canonical, Joreskog and Mahalanobis.

Usage

dist.quant(df, method = NULL, diag = FALSE, upper = FALSE, 
    tol = 1e-07)

Arguments

Details

All the distances are of type d=\|x-y\|_A = \sqrt{(x-y)^{t}A(x-y)}

1 = Canonical

A = Identity

2 = Joreskog

A=\frac{1}{diag(cov)}

3 = Mahalanobis

A = inv(cov)

Value

an object of class dist

Author(s)

Daniel Chessel
Stéphane Dray stephane.dray@univ-lyon1.fr

Examples

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))
}

Rao's diversity coefficient also called quadratic entropy

Description

Calculates Rao's diversity coefficient within samples.

Usage

divc(df, dis, scale)

Arguments

Value

Returns a data frame with samples as rows and the diversity coefficient within samples as columns

Author(s)

Sandrine Pavoine pavoine@mnhn.fr

References

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.

Examples

data(ecomor)
dtaxo <- dist.taxo(ecomor$taxo)
divc(ecomor$habitat, dtaxo)

data(humDNAm)
divc(humDNAm$samples, sqrt(humDNAm$distances))

Maximal value of Rao's diversity coefficient also called quadratic entropy

Description

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.

Usage

divcmax(dis, epsilon, comment)

Arguments

Value

Returns a list

Author(s)

Stéphane Champely Stephane.Champely@univ-lyon1.fr
Sandrine Pavoine pavoine@mnhn.fr

References

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.

Examples

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)
}

Representation of many quantitative variables in front of a phylogenetic tree

Description

dotchart.phylog represents the phylogenetic tree and draws Cleveland dot plot of each variable.

Usage

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)), ...)

Arguments

Author(s)

Daniel Chessel
Sébastien Ollier sebastien.ollier@u-psud.fr

See Also

symbols.phylog and table.phylog

Examples

# 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))

Representation of n values on a circle

Description

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.

Usage

dotcircle(z, alpha0 = pi/2, xlim = range(pretty(z)),
 labels = names(z), clabel = 1, cleg = 1)

Arguments

Author(s)

Daniel Chessel

See Also

circ.plot

Examples

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))

Pair of Ecological Tables

Description

This data set gives environmental variables, fish species and spatial coordinates for 30 sites.

Usage

data(doubs)

Format

doubs is a list with 4 components.

env

is a data frame with 30 rows (sites) and 11 environmental variables.

fish

is a data frame with 30 rows (sites) and 27 fish species.

xy

is a data frame with 30 rows (sites) and 2 spatial coordinates.

species

is a data frame with 27 rows (species) and 4 columns (names).

Details

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 (\ln(x + 1) 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.

Source

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.

References

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.

Examples

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))
}

Double principal coordinate analysis

Description

Performs a double principal coordinate analysis

Usage

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, ...)

Arguments

Value

Returns a list of class dpcoa containing:

Author(s)

Daniel Chessel
Sandrine Pavoine pavoine@mnhn.fr
Stéphane Dray stephane.dray@univ-lyon1.fr

References

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.

Examples

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)

Duality Diagram

Description

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.

Usage

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]

Arguments

Value

as.dudi and all the functions that use it return a list with the following components :

Author(s)

Daniel Chessel
Anne-Béatrice Dufour anne-beatrice.dufour@univ-lyon1.fr
Stéphane Dray stephane.dray@univ-lyon1.fr

References

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.

Examples

data(deug)
dd1 <- dudi.pca(deug$tab, scannf = FALSE)
dd1
t(dd1)
is.dudi(dd1)
redo.dudi(dd1,3)
summary(dd1)

Multiple Correspondence Analysis

Description

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.

Usage

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, ...) 

Arguments

Value

dudi.acm returns a list of class acm and dudi (see dudi) containing

Author(s)

Daniel Chessel
Anne-Béatrice Dufour anne-beatrice.dufour@univ-lyon1.fr

References

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.

See Also

s.chull, s.class

Examples

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)

Correspondence Analysis

Description

performs a correspondence analysis.

Usage

dudi.coa(df, scannf = TRUE, nf = 2)

Arguments

Value

returns a list of class coa and dudi (see dudi) containing

Author(s)

Daniel Chessel
Anne-Béatrice Dufour anne-beatrice.dufour@univ-lyon1.fr

References

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.

Examples

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)

Decentred Correspondence Analysis

Description

performs a decentred correspondence analysis.

Usage

dudi.dec(df, eff, scannf = TRUE, nf = 2)

Arguments

Value

Returns a list of class dec and dudi (see dudi) containing also

Author(s)

Daniel Chessel
Anne-Béatrice Dufour anne-beatrice.dufour@univ-lyon1.fr

References

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.

Examples

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))

Fuzzy Correspondence Analysis and Fuzzy Principal Components Analysis

Description

Theses functions analyse a table of fuzzy variables.

A fuzzy variable takes values of type a=(a_1,\dots,a_k) 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.

Usage

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)

Arguments

Value

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

The function dudi.fpca returns a list of class pca and dudi (see dudi) containing also

1. cent

2. norm

3. blo

4. indica

5. FST

6. inertia

Author(s)

Daniel Chessel
Anne-Béatrice Dufour anne-beatrice.dufour@univ-lyon1.fr

References

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.

Examples

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)

Ordination of Tables mixing quantitative variables and factors

Description

performs a multivariate analysis with mixed quantitative variables and factors.

Usage

dudi.hillsmith(df, row.w = rep(1, nrow(df))/nrow(df), 
   scannf = TRUE, nf = 2)

Arguments

Details

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

Value

Returns a list of class mix and dudi (see dudi) containing also

Author(s)

Stéphane Dray stephane.dray@univ-lyon1.fr
Anne-Béatrice Dufour anne-beatrice.dufour@univ-lyon1.fr

References

Hill, M. O., and A. J. E. Smith. 1976. Principal component analysis of taxonomic data with multi-state discrete characters. Taxon, 25, 249-255.

See Also

dudi.mix

Examples

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)
}

Ordination of Tables mixing quantitative variables and factors

Description

performs a multivariate analysis with mixed quantitative variables and factors.

Usage

dudi.mix(df, add.square = FALSE, scannf = TRUE, nf = 2)

Arguments

Details

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.

Value

Returns a list of class mix and dudi (see dudi) containing also

Author(s)

Daniel Chessel
Anne-Béatrice Dufour anne-beatrice.dufour@univ-lyon1.fr

References

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.

Examples

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)
}  

Non symmetric correspondence analysis

Description

performs a non symmetric correspondence analysis.

Usage

dudi.nsc(df, scannf = TRUE, nf = 2)

Arguments

Value

Returns a list of class nsc and dudi (see dudi) containing also

Author(s)

Daniel Chessel
Anne-Béatrice Dufour anne-beatrice.dufour@univ-lyon1.fr

References

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.

Examples

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
}

Principal Component Analysis

Description

dudi.pca performs a principal component analysis of a data frame and returns the results as objects of class pca and dudi.

Usage

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)

Arguments

Value

Returns a list of classes pca and dudi (see dudi) containing the used information for computing the principal component analysis :

Author(s)

Daniel Chessel
Anne-Béatrice Dufour anne-beatrice.dufour@univ-lyon1.fr

See Also

prcomp, princomp in the mva library

Examples

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))
}

Principal Coordinates Analysis

Description

dudi.pco performs a principal coordinates analysis of a Euclidean distance matrix and returns the results as objects of class pco and dudi.

Usage

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, ...) 

Arguments

Value

dudi.pco returns a list of class pco and dudi. See dudi

Author(s)

Daniel Chessel
Anne-Béatrice Dufour anne-beatrice.dufour@univ-lyon1.fr

References

Gower, J. C. (1966) Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika, 53, 325–338.

Examples

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))

Dune Meadow Data

Description

dunedata is a data set containing for 20 sites, environmental variables and plant species.

Usage

data(dunedata)

Format

dunedata is a list with 2 components.

envir

is a data frame with 20 rows (sites) 5 columns (environnemental variables).

veg

is a data frame with 20 rows (sites) 30 columns (plant species).

Source

Jongman, R. H., ter Braak, C. J. F. and van Tongeren, O. F. R. (1987) Data analysis in community and landscape ecology, Pudoc, Wageningen.

Examples

data(dunedata)
summary(dunedata$envir)
is.ordered(dunedata$envir$use)
score(dudi.mix(dunedata$envir, scan = FALSE))

Electrocardiogram data

Description

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.

Usage

data(ecg)

Format

A vector of class ts containing 2048 observations.

Source

Gust Bardy and Per Reinhall, University of Washington

References

Percival, D. B., and Walden, A.T. (2000) Wavelet Methods for Time Series Analysis, Cambridge University Press.

Examples

## 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)

Ecomorphological Convergence

Description

This data set gives ecomorphological informations about 129 bird species.

Usage

data(ecomor)

Format

ecomor is a list of 7 components.

forsub

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.

diet

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.

habitat

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.

morpho

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).

taxo

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.

labels

is a data frame with vectors of the names of species (complete and in abbreviated form.

categ

is a data frame with 129 species, 2 factors : 'forsub' summarizing the feeding place and 'diet' the diet type.

Source

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.

References

See a data description at http://pbil.univ-lyon1.fr/R/pdf/pps023.pdf (in French).

Examples

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)

Electoral Data

Description

This data set gives the results of the presidential election in France in 1988 for each department and all the candidates.

Usage

data(elec88)

Format

elec88 is a list with the following components:

tab

a data frame with 94 rows (departments) and 9 variables (candidates)

res

the global result of the election all-over the country

lab

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.

area

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.

contour

a data frame with 4 variables (x1, y1, x2, y2) for the contour display of France

xy

a data frame with two variables (x, y) giving the position of the center for each department

nb

the neighbouring graph between departments, object of the class nb

Spatial

the map of the french departments in Lambert II coordinates (an object of the class SpatialPolygons of sp)

Spatial.contour

the contour of the map of France in Lambert II coordinates (an object of the class SpatialPolygons of sp)

Source

Public data

See Also

This dataset is compatible with presid2002 and cnc2003

Examples

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))
}

K-tables of wine-tasting

Description

This data set describes 27 characteristics of 21 wines distributed in four fields : rest, visual, olfactory and global.

Usage

data(escopage)

Format

escopage is a list of 3 components.

tab

is a data frame with 21 observations (wines) and 27 variables.

tab.names

is the vector of the names of sub-tables : "rest" "visual" "olfactory" "global".

blo

is a vector of the numbers of variables for each sub-table.

Source

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.

Examples

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))

Triangular Data

Description

This data set gives the proportions of employement in the primary, secondary and tertiary sectors for 12 European countries in 1978, 1986 and 1997.

Usage

data(euro123)

Format

euro123 is a list of 4 components.

in78

is a data frame with 12 rows and 3 variables.

in86

: idem in 1986

in97

: idem in 1997

plan

is a data frame with two factors to both organize the 3 tables.

Source

Encyclopaedia Universalis, Symposium, Les chiffres du Monde. Encyclopaedia Universalis, Paris. 519.

Examples

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))
}

Fission pattern and heritable morphological traits

Description

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.

Usage

data(fission)

Format

fission is a list containing the 2 following objects :

tre

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

tab

is a data frame with 8 social groups and five traits : cranial metrics (GM1, GM2, GM3) and cranial non metrics (GN1, GN2)

References

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.

Examples

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 Correspondence Analysis with the same rows and the same columns

Description

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.

Usage

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, ...) 

Arguments

Value

foucart returns a list of the classes 'dudi', 'coa' and 'foucart'

Author(s)

Pierre Bady pierre.bady@univ-lyon1.fr
Anne-Béatrice Dufour anne-beatrice.dufour@univ-lyon1.fr

References

Foucart, T. (1984) Analyse factorielle de tableaux multiples, Masson, Paris.

Examples

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)
}

Functions to compute the fourth-corner statistic

Description

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.

Usage

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"), ...)

Arguments

Details

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.

Value

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).

Author(s)

Stéphane Dray stephane.dray@univ-lyon1.fr

References

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

See Also

rlq, combine.4thcorner, p.adjust.methods

Examples

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")

Faunistic K-tables

Description

This data set gives informations about sites, species and environmental variables.

Usage

data(friday87)

Format

friday87 is a list of 4 components.

fau

is a data frame containing a faunistic table with 16 sites and 91 species.

mil

is a data frame with 16 sites and 11 environmental variables.

fau.blo

is a vector of the number of species per group.

tab.names

is the name of each group of species.

Source

Friday, L.E. (1987) The diversity of macroinvertebrate and macrophyte communities in ponds, Freshwater Biology, 18, 87–104.

Examples

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)
}

Pair of Tables

Description

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.

Usage

data(fruits)

Format

fruits is a list of 3 components:

typ

is a vector returning the type of the 28 batches of fruits (peaches or nectarines).

jug

is a data frame of 28 rows and 16 columns (judges).

var

is a data frame of 28 rows and 16 measures (average of 2 judgements).

Details

fruits$var is a data frame of 15 variables:

1. taches: quantity of cork blemishes (0=absent - maximum 5)

2. stries: quantity of stria (1/none - maximum 4)

3. abmucr: abundance of mucron (1/absent - 4)

4. irform: shape irregularity (0/none - 3)

5. allong: length of the fruit (1/round fruit - 4)

6. suroug: percentage of the red surface (minimum 40% - maximum 90%)

7. homlot: homogeneity of the intra-batch coloring (1/strong - 4)

8. homfru: homogeneity of the intra-fruit coloring (1/strong - 4)

9. pubesc: pubescence (0/none - 4)

10. verrou: intensity of green in red area (1/none - 4)

11. foncee: intensity of dark area (0/pink - 4)

12. comucr: intensity of the mucron color (1=no contrast - 4/dark)

13. impres: kind of impression (1/watched - 4/pointillé)

14. coldom: intensity of the predominating color (0/clear - 4)

15. calibr: grade (1/<90g - 5/>200g)

Source

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.

Examples

 
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))
}

Moran's I and Geary'c randomization tests for spatial and phylogenetic autocorrelation

Description

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.

Usage

gearymoran(bilis, X, nrepet = 999, alter=c("greater", "less", "two-sided"))

Arguments

Details

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 :

a_{ij} = \frac{bilis_{ij}}{\sum_{i=1}^{n}\sum_{j=1}^{n}bilis_{ij}}

The neighbouring weights is defined by the matrix D = diag(d_1,d_2, \ldots) where d_i = \sum_{j=1}^{n}bilis_{ij}. For each vector x of the data frame X, the test is based on the Moran statistic x^{t}Ax where x is D-centred.

Value

Returns an object of class krandtest (randomization tests).

Author(s)

Sébastien Ollier sebastien.ollier@u-psud.fr
Daniel Chessel

References

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.

See Also

moran.test and geary.test for classical versions of Moran's test and Geary's one

Examples

# 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)

Microsatellites of Galapagos tortoises populations

Description

This data set gives genetic relationships between Galapagos tortoises populations with 10 microsatellites.

Usage

data(ggtortoises)

Format

ggtortoises is a list with the following components:

area

a data frame designed to be used in the area.plot function

ico

a list of three pixmap icons representing the tortoises morphotypes

pop

a data frame containing meta informations about populations

misc

a data frame containing the coordinates of the island labels

loc

a numeric vector giving the number of alleles by marker

tab

a data frame containing the number of alleles by populations for 10 microsatellites

Spatial

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

Source

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.

References

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).

Examples

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)
  }
}

Granulometric Curves

Description

This data set gives the repartition in diameter classes of deposit samples.

Usage

data(granulo)

Format

granulo is a list of 2 components.

tab

contains the 49 deposit samples, 9 diameter classes, weight of grains by size class

born

contains the boundaries of the diameter classes

Source

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.

Examples

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)
}

Complete regular grid analysis

Description

This function defines objects to analyse data sets associated with complete regular grid.

Usage

gridrowcol(nrow, ncol, cell.names = NULL)

Arguments

Value

Returns a list containing the following items :

Author(s)

Sébastien Ollier sebastien.ollier@u-psud.fr
Daniel Chessel

References

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.

See Also

orthobasis, orthogram, mld

Examples

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))
}

Genetic Variation In Human Populations

Description

This data set gives genotypes variation of 1066 individuals belonging to 52 predefined populations, for 404 microsatellite markers.

Usage

data(hdpg)

Format

hdpg is a list of 3 components.

tab

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).

ind

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).

locus

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.

Details

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.

Source

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.

Examples

data(hdpg)
names(hdpg)
str(hdpg)

Morphometric data set

Description

Morphometric data set describing the shape of the first upper molar in populations of the Western European house mouse (Mus musculus domesticus)

Usage

data(houmousr)

Format

houmousr is a list with 2 components.

dfcc

is a data frame with 214 rows (mice) and 128 morphometric variables.

faccc

is a factor giving the sampling location of the 214 mice.

Details

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.

Source

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

References

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

Examples

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)

Contingency Table

Description

The housetasks data frame gives 13 housetasks and their repartition in the couple.

Usage

data(housetasks)

Format

This data frame contains four columns : wife, alternating, husband and jointly. Each column is a numeric vector.

Source

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

Examples

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) 
}

human mitochondrial DNA restriction data

Description

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.

Usage

data(humDNAm)

Format

humDNAm is a list of 3 components.

distances

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.

samples

is a data frame with 56 haplotypes, 10 abundance variables (populations). These variables give the haplotype abundance in a given population.

structures

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.

Source

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.

Examples

data(humDNAm)
dpcoahum <- dpcoa(data.frame(t(humDNAm$samples)), 
    sqrt(humDNAm$distances), scan = FALSE, nf = 2)
plot(dpcoahum)

Point sampling of fish community

Description

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.

Usage

data(ichtyo)

Format

ichtyo is a list of 3 components.

tab

is a faunistic array with 9 columns and 32 rows.

eff

is a vector of the 32 sampling effort.

dat

is a factor where the levels are the 10 years of the sampling occasion.

Details

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.

Source

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.

Examples

data(ichtyo)
dudi1 <- dudi.dec(ichtyo$tab, ichtyo$eff, scannf = FALSE)
s.class(dudi1$li, ichtyo$dat, wt = ichtyo$eff / sum(ichtyo$eff))

Decomposition of inertia (i.e. contributions) in multivariate methods

Description

Computes the decomposition of inertia to measure the contributions of row and/or columns in multivariate methods

Usage

## 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, ...)

Arguments

Details

Contributions are printed in percentage and the sign is the sign of the coordinates

Value

An object of class inertia, i.e. a list containing :

Author(s)

Daniel Chessel
Stéphane Dray stephane.dray@univ-lyon1.fr
Anne-Béatrice Dufour anne-beatrice.dufour@univ-lyon1.fr

References

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.

Examples

data(housetasks)
coa1 <- dudi.coa(housetasks, scann = FALSE)
res <- inertia(coa1, col = TRUE, row = FALSE)
res
summary(res)

Geary's Irish Data

Description

This data set contains geographical informations about 25 counties of Ireland.

Usage

data(irishdata)

Format

irishdata is a list of 13 components:

area

a data frame with polygons for each of the 25 contiguous counties

county.names

a vector with the names of the 25 counties

xy

a data frame with the coordinates centers of the 25 counties

tab

a data frame with 25 rows (counties) and 12 variables

contour

a data frame with the global polygon of all the 25 counties

link

a matrix containing the common length between two counties from area

area.utm

a data frame with polygons for each of the 25 contiguous counties expressed in Universal Transverse Mercator (UTM) coordinates

xy.utm

a data frame with the UTM coordinates centers of the 25 counties

link.utm

a matrix containing the common length between two counties from area.utm

tab.utm

a data frame with the 25 counties (explicitly named) and 12 variables

contour.utm

a data frame with the global polygon of all the 25 counties expressed in UTM coordinates

Spatial

the map of the 25 counties of Ireland (an object of the class SpatialPolygons of sp)

Spatial.contour

the contour of the map of the 25 counties of Ireland (an object of the class SpatialPolygons of sp)

Source

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.

Examples

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))
}

Is a Distance Matrix Euclidean?

Description

Confirmation of the Euclidean nature of a distance matrix by the Gower's theorem.
is.euclid is used in summary.dist.

Usage

is.euclid(distmat, plot = FALSE, print = FALSE, tol = 1e-07)
## S3 method for class 'dist'
summary(object, ...)

Arguments

Value

returns a logical value indicating if all the eigenvalues are positive or equal to zero

Author(s)

Daniel Chessel
Stéphane Dray stephane.dray@univ-lyon1.fr

References

Gower, J.C. and Legendre, P. (1986) Metric and Euclidean properties of dissimilarity coefficients. Journal of Classification, 3, 5–48.

Examples

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)

Seed dispersal

Description

This data set gives the spatial distribution of seeds (quadrats counts) of seven species in the understorey of tropical rainforest.

Usage

data(julliot)

Format

julliot is a list with the following components:

tab

a data frame with 160 rows (quadrats) and 7 variables (species)

xy

a data frame with the coordinates of the 160 quadrats (positioned by their centers)

area

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.

Spatial

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

Details

Species names of julliot$tab are: Pouteria torta, Minquartia guianensis, Quiina obovata, Chrysophyllum lucentifolium, Parahancornia fasciculata, Virola michelii, and Pourouma spp.

References

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.

Examples

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)

K-tables Multi-Regions

Description

This data set gives physical and physico-chemical variables, fish species, spatial coordinates about 92 sites.

Usage

data(jv73)

Format

jv73 is a list with the following components:

morpho

a data frame with 92 sites and 6 physical variables

phychi

a data frame with 92 sites and 12 physico-chemical variables

poi

a data frame with 92 sites and 19 fish species

xy

a data frame with 92 sites and 2 spatial coordinates

contour

a data frame for mapping

fac.riv

a factor distributing the 92 sites on 12 rivers

Spatial

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

Source

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.

References

See a data description at http://pbil.univ-lyon1.fr/R/pdf/pps047.pdf (in French).

Examples

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)
}

Ponds in a nature reserve

Description

This data set contains informations about 33 ponds in De Maten reserve (Genk, Belgium).

Usage

data(kcponds)

Format

kponds is a list with the following components:

tab

a data frame with 15 environmental variables (columns) on 33 ponds (rows)

area

an object of class area

xy

a data frame with the coordinates of ponds

nb

the neighbourhood graph of the 33 sites (an object of class nb)

Spatial

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

Details

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)

Source

Cottenie, K. (2002) Local and regional processes in a zooplankton metacommunity. PhD, Katholieke Universiteit Leuven, Leuven, Belgium.

Examples

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)

the class of objects 'kdist' (K distance matrices)

Description

An object of class kdist is a list of distance matrices observed on the same individuals

Usage

kdist(..., epsi = 1e-07, upper = FALSE)

Arguments

Details

The attributs of a 'kdist' object are:
names: the names of the distances
size: the number of points between distances are known
labels: the labels of points
euclid: a logical vector indicating whether each distance of the list is Euclidean or not.
call: a call order
class: object 'kdist'

Value

returns an object of class 'kdist' containing a list of semidefinite matrices.

Author(s)

Daniel Chessel
Anne-Béatrice Dufour anne-beatrice.dufour@univ-lyon1.fr

References

Gower, J. C. (1966) Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika, 53, 325–338.

Examples

# 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

Transformation of K distance matrices (object 'kdist') into K Euclidean representations (object 'ktab')

Description

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.

Usage

kdist2ktab(kd, scale = TRUE, tol = 1e-07)

Arguments

Value

returns a list of class ktab containing for each distance of kd the data frame of its Euclidean representation

Author(s)

Daniel Chessel
Anne-Béatrice Dufour anne-beatrice.dufour@univ-lyon1.fr

Examples

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

Description

a way to obtain Euclidean distance matrices

Usage

kdisteuclid(obj, method = c("lingoes", "cailliez", "quasi"))

Arguments

Value

returns an object of class kdist with all distances Euclidean.

Author(s)

Daniel Chessel
Stéphane Dray stephane.dray@univ-lyon1.fr

References

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.

Examples

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)

Generic Function for Multiple Graphs in a K-tables Analysis

Description

Methods for foucart, mcoa, mfa, pta, sepan, sepan.coa and statis

Usage

kplot(object, ...)

Arguments

Examples

methods(plot)
methods(scatter)
methods(kplot)

Multiple Graphs for the Foucart's Correspondence Analysis

Description

performs high level plots of a Foucart's Correspondence Analysis, using an object of class foucart.

Usage

## 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", ...)

Arguments

Examples

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)
}

Multiple Graphs for a Multiple Co-inertia Analysis

Description

performs high level plots of a Multiple Co-inertia Analysis, using an object of class mcoa.

Usage

## 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",...)

Arguments

Author(s)

Daniel Chessel

Examples

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")

Multiple Graphs for a Multiple Factorial Analysis

Description

performs high level plots of a Multiple Factorial Analysis, using an object of class mfa.

Usage

## 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", ...)

Arguments

Author(s)

Daniel Chessel

Examples

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)

Multiple Graphs for a Partial Triadic Analysis

Description

performs high level plots of a Partial Triadic Analysis, using an object of class pta.

Usage

## 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"), ...)

Arguments

Author(s)

Daniel Chessel

Examples

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)

Multiple Graphs for Separated Analyses in a K-tables

Description

performs high level plots for Separed Analyses in a K-tables, using an object of class sepan.

Usage

## 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"), ...)

Arguments

Details

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.

Author(s)

Daniel Chessel

Examples

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)
}    

Multiple Graphs of a STATIS Analysis

Description

performs high level plots for a STATIS analysis, using an object of class statis.

Usage

## 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",...)

Arguments

Author(s)

Daniel Chessel

Examples

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)
}

Class of the Permutation Tests (in C).

Description

Plot, print and extract permutation tests. Objects of class 'krandtest' are lists.

Usage

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]]

Arguments

Value

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.

Author(s)

Daniel Chessel and Stéphane Dray stephane.dray@univ-lyon1.fr

See Also

randtest

Examples

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]]

the class of objects 'ktab' (K-tables)

Description

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 :

blo

: the vector of the numbers of columns for each table

lw

: the vector of the row weightings in common for all tables

cw

: the vector of the column weightings

TL

: a data frame of two components to manage the parameter positions associated with the rows of tables

TC

: a data frame of two components to manage the parameter positions associated with the columns of tables

T4

: a data frame of two components to manage the parameter positions of 4 components associated to an array

Usage

## 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)

Arguments

Details

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 ktabs : ktab.match2ktabs

Value

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.

Author(s)

Daniel Chessel
Anne-Béatrice Dufour anne-beatrice.dufour@univ-lyon1.fr Stéphane Dray stephane.dray@univ-lyon1.fr

Examples

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)
}

Creation of K-tables from a data frame

Description

creates K tables from a data frame.

Usage

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)))

Arguments

Value

returns a list of class ktab. See ktab.

Author(s)

Daniel Chessel
Anne-Béatrice Dufour anne-beatrice.dufour@univ-lyon1.fr

Examples

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))

Creating a K-tables from a list of data frames.

Description

creates a list of class ktab from a list of data frames

Usage

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))))

Arguments

Details

Each element of the initial list have to possess the same names and row numbers

Value

returns a list of class ktab. See ktab

Author(s)

Daniel Chessel
Anne-Béatrice Dufour anne-beatrice.dufour@univ-lyon1.fr

Examples

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)

Creation of a K-tables from a list of duality diagrams

Description

creates a list of class ktab from a list of duality diagrams.

Usage

ktab.list.dudi(obj, rownames = NULL, colnames = NULL, tabnames = NULL)

Arguments

Value

returns a list of class ktab. See ktab

Author(s)

Daniel Chessel
Anne-Béatrice Dufour anne-beatrice.dufour@univ-lyon1.fr

Examples

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)

STATIS and Co-Inertia : Analysis of a series of paired ecological tables

Description

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.

Usage

ktab.match2ktabs(KTX, KTY)

Arguments

Value

a list of class ktab, subclass kcoinertia. See ktab

WARNING

IMPORTANT : KTX and KTY must have the same k-tables structure, the same number of columns, and the same column weights.

Author(s)

Jean Thioulouse Jean.Thioulouse@univ-lyon1.fr

References

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.

Examples

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)

Process to go from a Within Analysis to a K-tables

Description

performs the process to go from a Within Analysis to a K-tables.

Usage

ktab.within(dudiwit, rownames = NULL, colnames = NULL, tabnames = NULL)

Arguments