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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.

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.

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