Function reference

One-table analysis

These functions are designed for the analysis of a single table.

Methods

Functions to run the analysis

dudi.acm() acm.burt() acm.disjonctif() boxplot(<acm>): Multiple Correspondence Analysis

dudi.coa(): Correspondence Analysis

dudi.dec(): Decentred Correspondence Analysis

prep.fuzzy.var() dudi.fca() dudi.fpca(): Fuzzy Correspondence Analysis and Fuzzy Principal Components Analysis

dudi.hillsmith(): Ordination of Tables mixing quantitative variables and factors

dudi.mix(): Ordination of Tables mixing quantitative variables and factors

dudi.nsc(): Non symmetric correspondence analysis

dudi.pca(): Principal Component Analysis

dudi.pco() scatter(<pco>): Principal Coordinates Analysis

pcoscaled(): Simplified Analysis in Principal Coordinates

nipals() scatter(<nipals>) print(<nipals>): Non-linear Iterative Partial Least Squares (NIPALS) algorithm

Graphics

Functions to display the results of the analysis

score(<coa>) reciprocal.coa(): Reciprocal scaling after a correspondence analysis

scatter() biplot(<dudi>) screeplot(<dudi>): Graphical representation of the outputs of a multivariate analysis

scatter(<acm>): Plot of the factorial maps in a Multiple Correspondence Analysis

scatter(<coa>): Plot of the factorial maps for a correspondence analysis

scatter(<dudi>): Plot of the Factorial Maps

scatter(<fca>): Plot of the factorial maps for a fuzzy correspondence analysis

score() scoreutil.base(): Graphs for One Dimension

score(<acm>): Graphs to study one factor in a Multiple Correspondence Analysis

score(<mix>): Graphs to Analyse a factor in a Mixed Analysis

score(<pca>): Graphs to Analyse a factor in PCA

Two-table analysis

These functions are designed for the analysis of a pair of tables.

Methods

Functions to run the analysis

coinertia() plot(<coinertia>) print(<coinertia>) summary(<coinertia>): Coinertia Analysis

pcaiv() plot(<pcaiv>) print(<pcaiv>) summary(<pcaiv>): Principal component analysis with respect to instrumental variables

pcaivortho() summary(<pcaivortho>): Principal Component Analysis with respect to orthogonal instrumental variables

procuste() plot(<procuste>) print(<procuste>) randtest(<procuste>): Simple Procruste Rotation between two sets of points

niche() print(<niche>) plot(<niche>) niche.param() rtest(<niche>): Method to Analyse a pair of tables : Environmental and Faunistic Data

varipart() print(<varipart>): Partition of the variation of a response multivariate table by 2 explanatory tables

Tests

Functions to evaluate the significance of observed structures

procuste.randtest(): Monte-Carlo Test on the sum of the singular values of a procustean rotation (in C).

procuste.rtest(): Monte-Carlo Test on the sum of the singular values of a procustean rotation (in R).

randtest(<coinertia>): Monte-Carlo test on a Co-inertia analysis (in C).

randtest(<pcaiv>) randtest(<pcaivortho>): Monte-Carlo Test on the percentage of explained (i.e. constrained) inertia

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

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

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

Three-table methods

3-tables methods.

combine.randtest.rlq() combine.4thcorner() p.adjust.4thcorner(): Functions to combine and adjust the outputs 3-table methods

fourthcorner() fourthcorner2() print(<4thcorner>) summary(<4thcorner>) plot(<4thcorner>) fourthcorner.rlq(): Functions to compute the fourth-corner statistic

rlq() print(<rlq>) plot(<rlq>) summary(<rlq>) randtest(<rlq>): RLQ analysis

Analysis including categorical variables (`factor`)

These functions allows to consider a partition of individuals to focus on or remove differences between groups.

Methods

bca(<dudi>): Between-Class Analysis

bca(<coinertia>): Between-class coinertia analysis

bca(<rlq>) plot(<betrlq>) print(<betrlq>): Between-Class RLQ analysis

bwca.dpcoa() bca(<dpcoa>) wca(<dpcoa>) randtest(<betwit>) summary(<betwit>) print(<witdpcoa>) print(<betdpcoa>): Between- and within-class double principal coordinate analysis

discrimin() plot(<discrimin>) print(<discrimin>): Linear Discriminant Analysis (descriptive statistic)

discrimin.coa(): Discriminant Correspondence Analysis

wca(<dudi>): Within-Class Analysis

wca(<coinertia>): Within-class coinertia analysis

wca(<rlq>) plot(<witrlq>) print(<witrlq>): Within-Class RLQ analysis

withinpca(): Normed within principal component analysis

witwit.coa() summary(<witwit>) witwitsepan(): Internal Correspondence Analysis

Additional tools

loocv(<between>) print(<bcaloocv>) plot(<bcaloocv>): Leave-one-out cross-validation for a bca

loocv(<discrimin>) print(<discloocv>) plot(<discloocv>): Leave-one-out cross-validation for a discrimin analysis

randtest(<between>): Monte-Carlo Test on the between-groups inertia percentage (in C).

randtest(<discrimin>): Monte-Carlo Test on a Discriminant Analysis (in C).

rtest(<between>): Monte-Carlo Test on the between-groups inertia percentage (in R).

rtest(<discrimin>): Monte-Carlo Test on a Discriminant Analysis (in R).

Management of `dudi` objects

These functions provides functionalities to manage objects of the class dudi (they can be used on the outputs od several methods to analyze one or two tables for instance).

as.dudi() print(<dudi>) is.dudi() redo.dudi() t(<dudi>) summary(<dudi>) `[`(<dudi>): Duality Diagram

scatter() biplot(<dudi>) screeplot(<dudi>): Graphical representation of the outputs of a multivariate analysis

dist.dudi(): Computation of the Distance Matrix from a Statistical Triplet

inertia(<dudi>) print(<inertia>) summary(<inertia>): Decomposition of inertia (i.e. contributions) in multivariate methods

loocv(<dudi>): Leave-one-out cross-validation for a dudi

reconst(): Reconstitution of Data from a Duality Diagram

supcol(): Projections of Supplementary Columns

suprow(<coa>) suprow(<dudi>) predict(<dudi>) suprow(<pca>) suprow(<acm>) suprow(<mix>) suprow(<fca>): Projections of Supplementary Rows

testdim(): Function to perform a test of dimensionality

K-table analysis

Functions designed to manage and analyze data structured in several tables (a.k.a. multitable analysis)

Methods

Functions to run the analysis

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

foucart() plot(<foucart>) print(<foucart>): K-tables Correspondence Analysis with the same rows and the same columns

mdpcoa() kplotX.mdpcoa() prep.mdpcoa(): Multiple Double Principal Coordinate Analysis

mbpls(): Multiblock partial least squares

mbpcaiv(): Multiblock principal component analysis with instrumental variables

mcoa() print(<mcoa>) summary(<mcoa>) plot(<mcoa>): Multiple CO-inertia Analysis

mfa() plot(<mfa>) print(<mfa>) summary(<mfa>): Multiple Factorial Analysis

pta() plot(<pta>) print(<pta>): Partial Triadic Analysis of a K-tables

sepan() plot(<sepan>) summary(<sepan>) print(<sepan>): Separated Analyses in a K-tables

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

statis() plot(<statis>) print(<statis>): STATIS, a method for analysing K-tables

Graphics

Functions to display the results of the analysis

kplot(): Generic Function for Multiple Graphs in a K-tables Analysis

kplot(<foucart>): Multiple Graphs for the Foucart's Correspondence Analysis

kplot(<mcoa>): Multiple Graphs for a Multiple Co-inertia Analysis

kplot(<mfa>): Multiple Graphs for a Multiple Factorial Analysis

kplot(<pta>): Multiple Graphs for a Partial Triadic Analysis

kplot(<sepan>) kplotsepan.coa(): Multiple Graphs for Separated Analyses in a K-tables

kplot(<statis>): Multiple Graphs of a STATIS Analysis

mdpcoa() kplotX.mdpcoa() prep.mdpcoa(): Multiple Double Principal Coordinate Analysis

summary(<multiblock>) print(<multiblock>): Display and summarize multiblock objects

Utilities

Functions to manage objects of class ktab

c(<ktab>) `[`(<ktab>) is.ktab() t(<ktab>) row.names(<ktab>) tab.names() col.names() ktab.util.names(): the class of objects 'ktab' (K-tables)

ktab.data.frame(): Creation of K-tables from a data frame

ktab.list.df(): Creating a K-tables from a list of data frames.

ktab.list.dudi(): Creation of a K-tables from a list of duality diagrams

ktab.match2ktabs(): STATIS and Co-Inertia : Analysis of a series of paired ecological tables

ktab.within(): Process to go from a Within Analysis to a K-tables

mdpcoa() kplotX.mdpcoa() prep.mdpcoa(): Multiple Double Principal Coordinate Analysis

suprow(<pta>): Projections of Supplementary Rows for a Partial Triadic Analysis of K-tables

Tests

Functions to evaluate the significance of observed structures

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

randboot(<multiblock>): Bootstraped simulations for multiblock methods

testdim(<multiblock>): Selection of the number of dimension by two-fold cross-validation for multiblock methods

statico.krandtest(): Monte-Carlo test on a Statico analysis (in C).

Distance methods

cailliez(): Transformation to make Euclidean a distance matrix

dist.binary(): Computation of Distance Matrices for Binary Data

dist.dudi(): Computation of the Distance Matrix from a Statistical Triplet

dist.ktab() ldist.ktab() kdist.cor() prep.fuzzy() prep.binary() prep.circular(): Mixed-variables coefficient of distance

dist.neig(): Computation of the Distance Matrix associated to a Neighbouring Graph

dist.prop(): Computation of Distance Matrices of Percentage Data

dist.quant(): Computation of Distance Matrices on Quantitative Variables

as.taxo() dist.taxo(): Taxonomy

dudi.pco() scatter(<pco>): Principal Coordinates Analysis

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

kdist2ktab(): Transformation of K distance matrices (object 'kdist') into K Euclidean representations (object 'ktab')

kdisteuclid(): a way to obtain Euclidean distance matrices

mantel.randtest(): Mantel test (correlation between two distance matrices (in C).)

mantel.rtest(): Mantel test (correlation between two distance matrices (in R).)

mantelkdist() RVkdist() plot(<corkdist>): Tests of randomization between distances applied to 'kdist' objetcs

quasieuclid(): Transformation of a distance matrice to a Euclidean one

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

supdist(): Projection of additional items in a PCO analysis

table.dist(): Graph Display for Distance Matrices

is.euclid() summary(<dist>): Is a Distance Matrix Euclidean?

lingoes(): Transformation of a Distance Matrix for becoming Euclidean

mstree(): Minimal Spanning Tree

pcoscaled(): Simplified Analysis in Principal Coordinates

Graphics

Functions to draw graphics. See functions of the package adegraphics for a more flexible implementation based on lattice.

1D plot

sco.boxplot(): Representation of the link between a variable and a set of qualitative variables

sco.class(): 1D plot of a numeric score and a factor with labels

sco.distri(): Representation by mean- standard deviation of a set of weight distributions on a numeric score

sco.gauss(): Relationships between one score and qualitative variables

sco.quant(): Graph to Analyse the Relation between a Score and Quantitative Variables

sco.label(): 1D plot of a numeric score with labels

sco.match(): 1D plot of a pair of numeric scores with labels

2D plot

s.arrow(): Plot of the factorial maps for the projection of a vector basis

s.chull(): Plot of the factorial maps with polygons of contour by level of a factor

s.class(): Plot of factorial maps with representation of point classes

s.corcircle(): Plot of the factorial maps of a correlation circle

s.distri(): Plot of a frequency distribution

s.hist(): Display of a scatterplot and its two marginal histograms

s.image(): Graph of a variable using image and contour

s.kde2d(): Scatter Plot with Kernel Density Estimate

s.label(): Scatter Plot

s.logo(): Representation of an object in a graph by a picture

s.match(): Plot of Paired Coordinates

s.match.class(): Scatterplot of two sets of coordinates and a partionning into classes

s.multinom(): Graph of frequency profiles (useful for instance in genetic)

s.traject(): Trajectory Plot

s.value(): Representation of a value in a graph

Other types of plot

dotcircle(): Representation of n values on a circle

table.cont(): Plot of Contingency Tables

table.dist(): Graph Display for Distance Matrices

table.paint(): Plot of the arrays by grey levels

table.value(): Plot of the Arrays

triangle.plot() triangle.biplot(): Triangular Plotting

triangle.class(): Triangular Representation and Groups of points

Utilities

These functions are mainly designed for internal use.

add.scatter() add.scatter.eig(): Add graphics to an existing plot

scatterutil scatterutil.base scatterutil.sco scatterutil.chull scatterutil.eigen scatterutil.ellipse scatterutil.eti.circ scatterutil.eti scatterutil.grid scatterutil.legend.bw.square scatterutil.legend.square.grey scatterutil.legendgris scatterutil.scaling scatterutil.star scatterutil.sub scatterutil.convrot90: Graphical utility functions

s.logo(): Representation of an object in a graph by a picture

Diversity

Functions focusing on the measurment of diversity. See also the package adiv for more functionalities.

amova() print(<amova>): Analysis of molecular variance

apqe() print(<apqe>): Apportionment of Quadratic Entropy

disc(): Rao's dissimilarity coefficient

divc(): Rao's diversity coefficient also called quadratic entropy

divcmax(): Maximal value of Rao's diversity coefficient also called quadratic entropy

dpcoa() plot(<dpcoa>) print(<dpcoa>) summary(<dpcoa>): Double principal coordinate analysis

originality(): Originality of a species

randtest(<dpcoa>): Permutation test for double principal coordinate analysis (DPCoA)

randtest(<amova>): Permutation tests on an analysis of molecular variance (in C).

Spatial

Functions focusing on the analysis of spatial data. See also the package adespatial for more functionalities.

gridrowcol(): Complete regular grid analysis

mld() haar2level(): Multi Level Decomposition of unidimensional data

mstree(): Minimal Spanning Tree

orthobasis.neig() orthobasis.line() orthobasis.circ() orthobasis.mat() orthobasis.haar() print(<orthobasis>) plot(<orthobasis>) summary(<orthobasis>) is.orthobasis(): Orthonormal basis for orthonormal transform

neig() scores.neig() print(<neig>) summary(<neig>) nb2neig() neig2nb() neig2mat(): Neighbourhood Graphs

Phylogenetic methods

These functions allows to manage phylogenetic data. See functions of the package adephylo for a more flexible implementation based on ape and phylobase.

dotchart.phylog(): Representation of many quantitative variables in front of a phylogenetic tree

plot(<phylog>) radial.phylog() enum.phylog(): Plot phylogenies

gearymoran(): Moran's I and Geary'c randomization tests for spatial and phylogenetic autocorrelation

newick2phylog() hclust2phylog() taxo2phylog() newick2phylog.addtools(): Create phylogeny

print(<phylog>) phylog.extract() phylog.permut(): Phylogeny

symbols.phylog(): Representation of a quantitative variable in front of a phylogenetic tree

table.phylog(): Plot arrays in front of a phylogenetic tree

variance.phylog(): The phylogenetic ANOVA

PI2newick(): Import data files from Phylogenetic Independance Package

Tests

Utilities functions to manage results of randomization procedures.

as.krandboot() print(<krandboot>) as.randboot() print(<randboot>) randboot(): Bootstrap simulations

as.krandtest() plot(<krandtest>) print(<krandtest>) `[`(<krandtest>) `[[`(<krandtest>): Class of the Permutation Tests (in C).

as.krandxval() print(<krandxval>) as.randxval() print(<randxval>): Two-fold cross-validation

randtest() as.randtest() plot(<randtest>) print(<randtest>): Class of the Permutation Tests (in C).

rtest(): Class of the Permutation Tests (in R).

Misc

ade4-package ade4: The ade4 package

dudi.acm() acm.burt() acm.disjonctif() boxplot(<acm>): Multiple Correspondence Analysis

adegraphicsLoaded(): Utility function to test if the adegraphics package is loaded

bicenter.wt(): Double Weighted Centring

covwt() varwt() scalewt() meanfacwt() varfacwt() covfacwt() scalefacwt(): Compute or scale data using (weighted) means, variances and covariances (possibly for the levels of a factor)

dagnelie.test(): Dagnelie multinormality test

uniquewt.df(): Elimination of Duplicated Rows in a Array

Datasets

Datasets available in ade4. You can access them by typing r data(dataset).

abouheif.eg: Phylogenies and quantitative traits from Abouheif

acacia: Spatial pattern analysis in plant communities

aminoacyl: Codon usage

apis108: Allelic frequencies in ten honeybees populations at eight microsatellites loci

aravo: Distribution of Alpine plants in Aravo (Valloire, France)

ardeche: Fauna Table with double (row and column) partitioning

arrival: Arrivals at an intensive care unit

atlas: Small Ecological Dataset

atya: Genetic variability of Cacadors

avijons: Bird species distribution

avimedi: Fauna Table for Constrained Ordinations

aviurba: Ecological Tables Triplet

bacteria: Genomes of 43 Bacteria

banque: Table of Factors

baran95: African Estuary Fishes

bf88: Cubic Ecological Data

bordeaux: Wine Tasting

bsetal97: Ecological and Biological Traits

buech: Buech basin

butterfly: Genetics-Ecology-Environment Triple

capitales: Road Distances

carni19: Phylogeny and quantative trait of carnivora

carni70: Phylogeny and quantitative traits of carnivora

carniherbi49: Taxonomy, phylogenies and quantitative traits of carnivora and herbivora

casitas: Enzymatic polymorphism in Mus musculus

chatcat: Qualitative Weighted Variables

chats: Pair of Variables

chazeb: Charolais-Zebus

chevaine: Enzymatic polymorphism in Leuciscus cephalus

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

clementines: Fruit Production

cnc2003: Frequenting movie theaters in France in 2003

coleo: Table of Fuzzy Biological Traits

corvus: Corvus morphology

deug: Exam marks for some students

doubs: Pair of Ecological Tables

dunedata: Dune Meadow Data

ecg: Electrocardiogram data

ecomor: Ecomorphological Convergence

elec88: Electoral Data

escopage: K-tables of wine-tasting

euro123: Triangular Data

fission: Fission pattern and heritable morphological traits

friday87: Faunistic K-tables

fruits: Pair of Tables

ggtortoises: Microsatellites of Galapagos tortoises populations

granulo: Granulometric Curves

hdpg: Genetic Variation In Human Populations

houmousr: Morphometric data set

housetasks: Contingency Table

humDNAm: human mitochondrial DNA restriction data

ichtyo: Point sampling of fish community

irishdata: Geary's Irish Data

julliot: Seed dispersal

jv73: K-tables Multi-Regions

kcponds: Ponds in a nature reserve

lascaux: Genetic/Environment and types of variables

lizards: Phylogeny and quantitative traits of lizards

macaca: Landmarks

macon: Wine Tasting

macroloire: Assemblages of Macroinvertebrates in the Loire River (France)

mafragh: Phyto-Ecological Survey

maples: Phylogeny and quantitative traits of flowers

mariages: Correspondence Analysis Table

meau: Ecological Data : sites-variables, sites-species, where and when

meaudret: Ecological Data : sites-variables, sites-species, where and when

microsatt: Genetic Relationships between cattle breeds with microsatellites

mjrochet: Phylogeny and quantitative traits of teleos fishes

mollusc: Faunistic Communities and Sampling Experiment

monde84: Global State of the World in 1984

morphosport: Athletes' Morphology

newick.eg: Phylogenetic trees in Newick format

njplot: Phylogeny and trait of bacteria

olympic: Olympic Decathlon

oribatid: Oribatid mite

ours: A table of Qualitative Variables

palm: Phylogenetic and quantitative traits of amazonian palm trees

pap: Taxonomy and quantitative traits of carnivora

pcw: Distribution of of tropical trees along the Panama canal

perthi02: Contingency Table with a partition in Molecular Biology

piosphere: Plant traits response to grazing

presid2002: Results of the French presidential elections of 2002

procella: Phylogeny and quantitative traits of birds

rankrock: Ordination Table

rhizobium: Genetic structure of two nitrogen fixing bacteria influenced by geographical isolation and host specialization

rhone: Physico-Chemistry Data

rpjdl: Avifauna and Vegetation

santacatalina: Indirect Ordination

sarcelles: Array of Recapture of Rings

seconde: Students and Subjects

skulls: Morphometric Evolution

steppe: Transect in the Vegetation

syndicats: Two Questions asked on a Sample of 1000 Respondents

t3012: Average temperatures of 30 French cities

tarentaise: Mountain Avifauna

taxo.eg: Examples of taxonomy

tintoodiel: Tinto and Odiel estuary geochemistry

tithonia: Phylogeny and quantitative traits of flowers

tortues: Morphological Study of the Painted Turtle

toxicity: Homogeneous Table

trichometeo: Pair of Ecological Data

ungulates: Phylogeny and quantitative traits of ungulates.

vegtf: Vegetation in Trois-Fontaines

veuvage: Example for Centring in PCA

westafrica: Freshwater fish zoogeography in west Africa

woangers: Plant assemblages in woodlands of the conurbation of Angers (France)

worksurv: French Worker Survey (1970)

yanomama: Distance Matrices

zealand: Road distances in New-Zealand

Deprecated

ade4-deprecated: Deprecated functions in ade4

plot(<between>) print(<between>) plot(<betcoi>) print(<betcoi>) summary(<between>): Between-Class Analysis

plot(<within>) print(<within>) plot(<witcoi>) print(<witcoi>) summary(<within>): Within-Class Analysis

One-table analysis

Methods

Graphics

Two-table analysis

Methods

Tests

Three-table methods

Analysis including categorical variables (factor)

Methods

Additional tools

Management of dudi objects

K-table analysis

Methods

Graphics

Utilities

Tests

Distance methods

Graphics

1D plot

2D plot

Other types of plot

Utilities

Diversity

Spatial

Phylogenetic methods

Tests

Misc

Datasets

Deprecated

Analysis including categorical variables (`factor`)

Management of `dudi` objects