The ade4 Package - NexTag Supports Open Source Initiatives

The ade4 Package 

February 16, 2008 

Version 1.4-5 

Date 2007/10/12 

Title Analysis of Ecological Data : Exploratory and Euclidean methods in Environmental sciences 

Author Daniel Chessel, Anne-Beatrice Dufour and Stephane Dray 

, with contributions from Jean R. Lobry, Sebastien Ollier, 

Sandrine Pavoine and Jean Thioulouse. 

Maintainer Simon Penel 

Suggests waveslim, splancs, MASS, maptools, spdep, pixmap, ape, tripack, ade4TkGUI 

Description Multivariate data analysis and graphical display. 

License GPL version 2 or newer 

URL http://pbil.univ-lyon1.fr/ADE-4, Mailing list: http://listes.univ-lyon1.fr/wws/info/adelist 

R topics documented: 

EH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 

PI2newick . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 

RV.rtest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 

RVdist.randtest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 

abouheif.eg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 

acacia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 

add.scatter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 

ade4toR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 

aminoacyl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 

amova . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 

apis108 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 

ardeche . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 

area.plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 

arrival . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 

as.taxo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 

atlas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 

1

2 R topics documented: 

atya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 

avijons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 

avimedi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 

aviurba . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 

bacteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 

banque . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 

baran95 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 

between . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 

bf88 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 

bicenter.wt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 

bordeaux . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 

bsetal97 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 

buech . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 

butterfly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 

cailliez . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 

capitales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 

carni19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 

carni70 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 

carniherbi49 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 

casitas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 

cca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 

chatcat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 

chats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 

chazeb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 

chevaine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 

clementines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 

cnc2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 

coinertia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 

coleo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 

corkdist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 

corvus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 

deug . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 

disc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 

discrimin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 

discrimin.coa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 

dist.binary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 

dist.dudi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 

dist.genet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 

dist.neig . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 

dist.prop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 

dist.quant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 

divc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 

divcmax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 

dotchart.phylog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 

dotcircle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 

doubs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 

dpcoa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 

dudi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

R topics documented: 3 

dudi.acm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 

dudi.coa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 

dudi.dec . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 

dudi.fca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 

dudi.hillsmith . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 

dudi.mix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 

dudi.nsc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 

dudi.pca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 

dudi.pco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 

dunedata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 

ecg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 

ecomor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 

elec88 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 

escopage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 

euro123 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 

fission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 

foucart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 

friday87 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 

fruits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 

fuzzygenet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 

gearymoran . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 

genet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 

ggtortoises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 

granulo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 

gridrowcol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 

hdpg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 

housetasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 

humDNAm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 

ichtyo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 

inertia.dudi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 

irishdata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 

is.euclid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 

julliot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 

jv73 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 

kcponds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 

kdist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 

kdist2ktab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 

kdisteuclid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 

kplot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 

kplot.foucart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 

kplot.mcoa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 

kplot.mfa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 

kplot.pta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 

kplot.sepan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 

kplot.statis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 

krandtest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 

ktab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 

ktab.data.frame . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139


ktab.list.df . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 

ktab.list.dudi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 

ktab.match2ktabs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 

ktab.within . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 

lascaux . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 

lingoes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 

lizards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 

macaca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 

macon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148 

mafragh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148 

mantel.randtest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 

mantel.rtest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 

maples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 

mariages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 

mcoa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 

meau . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 

meaudret . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156 

mfa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 

microsatt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 

mjrochet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160 

mld . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 

mollusc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162 

monde84 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 

morphosport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 

mstree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 

multispati . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 

multispati.randtest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 

multispati.rtest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 

neig . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 

newick.eg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 

newick2phylog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 

niche . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 

njplot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 

olympic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 

optimEH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 

oribatid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182 

originality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 

orisaved . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 

orthobasis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186 

orthogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 

ours . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 

palm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 

pap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194 

pcaiv . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 

pcaivortho . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196 

pcoscaled . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198 

perthi02 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199 

phylog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200

R topics documented: 5 

plot.phylog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202 

presid2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 

procella . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206 

procuste . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207 

procuste.randtest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 

procuste.rtest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210 

pta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 

quasieuclid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 

randEH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214 

randtest-internal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215 

randtest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216 

randtest.amova . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217 

randtest.between . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218 

randtest.coinertia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 

randtest.discrimin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 

rankrock . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 

reconst . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 

rhone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 

rlq . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224 

rpjdl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226 

rtest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227 

rtest.between . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228 

rtest.discrimin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229 

s.arrow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230 

s.chull . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231 

s.class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 

s.corcircle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 

s.distri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236 

s.hist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238 

s.image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 

s.kde2d . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 

s.label . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242 

s.logo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244 

s.match . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246 

s.multinom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247 

s.traject . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249 

s.value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 

santacatalina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 

sarcelles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254 

scalewt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255 

scatter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 256 

scatter.acm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258 

scatter.coa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258 

scatter.dudi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 

scatter.fca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261 

sco.boxplot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262 

sco.distri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263 

sco.quant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264


score . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265 

score.acm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 

score.coa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267 

score.mix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269 

score.pca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 270 

seconde . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 

sepan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 

skulls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 

statis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274 

steppe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275 

supcol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276 

suprow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 

symbols.phylog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279 

syndicats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280 

t3012 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280 

table.cont . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281 

table.dist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282 

table.paint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283 

table.phylog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284 

table.value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285 

tarentaise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 286 

taxo.eg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288 

testdim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289 

tintoodiel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 290 

tithonia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291 

tortues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292 

toxicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293 

triangle.class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294 

triangle.plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295 

trichometeo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297 

ungulates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 298 

uniquewt.df . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 

variance.phylog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 300 

vegtf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301 

veuvage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302 

westafrica . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303 

within . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305 

withinpca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307 

witwit.coa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308 

worksurv . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309 

yanomama . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310 

zealand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311 

Index 313

EH 7 

EH 

Amount of Evolutionary History 

Description 

computes the sum of branch lengths on an ultrametric phylogenetic tree. 

Usage 

EH(phyl, select = NULL) 

Arguments 

phyl 

select 

an object of class phylog 

a vector containing the numbers of the leaves (species) which must be considered 

in the computation of the amount of Evolutionary History. This parameter 

allows the calculation of the amount of Evolutionary History for a subset of 

species. 

Value 

returns a real value. 

Author(s) 

Sandrine Pavoine 〈pavoine@biomserv.univ-lyon1.fr〉 

References 

Nee, S. and May, R.M. (1997) Extinction and the loss of evolutionary history. Science, 278, 692– 

694. 

Examples 

data(carni70) 

carni70.phy

8 PI2newick 

PI2newick 

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 

x 

is a data frame that contains information on phylogeny topology and trait values 

Value 

Returns a list containing : 

tre 

trait 

: a character string giving the phylogenetic tree in Newick format 

: a vector containing values of the trait 

Author(s) 

Sébastien Ollier 〈ollier@biomserv.univ-lyon1.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

RV.rtest 9 

RV.rtest 

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 

df1, df2 

nrepet 

two data frames with the same rows 

the number of permutations 

Value 

returns a list of class ’rtest’ 

Author(s) 


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

10 abouheif.eg 

RVdist.randtest 

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 

m1, m2 two Euclidean matrices 

nrepet the number of permutations 

Value 

returns a list of class ’randtest’ 

Author(s) 


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 

abouheif.eg 

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)

acacia 11 

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. 


tre3 is a character string giving the third phylogenetic tree made up of 15 leaves. 


Source 

Data taken from the phylogenetic independence program developped by Ehab Abouheif starting 

from http://ww2.mcgill.ca/biology/faculty/abouheif/programs.html. 

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) 

acacia 

Spatial pattern analysis in plant communities 

Description 

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

12 add.scatter 

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. 


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) 

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)) 

scatterutil.sub(names(acacia)[k], 1.5, "topleft") 

} 

par(mfcol = c(1,1)) 

add.scatter 

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 graphicinside this area. The additional graphic is determined by a function which is 

the first argument taken by add.scatter. It can be used in various ways, for instance to add a 

screeplot to an ordination scatterplot (add.scatter.eig). 

The function add.scatter.eig uses the following colors: black (represented axes), grey(axes 

retained in the analysis) and white (others).

add.scatter 13 

Usage 

add.scatter(func,posi=c("bottomleft","bottomright","topleft","topright"),ratio=.2,i 

add.scatter.eig(w,nf=NULL, xax, yax, posi = "bottomleft", ratio = .25, inset = .01, 

Arguments 

func 

posi 

ratio 

inset 

bg.col 

w 

nf 

xax 

yax 

sub 

csub 

an - evaluated - function producing a graphic 

a character vector (only its first element being considered) giving the position of 

the added graph. Possible values are "bottomleft" (="bottom"),"bottomright","topleft" 

(="top"),"topright", and "none" (no plot). 

the size of the added graph in proportion of the current plot region 

the inset from which the graph is drawn, in proportion of the whole plot region. 

Can be a vector of length 2, giving the inset in x and y. If atomic, same inset is 

used in x and y 

the color of the background of the added graph 

numeric vector of eigenvalues 

the number of retained factors, NULL if not provided 

first represented axis 

second represented axis 

title of the screeplot 

size of the screeplot title 

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 〈jombart@biomserv.univ-lyon1.fr〉 

See Also 

scatter

14 ade4toR 

Examples 


f1

ade4toR 15 

Arguments 

fictab 

ficcolnames 

ficrownames 

x 

a name of ADE4 text file. A data frame with the same name is created in the R 

environment. 

the column names label file 

the row names label file 

a data frame 

Details 

"xxx" is the name of object x ((deparse(substitute(x)))) 

For any table : 

creates a file "xxx.txt" 

creates a file "xxx_row_lab.txt" with row names 

creates a file "xxx_col_lab.txt" with column names 

if x has the ’col.blocks’ attribute 

creates a file "xxx_col_bloc_lab.txt" with blocks names 

creates a file "xxx_col_bloc.txt" with blocks sizes 

For a table which all columns are factors : 

creates a file "xxx.txt" 

creates a file "xxx_var_lab.txt" with row names 

creates a file "xxx_moda_lab.txt" with categories names 

Value 

Files are created in the current working directory. 

’ade4toR’ gives data frames. 

’Rtoade4’ gives text files. 

Examples 

data(tarentaise) 

traits

16 aminoacyl 

aminoacyl 

Codon usage 

Description 

aminoacyl is a list containing the codon counts of 36 genes encoding yeast aminoacyl-tRNAsynthetase(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. 


Data prepared by D. Charif 〈charif@biomserv.univ-lyon1.fr〉 starting from: 

http://www.expasy.org/sprot/ 

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)

amova 17 

amova 

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 

samples 

distances 

structures 

x 

full 

a data frame with haplotypes (or genotypes) as rows, populations as columns 

and abundance as entries 

an object of class dist computed from Euclidean distance. If distances is 

null, equidistances are used. 

a data frame containing, in the jth row and the kth column, the name of the group 

of level k to which the jth population belongs 

an object of class amova 

a logical value indicating whether the original data (’distances’, ’samples’, ’structures’) 

should be printed 

... further arguments passed to or from other methods 

Value 

Returns a list of class amova 

call 

results 

call 

a data frame with the degrees of freedom, the sums of squares, and the mean 

squares. Rows represent levels of variability. 

componentsofcovariance 

a data frame containing the components of covariance and their contribution to 

the total covariance 

statphi 

a data frame containing the phi-statistics 

Author(s) 

Sandrine Pavoine 〈pavoine@biomserv.univ-lyon1.fr〉

18 apis108 

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

ardeche 19 

ardeche 

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). 


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

20 area.plot 

area.plot 

Graphical Display of Areas 

Description 

Usage 

’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’ (spdep package, that contains the older package ’spweights’) 

and returns a data frame of type area. 

area2poly takes an object of type ’area’ and returns a list of class ’polylist’ 

area2link takes an object of type ’area’ and returns a proximity matrix which terms are given by the 

length of the frontier between two polygons. 

area.util.contour,area.util.xy and area.util.class are three utility functions. 

area.plot(x, center = NULL, values = NULL, graph = NULL, lwdgraph = 2, 

nclasslegend = 8, clegend = 0.75, sub = "", csub = 1, 

possub = "topleft", cpoint = 0, label = NULL, clabel = 0, ...) 

area2poly(area) 

poly2area(polys) 

area2link(area) 

area.util.contour(area) 

area.util.xy(area) 

Arguments 

x 

center 

values 

graph 

lwdgraph 

a data frame with three variables 

a matrix with the same row number as x and two columns, the coordinates of 

polygone centers. If NULL, it is computed with area.util.xy 

if not NULL, a vector which values will be mapped to grey levels. The values 

must be in the same order as the values in unique(x.area[,1]) 

if not NULL, graph is a neighbouring graph (object of class "neig") between 

polygons 

a line width to draw the neighbouring graph 

nclasslegend if value not NULL, a number of classes for the legend 

clegend 

sub 

csub 

if not NULL, a character size for the legend, used with par("cex")*clegend 

a string of characters to be inserted as sub-title 

a character size for the sub-titles, used with par("cex")*csub

area.plot 21 

Value 

possub 

cpoint 

label 

clabel 

polys 

area 

a string of characters indicating the sub-titles position ("topleft", "topright", 

"bottomleft", "bottomright") 

if positive, a character size for drawing the polygons vertices (check up), used 

with par("cex")*cpoint 

if not NULL, by default the levels of the factor that define the polygons are used 

as labels. To change this value, use label. These labels must be in the same order 

than unique(x.area[,1]) 

if not NULL, a character size for the polygon labels, 

used with par("cex")*clabel 

a list belonging to the ’polylist’ class in the spdep package 

a data frame of class ’area’ 


poly2area returns a data frame ’factor,x,y’. 

area2poly returns a list of class polylist. 

Author(s) 


Examples 

data(elec88) 


area.plot(elec88$area, cpoint = 1) 

area.plot(elec88$area, lab = elec88$lab$dep, clab = 0.75) 

area.plot(elec88$area, clab = 0.75) 

# elec88$neig

22 area.plot 

} 

data(irishdata) 


w

arrival 23 

# 4 

fr.poly

24 as.taxo 

Examples 

data(arrival) 

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

as.taxo 

Taxonomy 

Description 

Usage 

The function as.taxo creates an object of class taxo that is a sub-class of data.frame. Each 

column of the data frame must be a factor corresponding to a level j of the taxonomy (genus, family, 

. . . ). The levels of factor j define some classes that must be completly included in classes of factor 

j+1. 

A factor with exactly one level is not allowed. A factor with exactly one individual in each level is 

not allowed. The function dist.taxo compute taxonomic distances. 

as.taxo(df) 

dist.taxo(taxo) 

Arguments 

df 

taxo 

a data frame 

a data frame of class taxo 

Value 

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

Author(s) 



See Also 

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

Examples 

data(taxo.eg) 

tax

atlas 25 


all(dist.taxo(tax)==tax.phy$Wdist) 

atlas 

Small Ecological Dataset 

Description 

Usage 

Format 


atlas is a list containing three kinds of information about 23 regions (The French Alps) : 

geographical coordinates, meteorology and bird presences. 

data(atlas) 

This list contains the following objects: 

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). 

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

Extract from: 

Lebreton, Ph. (1977) Les oiseaux nicheurs rhonalpins. Atlas ornithologique Rhone-Alpes. Centre 

Ornithologique Rhone-Alpes, Université Lyon 1, 69621 Villeurbanne. Direction de la Protection de 

la Nature, Ministère de la Qualité de la Vie. 1–354. 

Examples 

data(atlas) 

op

26 atya 

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

avijons 27 

Examples 

## Not run: 

data(atya) 

if (require(pixmap, quiet = TRUE)) { 

atya.digi

28 avijons 

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/pps/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 (require(pixmap,quiet=TRUE)) { 

pnm.eau

avimedi 29 

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) 


}## End(Not run) 

avimedi 

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 


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. 


Blondel, J., Chessel, D., & Frochot, B. (1988) Bird species impoverishment, niche expansion, and 

density inflation in mediterranean island habitats. Ecology, 69, 1899–1917.

30 aviurba 

Examples 

## Not run: 

data(avimedi) 


coa1

acteria 31 

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). 


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

32 banque 


Data prepared by J. Lobry 〈lobry@biomserv.univ-lyon1.fr〉 starting from: 

http://www.tigr.org/tdb/mdb/mdbcomplete.html 

Examples 

data(bacteria) 

names(bacteria$espcodon) 

names(bacteria$espaa) 

names(bacteria$espbase) 

sum(bacteria$espcodon) # 22,619,749 codons 

scatter.coa(dudi.coa(bacteria$espcodon, scann = FALSE), 

posi = "bottom") 

banque 

Table of Factors 

Description 

Usage 

Format 

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

data(banque) 

This data frame contains the following columns: 

csp : "Socio-professional categories" a factor with levels agric Farmers artis Craftsmen, 

Shopkeepers, Company directors cadsu Executives and higher intellectual professions inter 

Intermediate professions emplo Other white-collar workers ouvri Manual workers retra 

Pensionners inact Non working population etudi Students 

duree : "Time relations with the customer" a factor with levels dm2 = 12 years 

oppo : "Stopped a check ?" a factor with levels non no oui yes 

age : "Customer’s age" a factor with levels ai25 [18 years, 25 years[ ai35 [25 years, 35 years[ 

ai45 [35 years, 45 years[ ai55 [45 years, 55 years[ ai75 [55 years, 75 years[ 

sexe : "Customer’s gender" a factor with levels hom Male fem Female 

interdit : "No checkbook allowed" a factor with levels non no oui yes 

cableue : "Possess a bank card ?" a factor with levels non no oui yes 

assurvi : "Contrat of life insurance ?" a factor with levels non no\ oui yes 

soldevu : "Balance of the current accounts" a factor with levels p4 credit balance > 20000 p3 credit 

balance 12000-20000 p2 credit balance 4000-120000 p1 credit balance >0-4000 n1 debit 

balance 0-4000 n2 debit balance >4000

aran95 33 

eparlog : "Savings and loan association account amount" a factor with levels for > 20000 fai >0 

and 20000 fai >0 and 0 and 20000 

versesp : "Check deposits" a factor with levels oui yes non no 

retresp : "Cash withdrawals" a factor with levels fai < 2000 moy 2000-5000 for > 5000 

remiche : "Endorsed checks amount" a factor with levels for >10000 moy 10000-5000 fai 1-5000 

nul none 

preltre : "Treasury Department tax deductions" a factor with levels nul none fai 1000 

prelfin : "Financial institution deductions" a factor with levels nul none fai 1000 

viredeb : "Debit transfer amount" a factor with levels nul none fai 5000 

virecre : "Credit transfer amount" a factor with levels for >10000 moy 10000-5000 fai 100000 


anonymous 

Examples 

data(banque) 

banque.acm

34 baran95 

Format 



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. 

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 


Examples 

data(baran95) 

w

etween 35 


between 

Between-Class Analysis 

Description 

Usage 

Performs a particular case of a Principal Component Analysis with respect to Instrumental Variables, 

in which there is only one instrumental variable, and it is a factor. 

between(dudi, fac, scannf = TRUE, nf = 2) 

## S3 method for class 'between': 

plot(x, xax = 1, yax = 2, ...) 


print(x, ...) 

Arguments 

dudi 

fac 

scannf 

nf 

x 

Value 

xax, yax 

a duality diagram, object of class dudi from one of the functions dudi.coa, 

dudi.pca, ... 

a factor partitioning the rows of dudi$tab in classes 

a logical value indicating whether the eigenvalues bar plot should be displayed 

if scannf FALSE, a numeric value indicating the number of kept axes 

an object of class ’between’ 

the numbers of the x-axis and the y-axis 


Returns a list of subclass ’between’ of class ’dudi’ (see dudi) 

tab 

cw 

lw 

eig 

rank 

nf 

c1 

l1 

co 

li 

a data frame class-variables, array of variables means in each class 

a numeric vector of the column weigths 

a numeric vector of the group weigths 

a numeric vector with all the eigenvalues 

an integer 

an integer value indicating the number of kept axes 

a data frame with the column normed scores 

a data frame with the class normed scores 

a data frame with the column coordinates 

a data frame with the class coordinates

36 bf88 

call 

ratio 

ls 

as 

the origin 

the bewteen-class inertia percentage 

a data frame with the row coordinates 

a data frame containing the projection of inertia axes onto between axes 

Author(s) 


Anne B Dufour 〈dufour@biomserv.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

icenter.wt 37 

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. 

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

38 bsetal97 

Examples 

w

setal97 39 

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: ≤ 1, 2, > 2), oviposition period - the length of time during 

which oviposition occurred (3 modalities: ≤ 2 months, between 2 and 5 months, > 5 months), incubation 

time - the time between oviposition and hatching of the larvae (3 modalities: ≤ 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). 


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/pps/pps029.pdf (in French).

40 buech 

Examples 

data(bsetal97) 

X

utterfly 41 

Examples 

data(buech) 


s.label(buech$xy, contour = buech$contour, neig = buech$neig) 

s.value (buech$xy, buech$tab2$Suspens-buech$tab1$Suspens, 

contour = buech$contour, neig = buech$neig, csi = 3) 


butterfly 

Genetics-Ecology-Environment Triple 

Description 

Usage 

Format 


This data set contains environmental and genetics informations about 16 Euphydryas editha butterfly 

colonies studied in California and Oregon. 

data(butterfly) 

butterfly is a list with 4 components. 

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

envir is a environmental data frame of 16 sites - 4 variables. 

genet is a genetics data frame of 16 sites - 6 allele frequencies. 

contour is a data frame for background map (California map). 

McKechnie, S.W., Ehrlich, P.R. and White, R.R. (1975) Population genetics of Euphydryas butterflies. 

I. Genetic variation and the neutrality hypothesis. Genetics, 81, 571–594. 

References 

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

London. 1–215. 

Examples 

data(butterfly) 


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))

42 cailliez 

cailliez 

Transformation to make Euclidean a distance matrix 

Description 

Usage 

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

applies it. 

cailliez(distmat, print = FALSE) 

Arguments 

distmat 

print 

an object of class dist 

if TRUE, prints the eigenvalues of the matrix 

Value 

an object of class dist containing a Euclidean distance matrix. 

Author(s) 


Stéphane Dray 〈dray@biomserv.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. 

From the DistPCoa program of P. Legendre et M.J. Anderson 

http://www.fas.umontreal.ca/BIOL/Casgrain/en/labo/distpcoa.html 

Examples 

data(capitales) 

d0

capitales 43 

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 

capitales 

Road Distances 

Description 

Usage 

Format 


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



df is a data frame containing the road distances between 15 European capitals. 

xy is a data frame containing the coordinates of capitals. 

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

logo is a list of pixmap objects, each one symbolizing a capital 

http://www.euro.gouv.fr/jeunes/eurocollege/tableaucarte.htm 

Examples 

if (require(pixmap, quiet = TRUE)) { 


names(capitales$df) 

# [1] "Madrid" "Paris" "Londres" "Dublin" "Rome" 

# [6] "Bruxelles" "Amsterdam" "Berlin" "Copenhague" "Stokholm" 

#[11] "Luxembourg" "Helsinki" "Vienne" "Athenes" "Lisbonne" 

} 

index

44 carni70 

carni19 

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). 


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

carniherbi49 45 

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)). 

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: 

data(carni70) 

carni70.phy

46 casitas 

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). 


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) 


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

cca 47 

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) 


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). 

http://www.univ-montp2.fr/~genetix/genetix/genetix.htm 

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) 

casitas.pop

48 cca 

Arguments 

sitspe 

sitenv 

scannf 

nf 

a data frame for correspondence analysis, typically a sites x species table 

a data frame containing variables, typically a sites x environmental variables 

table 


if scannf FALSE, an integer indicating the number of kept axes 

Value 

returns an object of class pcaiv. See pcaiv 

Author(s) 



References 

Ter Braak, C. J. F. (1986) Canonical correspondence analysis : a new eigenvector technique for 

multivariate direct gradient analysis. Ecology, 67, 1167–1179. 

Ter Braak, C. J. F. (1987) The analysis of vegetation-environment relationships by canonical correspondence 

analysis. Vegetatio, 69, 69–77. 

Chessel, D., Lebreton J. D. and Yoccoz N. (1987) Propriétés de l’analyse canonique des correspondances. 

Une utilisation en hydrobiologie. Revue de Statistique Appliquée, 35, 55–72. 

See Also 

cca in the package vegan 

Examples 

data(rpjdl) 

millog

chatcat 49 

s.match(iv1$ls, iv1$li, 2, 1, clab = 0.5) 

# analysis with fa - l1 - co -cor 

# canonical weights giving unit variance combinations 

s.arrow(iv1$fa) 

# sites position by environmental variables combinations 

# position of species by averaging 

s.label(iv1$l1, 2, 1, clab = 0, cpoi = 1.5) 

s.label(iv1$co, 2, 1, add.plot = TRUE) 

s.distri(iv1$l1, rpjdl$fau, 2, 1, cell = 0, csta = 0.33) 

s.label(iv1$co, 2, 1, clab = 0.75, add.plot = TRUE) 

# coherence between weights and correlations 


s.corcircle(iv1$cor, 2, 1) 

s.arrow(iv1$fa, 2, 1) 


chatcat 

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 

Details 

chatcat is a list of two objects : 

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

eff is a vector of numbers. 

One row of tab corresponds to one group of cats. 

The value in eff is the number of cats in this group. 


Pontier, D. (1984) Contribution à la biologie et à la génétique des populations de chats domestiques 

(Felis catus). Thèse de 3ème cycle. Université Lyon 1, p. 67.

50 chats 

Examples 

data(chatcat) 

summary(chatcat$tab) 

w

chazeb 51 

chazeb 

Charolais-Zebus 

Description 

Usage 

Format 


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

data(chazeb) 

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". 

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

Masson, Paris. p. 43 

Examples 

data(chazeb) 

plot(discrimin(dudi.pca(chazeb$tab, scan = FALSE), 

chazeb$cla, scan = FALSE)) 

chevaine 

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

52 clementines 

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. 


Examples 

data(chevaine) 

'fun.chevaine'

cnc2003 53 

Usage 

data(clementines) 

Format 

A data frame with 15 rows and 20 columns 


Tisné-Agostini, D. (1988) Description par analyse en composantes principales de l’évolution de la 

production du clémentinier en association avec 12 types de porte-greffe. Rapport technique, DEA 

Analyse et modélisation des systèmes biologiques, Université Lyon 1. 

Examples 

data(clementines) 

op

54 cnc2003 

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. 


National Center of Cinematography (CNC), september 2003 

http://www.cnc.fr/cncinfo/288/index.html 

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 55 

coinertia 

Coinertia Analysis 

Description 

Usage 

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

coinertia(dudiX, dudiY, scannf = TRUE, nf = 2) 

## S3 method for class 'coinertia': 

plot (x, xax = 1, yax = 2, ...) 


print (x, ...) 


summary (object, ...) 

Arguments 

dudiX a duality diagram providing from one of the functions dudi.coa, dudi.pca, . . . 

dudiY a duality diagram providing from one of the functions dudi.coa, dudi.pca, . . . 

scannf 

nf 

Value 



x, object an object of class ’coinertia’ 

xax, yax 



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

call 

rank 

nf 

RV 

eig 

lw 

cw 

tab 

li 

l1 

co 

call 

rank 

a numeric value indicating the number of kept axes 

a numeric value, the RV coefficient 


a numeric vector with the rows weigths (crossed array) 

a numeric vector with the columns weigths (crossed array) 

a crossed array (CA) 

Y col = CA row: coordinates 

Y col = CA row: normed scores 

X col = CA column: coordinates

56 coinertia 

c1 

lX 

mX 

lY 

mY 

aX 

aY 

X col = CA column: normed scores 

the row coordinates (X) 

the normed row scores (X) 

the row coordinates (Y) 

the normed row scores (Y) 

the axis onto co-inertia axis (X) 

the axis onto co-inertia axis (Y) 

WARNING 

IMPORTANT : dudi1 and dudi2 must have identical row weights. 

Author(s) 



References 

Dolédec, S. and Chessel, D. (1994) Co-inertia analysis: an alternative method for studying speciesenvironment 

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

coleo 57 

coleo 

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. 


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

58 corkdist 

corkdist 

Tests of randomization between distances applied to ’kdist’ objetcs 

Description 

Usage 

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

RV.rtest functions. 

mantelkdist (kd, nrepet = 999) 

RVkdist (kd, nrepet = 999) 

## S3 method for class 'corkdist': 

plot(x, whichinrow = NULL, whichincol = NULL, 

gap = 4, nclass = 10, coeff = 1,...) 

Arguments 

kd 

nrepet 

x 

whichinrow 

whichincol 

gap 

Details 

Value 

nclass 

coeff 

a list of class kdist 


an objet of class corkdist, coming from RVkdist or mantelkdist 

a vector of integers to select the graphs in rows (if NULL all the graphs are 

computed) 

a vector of integers to select the graphs in columns (if NULL all the graphs are 

computed) 

an integer to determinate the space between two graphs 

a number of intervals for the histogram 

an integer to fit the magnitude of the graph 


The corkdist class has some generic functions print, plot and summary. The plot shows 

bivariate scatterplots between semi-matrices of distances or histograms of simulated values with an 

error position. 

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

tests). 

Author(s) 


Stéphane Dray 〈dray@biomserv.univ-lyon1.fr〉

corvus 59 

Examples 

data(friday87) 

fri.w

60 deug 

Examples 

data(corvus) 

plot(corvus[,1:2]) 

s.class(corvus[,1:2], corvus[,4]:corvus[,3], add.p = TRUE) 

deug 

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. 


University of Lyon 1 

Examples 

data(deug) 

# decentred PCA 

pca1

disc 61 

disc 

Rao’s dissimilarity coefficient 

Description 

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

Usage 

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

Arguments 

samples 

dis 

structures 

a data frame with elements as rows, samples as columns, and abundance, presenceabsence 

or frequencies as entries 

an object of class dist containing distances or dissimilarities among elements. 

If dis is NULL, equidistances are used. 

a data frame containing, in the jth row and the kth column, the name of the group 

of level k to which the jth population belongs. 

Value 

Returns a list of objects of class dist 

Author(s) 


References 

Rao, C.R. (1982) Diversity and dissimilarity coefficients: a unified approach. Theoretical Population 

Biology, 21, 24–43. 

Examples 

data(humDNAm) 

humDNA.dist

62 discrimin 

discrimin 

Linear Discriminant Analysis (descriptive statistic) 

Description 

Usage 

performs a linear discriminant analysis. 

discrimin(dudi, fac, scannf = TRUE, nf = 2) 

## S3 method for class 'discrimin': 

plot(x, xax = 1, yax = 2, ...) 

## S3 method for class 'discrimin': 

print(x, ...) 

Arguments 

dudi 

fac 

scannf 

nf 

x 

Value 

xax 

yax 

a duality diagram, object of class dudi 

a factor defining the classes of discriminant analysis 



an object of class ’discrimin’ 

the column number of the x-axis 

the column number of the y-axis 


returns a list of class ’discrimin’ containing : 

nf 

eig 

fa 

li 

va 

cp 

gc 



a matrix with the loadings: the canonical weights 

a data frame which gives the canonical scores 

a matrix which gives the cosines between the variables and the canonical scores 

a matrix which gives the cosines between the components and the canonical 

scores 

a data frame which gives the class scores 

Author(s) 


Anne B Dufour 〈dufour@biomserv.univ-lyon1.fr〉

discrimin.coa 63 

See Also 

lda in package MASS 

Examples 

data(chazeb) 

dis1

64 dist.binary 

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)) 

dist.binary 

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 

df 

method 

diag 

upper 

a data frame with positive or zero values. Used with as.matrix(1 * (df 

> 0)) 

an integer between 1 and 10 . If NULL the choice is made with a console 

message. See details 

a logical value indicating whether the diagonal of the distance matrix should be 

printed by ‘print.dist’ 

a logical value indicating whether the upper triangle of the distance matrix 

should be printed by ‘print.dist’ 

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 = √ 1 − s with s a similarity coefficient. 

1 = Jaccard index (1901) S3 coefficient of Gower & Legendre s 1 = a 

a+b+c 

2 = Sockal & Michener index (1958) S4 coefficient of Gower & Legendre s 2 = a+d 

a+b+c+d 

3 = Sockal & Sneath(1963) S5 coefficient of Gower & Legendre s 3 = 

a 

a+2(b+c) 

4 = Rogers & Tanimoto (1960) S6 coefficient of Gower & Legendre s 4 = 

a+d 

(a+2(b+c)+d)

dist.dudi 65 

5 = Czekanowski (1913) or Sorensen (1948) S7 coefficient of Gower & Legendre s 5 = 2a 

2a+b+c 

6 = S9 index of Gower & Legendre (1986) s 6 = a−(b+c)+d 

a+b+c+d 

a 

7 = Ochiai (1957) S12 coefficient of Gower & Legendre s 7 = √ 

(a+b)(a+c) 

ad 

8 = Sockal & Sneath (1963) S13 coefficient of Gower & Legendre s 8 = √ 

(a+b)(a+c)(d+b)(d+c) 

ad−bc 

9 = Phi of Pearson S14 coefficient of Gower & Legendre s 9 = √ 

(a+b)(a+c)(b+d)(d+c) 

10 = S2 coefficient of Gower & Legendre s 1 = 

a 

a+b+c+d 

Value 

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

Author(s) 



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

66 dist.genet 

Value 


Author(s) 



Examples 

data (meaudret) 

pca1

dist.genet 67 

Let P the table of general term p k ij 

p + ij = ∑ m(j) 

k=1 pk ij = 1, p+ i+ = ∑ ν 

j=1 p+ ij = ν, p+ ++ = ∑ ν 

j=1 p+ i+ = tν 

The option method computes the distance matrices between populations using the frequencies p k ij . 

Value 

1. Nei’s distance: 

D 1 (a, b) = − ln( 

√ ∑ν 

k=1 

∑ ν 

∑ m(k) 

k=1 j=1 pk aj pk bj 

∑ ) 

m(k) 

j=1 (pk bj )2 

∑ m(k) 

j=1 (pk aj )2 √ ∑ν 

k=1 

2. Angular distance √ or Edwards’ distance: 

D 2 (a, b) = 

1 − 1 ν 

∑ ν 

k=1 

∑ m(k) 

j=1 

√ 

p k aj pk bj 

3. Coancestrality coefficient or Reynolds’ distance: 

D 3 (a, b) = 

√ ∑ν ∑ m(k) 

∑ k=1 j=1 (pk aj −pk bj )2 

ν 

2 (1−∑ m(k) 

k=1 j=1 pk aj pk bj ) 

4. Classical Euclidean distance or Rogers’ distance: 

D 4 (a, b) = 1 ∑ ν ∑ m(k) 

ν k=1 j=1 (pk aj − pk bj )2 

√ 

1 

2 

5. Absolute genetics distance or Provesti ’s distance: 

D 5 (a, b) = 1 ∑ ν ∑ m(k) 

2ν k=1 j=1 |pk aj − pk bj | 

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

Author(s) 



References 

To complete informations about distances: 

Distance 1: 

Nei, M. (1972) Genetic distances between populations. American Naturalist, 106, 283–292. 

Nei M. (1978) Estimation of average heterozygosity and genetic distance from a small number of 

individuals. Genetics, 23, 341–369. 

Avise, J. C. (1994) Molecular markers, natural history and evolution. Chapman & Hall, London. 

Distance 2: 

Edwards, A.W.F. (1971) Distance between populations on the basis of gene frequencies. Biometrics, 

27, 873–881. 

Cavalli-Sforza L.L. and Edwards A.W.F. (1967) Phylogenetic analysis: models and estimation procedures. 

Evolution, 32, 550–570.

68 dist.neig 

Hartl, D.L. and Clark, A.G. (1989) Principles of population genetics. Sinauer Associates, Sunderland, 

Massachussetts (p. 303). 

Distance 3: 

Reynolds, J. B., B. S. Weir, and C. C. Cockerham. (1983) Estimation of the coancestry coefficient: 

basis for a short-term genetic distance. Genetics, 105, 767–779. 

Distance 4: 

Rogers, J.S. (1972) Measures of genetic similarity and genetic distances. Studies in Genetics, Univ. 

Texas Publ., 7213, 145–153. 

Avise, J. C. (1994) Molecular markers, natural history and evolution. Chapman & Hall, London. 

Distance 5: 

Prevosti A. (1974) La distancia genética entre poblaciones. Miscellanea Alcobé, 68, 109–118. 

Prevosti A., Ocaña J. and Alonso G. (1975) Distances between populations of Drosophila subobscura, 

based on chromosome arrangements frequencies. Theoretical and Applied Genetics, 45, 

231–241. 

To find some useful explanations: 

Sanchez-Mazas A. (2003) Cours de Génétique Moléculaire des Populations. Cours VIII Distances 

génétiques - Représentation des populations. 

http://anthro.unige.ch/GMDP/Alicia/GMDP_dist.htm 

Examples 

data(casitas) 

casi.genet

dist.prop 69 

Value 

returns a distance matrix, object of class dist 

Author(s) 



Examples 

data(elec88) 

d0

70 dist.quant 

4 = Nei 1972 (one locus) d 4 = ln 

∑ K 

√ i=1 piqi 

∑K 

√ 

∑K 

i=1 p2 i i=1 q2 i 

5 = Edwards 1971 (one locus) d 5 = 

√ 

1 − ∑ K 

i=1 

√ 

p1 q i 

Value 

returns a distance matrix, object of class dist 

Author(s) 



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

dist.quant 71 

Arguments 

df 

method 

diag 

upper 

tol 

a data frame containing only quantitative variables 

an integer between 1 and 3. If NULL the choice is made with a console message. 

See details 

a logical value indicating whether the diagonal of the distance matrix should be 

printed by ‘print.dist’ 

a logical value indicating whether the upper triangle of the distance matrix 

should be printed by ‘print.dist’ 

used in case 3 of method as a tolerance threshold for null eigenvalues 

Details 

All the distances are of type d = ‖x − y‖ A = √ (x − y) t A(x − y) 

1 = Canonical A = Identity 

2 = Joreskog A = 

1 

diag(cov) 

3 = Mahalanobis A = inv(cov) 

Value 


Author(s) 



Examples 

data(ecomor) 


scatter(dudi.pco(dist.quant(ecomor$morpho,3), scan = FALSE)) 


scatter(dudi.pco(dist(scalewt(ecomor$morpho)), scan = FALSE)) 


par(mfrow = c(1,1))

72 divc 

divc 

Rao’s diversity coefficient also called quadratic entropy 

Description 

Calculates Rao’s diversity coefficient within samples. 

Usage 

divc(df, dis, scale) 

Arguments 

df 

dis 

scale 

a data frame with elements as rows, samples as columns, and abundance, presenceabsence 

or frequencies as entries 


If dis is NULL, Gini-Simpson index is performed. 

a logical value indicating whether or not the diversity coefficient should be 

scaled by its maximal value over all frequency distributions. 

Value 

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

Author(s) 


References 


Biology, 21, 24–43. 

Gini, C. (1912) Variabilitá e mutabilitá. 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

divcmax 73 

divcmax 

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

Description 

Usage 

For a given dissimilarity matrix, this function calculates the maximal value of Rao’s diversity coefficient 

over all frequency distribution. It uses an optimization technique based on Rosen’s projection 

gradient algorithm and is verified using the Kuhn-Tucker conditions. 

divcmax(dis, epsilon, comment) 

Arguments 

dis 

epsilon 

comment 


a tolerance threshold : a frequency is non null if it is higher than epsilon. 

a logical value indicating whether or not comments on the optimization technique 

should be printed. 

Value 

Returns a list 

value 

vectors 

the maximal value of Rao’s diversity coefficient. 

a data frame containing four frequency distributions : sim is a simple distribution 

which is equal to 

D1 

1 t D1 , pro is equal to z 

1 t z1 

, where z is the nonnegative 

eigenvector of the matrix containing the squared dissimilarities among the elements, 

met is equal to z 2 , num is a frequency vector maximizing Rao’s diversity 

coefficient. 

Author(s) 

Stéphane Champely 〈Stephane.Champely@univ-lyon1.fr〉 


References 


Biology, 21, 24–43. 

Gini, C. (1912) Variabilitá e mutabilitá. 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.

74 dotchart.phylog 

Examples 

par.safe

dotchart.phylog 75 

Usage 

dotchart.phylog(phylog, values, y = NULL, scaling = TRUE, ranging = TRUE, yranging 

joining = TRUE, yjoining = NULL, ceti = 1, cdot = 1, csub = 1, 

f.phylog = 1/(1 + ncol(values)), ...) 

Arguments 

phylog 

values 

y 

scaling 

ranging 

yranging 

joining 


a vector or a data frame giving the variables 

a vector which values correspond to leaves positions 

if TRUE, data are scaled 

if TRUE, dotplots are drawn with the same horizontal limits 

a vector with two values giving the horizontal limits. If NULL, horizontal limits 

are defined by lower and upper values of data 

if TRUE, segments join each point to a central value 

yjoining a vector with the central value. If NULL, the central value equals 0 

ceti 

cdot 

csub 

f.phylog 

a character size for editing horizontal limits, 

used with par("cex")*ceti 

a character size for plotting the points of the dot plot, used with par("cex")*cdot 

a character size for editing the names of variables, 

used with par("cex")*csub 

a size coefficient for tree size (a parameter to draw the tree in proportion to 

leaves labels) 


Author(s) 



See Also 

symbols.phylog and table.phylog 

Examples 

# one variable 

tre

76 dotcircle 


# many variables 

data(mjrochet) 

phy

doubs 77 

doubs 

Pair of Ecological Tables 

Description 

Usage 

Format 

Details 


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

data(doubs) 

doubs is a list with 3 components. 

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

poi 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. 

The rows of doubs$mil, doubs$poi and doubs$xy are 30 sites along the Doubs, a French 

and Switzerland river. 

doubs$mil contains the following variables: das - distance to the source (km * 10), alt - altitude 

(m), pen (ln(x + 1) where x is the slope (per mil * 100), deb - minimum average debit (m3/s * 

100), pH (* 10), dur - 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), 

dbo - biological demand for oxygen (mg/l * 10). 

doubs$poi contains the abundance of the following fish species: Cottus gobio (CHA), Salmo 

trutta fario (TRU), Phoxinus phoxinus (VAI), Nemacheilus barbatulus (LOC), Thymallus thymallus 

(OMB), Telestes soufia agassizi (BLA), Chondrostoma nasus (HOT), Chondostroma toxostoma 

(TOX), Leuciscus leuciscus (VAN), Leuciscus cephalus cephalus (CHE), Barbus barbus (BAR), 

Spirlinus bipunctatus (SPI), Gobio gobio (GOU), Esox lucius (BRO), Perca fluviatilis (PER), Rhodeus 

amarus (BOU), Lepomis gibbosus (PSO), Scardinius erythrophtalmus (ROT), Cyprinus carpio 

(CAR), Tinca tinca (TAN), Abramis brama (BCO), Ictalurus melas (PCH), Acerina cernua (GRE), 

Rutilus rutilus (GAR), Blicca bjoerkna (BBO), Alburnus alburnus (ABL), Anguilla anguilla (ANG). 

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/articles/ 

arti049.pdf. 

Chesse, 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.

78 dpcoa 

Examples 

data(doubs) 

pca1

dpcoa 79 

Value 

option 

csize 

the function plot.dpcoa produces four graphs, option allows us to choose 

only some of them 

a size coefficient for symbols 

... ... further arguments passed to or from other methods 

Returns a list of class dpcoa containing: 

call 

nf 

w1 

w2 

eig 

RaoDiv 

RaoDis 

RaoDecodiv 

l1 

l2 

c1 

call 


a numeric vector containing the weights of the elements 

a numeric vector containing the weights of the samples 


a numeric vector containing diversities within samples 

an object of class dist containing the dissimilarities between samples 

a data frame with the decomposition of the diversity 

a data frame with the coordinates of the elements 

a data frame with the coordinates of the samples 

a data frame with the scores of the principal axes of the elements 

Author(s) 




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

80 dudi 

dudi 

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) 


t(x) 

Arguments 

df 

col.w 

row.w 

scannf 

nf 

call 

type 

tol 

full 

a data frame with n rows and p columns 

a numeric vector containing the row weights 

a numeric vector containing the column weights 



generally match.call() 

a string of characters : the returned list will be of class c(type, "dudi") 

a tolerance threshold for null eigenvalues (a value less than tol times the first one 

is considered as null) 

a logical value indicating whether all non null eigenvalues should be kept 

x, dudi objects of class dudi 


newnf 

an integer indicating the number of kept axes

dudi.acm 81 

Value 

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

tab 

cw 

lw 

eig 

nf 

c1 

l1 

co 

li 

call 

a data frame with n rows and p columns 

column weights, a vector with n components 

row (lines) weights, a vector with p components 

eigenvalues, a vector with min(n,p) components 

integer, number of kept axes 

principal axes, data frame with p rows and nf columns 

principal components, data frame with n rows and nf columns 

column coordinates, data frame with p rows and nf columns 

row coordinates, data frame with n rows and nf columns 

original call 

Author(s) 



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

82 dudi.acm 

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 

df, df1, df2 data frames containing only factors 

row.w, counts 

vector of row weights, by default, uniform weighting 

scannf 

nf 

x 

xax 



an object of class acm 

the number of factor to display 


Value 

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

cr 

a data frame which rows are the variables, columns are the kept scores and the 

values are the correlation ratios 

Author(s) 



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

dudi.coa 83 

Examples 

data(ours) 

summary(ours) 

boxplot(dudi.acm(ours, scan = FALSE)) 

## Not run: 

data(banque) 

banque.acm

84 dudi.dec 

Value 

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

N 

the sum of all the values of the initial table 

Author(s) 



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.fca 85 

Arguments 

df 

eff 

scannf 

nf 

a data frame containing positive or null values 

a vector containing the reference distribution. Its length is equal to the number 

of rows of df 



Value 

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

R 

sum of all the values of the initial table 

Author(s) 



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

86 dudi.fca 

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 

df 

col.blocks 

row.w 

scannf 

nf 


a vector containing the number of categories for each fuzzy variable 

a vector of row weights 



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 

cr 

cent 

norm 

blo 

indica 

FST 

inertia 

a data frame which rows are the blocs, columns are the kept axes, and values are 

the correlation ratios. 

normal-bracket41bracket-normal 

Author(s) 



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

dudi.hillsmith 87 

data(bsetal97) 

w

88 dudi.mix 

Value 

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

index 

assign 

cr 

Author(s) 

a factor giving the type of each variable : f = factor, q = quantitative 

a factor indicating the initial variable for each column of the transformed table 

a data frame giving for each variable and each score: 

the squared correlation coefficients if it is a quantitative variable 

the correlation ratios if it is a factor 

Stephane Dray 〈dray@biomserv.univ-lyon1.fr〉 


References 

Hill, M. O., and A. J. E. Smith. 1976. Principal component analysis of taxonomic data with multistate 

discrete characters. Taxon, 25, 249-255. 

See Also 

dudi.mix 

Examples 

data(dunedata) 

attributes(dunedata$envir$use)$class

dudi.mix 89 

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 

index 

assign 

cr 

a factor giving the type of each variable : f = factor, o = ordered, q = quantitative 

a factor indicating the initial variable for each column of the transformed table 

a data frame giving for each variable and each score: 

the squared correlation coefficients if it is a quantitative variable 

the correlation ratios if it is a factor 

the squared multiple correlation coefficients if it is ordered 

Author(s) 



References 

Hill, M. O., and A. J. E. Smith. 1976. Principal component analysis of taxonomic data with multistate 

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

90 dudi.nsc 

dudi.nsc 

Non symmetric correspondence analysis 

Description 

performs a non symmetric correspondence analysis. 

Usage 

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

Arguments 

df 

scannf 

nf 




Value 

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

N 

sum of the values of the initial table 

Author(s) 



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.pca 91 

dudi.pca 

Principal Component Analysis 

Description 

Usage 

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

objects of class pca and dudi. 

dudi.pca(df, row.w = rep(1, nrow(df))/nrow(df), 

col.w = rep(1, ncol(df)), center = TRUE, scale = TRUE, 

scannf = TRUE, nf = 2) 

Arguments 

df 

row.w 

col.w 

center 

scale 

scannf 

nf 

a data frame with n rows (individuals) and p columns (numeric variables) 

an optional row weights (by default, uniform row weights) 

an optional column weights (by default, unit column weights) 

a logical or numeric value, centring option 

if TRUE, centring by the mean 

if FALSE no centring 

if a numeric vector, its length must be equal to the number of columns of the 

data frame df and gives the decentring 

a logical value indicating whether the column vectors should be normed for the 

row.w weighting 

a logical value indicating whether the screeplot should be displayed 


Value 

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

the principal component analysis : 

tab 

cw 

lw 

eig 

rank 

nf 

c1 

l1 

the data frame to be analyzed depending of the transformation arguments (center 

and scale) 

the column weights 

the row weights 

the eigenvalues 

the rank of the analyzed matrice 

the number of kept factors 

the column normed scores i.e. the principal axes 

the row normed scores

92 dudi.pco 

co 

li 

call 

cent 

norm 

the column coordinates 

the row coordinates i.e. the principal components 

the call function 

the p vector containing the means for variables 

the p vector containing the standard deviations for variables i.e. the root of the 

sum of squares deviations of the values from their means divided by n 

Author(s) 



See Also 

prcomp, princomp in the mva library 

Examples 

data(deug) 

deug.dudi

dudi.pco 93 

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 

d 

row.w 

scannf 

nf 

full 

tol 

x 

xax 

yax 

clab.row 

posieig 

sub 

csub 

an object of class dist containing a Euclidean distance matrix. 

an optional distance matrix row weights. If not NULL, must be a vector of 

positive numbers with length equal to the size of the distance matrix 



a logical value indicating whether all the axes should be kept 

a tolerance threshold to test whether the distance matrix is Euclidean : an eigenvalue 

is considered positive if it is larger than -tol*lambda1 where lambda1 

is the largest eigenvalue. 

an object of class pco 

the column number for the x-axis 

the column number for the y-axis 

a character size for the row labels 

if "top" the eigenvalues bar plot is upside, if "bottom" it is downside, if "none" 

no plot 

a string of characters to be inserted as legend 

a character size for the legend, used with par("cex")*csub 


Value 

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

Author(s) 



References 

Gower, J. C. (1966) Some distance properties of latent root and vector methods used in multivariate 

analysis. Biometrika, 53, 325–338.

94 dunedata 

Examples 

data(yanomama) 

gen

ecg 95 

ecg 

Electrocardiogram data 

Description 

Usage 

Format 


These data were measured during the normal sinus rhythm of a patient who occasionally experiences 

arrhythmia. There are 2048 observations measured in units of millivolts and collected at a rate of 

180 samples per second. This time series is a good candidate for a multiresolution analysis because 

its components are on different scales. For example, the large scale (low frequency) fluctuations, 

known as baseline drift, are due to the patient respiration, while the prominent short scale (high 

frequency) intermittent fluctuations between 3 and 4 seconds are evidently due to patient movement. 

Heart rhythm determines most of the remaining features in the series. The large spikes occurring 

about 0.7 seconds apart the R waves of normal heart rhythm; the smaller, but sharp peak coming 

just prior to an R wave is known as a P wave; and the broader peak that comes after a R wave is a T 

wave. 

data(ecg) 

A vector of class ts containing 2048 observations. 

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 (require(waveslim) == TRUE) { 

data(ecg) 

ecg.level

96 ecomor 

} 

## End(Not run) 

ecomor 

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. 


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.

elec88 97 

References 


Examples 

data(ecomor) 

ric

98 elec88 

Format 

elec88 is a list of 7 components. 

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

res is the global result of the election all-over the country. 

lab is a data frame with three variables: elec88$lab$dep a vector containing the names of the 

94 french departments, elec88$lab$reg a vector containing the names of the 21 French 

administraitve regions. and, elec88$lab$reg.fac a factor with 21 levels defining the 

French administraitve regions. 

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

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

neig is the neighbouring graph between departments, object of the class neig 


Public data 

See Also 

This dataset is compatible with presid2002 and cnc2003 

Examples 

data(elec88) 

apply(elec88$tab, 2, mean) 

summary(elec88$res) 


plot(elec88$area[,2:3], type = "n", asp = 1) 

lpoly

escopage 99 

escopage 

K-tables of wine-tasting 

Description 

Usage 

Format 


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

and global. 

data(escopage) 

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. 

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 


w

100 fission 

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. 

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

519. 

Université de Barcelone : http://www.ub.es/medame/nutstat1.html 

Examples 

data(euro123) 


triangle.plot(euro123$in78, addaxes = TRUE) 



triangle.biplot(euro123$in78, euro123$in97) 


fission 

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)

foucart 101 

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

102 foucart 

Value 

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

call 

nf 

rank 

blo 

cw 

lw 

eig 

tab 

li 

l1 

co 

c1 

Tli 

Tco 

TL 

TC 

origine 

axes-components saved 

rank 

useful vector 

vector: column weights 

vector: row weights 

vector: eigen values 

data.frame: modified array 

data.frame: row coordinates 

data.frame: row normed scores 

data.frame: column coordinates 

data.frame: column normed scores 

data.frame: row coordinates (each table) 

data.frame: col coordinates (each table) 

data.frame: factors for Tli 

data.frame: factors for Tco 

Author(s) 

P. Bady 〈pierre.bady@univ-lyon1.fr〉 


References 

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

Examples 

data(bf88) 

fou1

friday87 103 

friday87 

Faunistic K-tables 

Description 

Usage 

Format 


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


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. 

Friday, L.E. (1987) The diversity of macroinvertebrate and macrophyte communities in ponds, 

Freshwater Biology, 18, 87–104. 

Examples 


wfri

104 fruits 

Format 

Details 

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). 

fruits$var is a data frame of 15 variables: 

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

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

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

irform : shape irregularity (0/none - 3) 

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

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

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

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

pubesc : pubescence (0/none - 4) 

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

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

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

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

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

calibr : grade (1/200g) 


Kervella, J. (1991) Analyse de l’attrait d’un produit : exemple d’une comparaison de lots de pêches. 

Agro-Industrie et méthodes statistiques. Compte-rendu des secondes journées européennes. Nantes 

13-14 juin 1991. Association pour la Statistique et ses Utilisations, Paris, 313–325. 

Examples 

data(fruits) 


pcajug

fuzzygenet 105 

fuzzygenet 

Reading a table of genetic data (diploid individuals) 

Description 

Reads data like char2genet without a priori population 

Usage 

fuzzygenet(X) 

Arguments 

X 

a data frame of strings of characters (individuals in row, locus in variables), the 

value coded ’000000’ or two alleles of 6 characters 

Details 

Value 

In entry, a row is an individual, a variable is a locus and a value is a string of characters, for example, 

012028 for a heterozygote carying alleles 012 and 028; 020020 for a homozygote carrying two 

alleles 020 and 000000 for a not classified locus (missing data). 

In exit, a fuzzy array with the following encoding for a locus: 

0 0 1 . . . 0 for a homozygote 

0 0.5 0.5 . . . 0 for a heterozygote 

p1 p2 p3 . . . pm for an unknown where (p1 p2 p3 . . . pm) is the observed allelic frequencies for all 

tha available data. 

returns a data frame with the 6 following attributs: 

col.blocks 

all.names 

loc.names 

row.w 

col.freq 

col.num 

a vector containing the number of alleles by locus 

a vector containing the names of alleles 

a vector containing the names of locus 

a vector containing the uniform weighting of rows 

a vector containing the global allelic frequencies 

a factor ranking the alleles by locus 

Note 

In the exit data frame, the alleles are numbered 1, 2, 3, . . . by locus and the loci are called L01, L02, 

L03, . . . for the simplification of listing. The original names are kept. 

Author(s) 

Daniel Chessel

106 gearymoran 

References 

See Also 

put references to the literature/web site here 

char2genet if you have the a priori definition of the groups of individuals (populations). It may 

be used on the created object dudi.fca 

Examples 

data(casitas) 

casitas[1:5, ] 

casitas

genet 107 

Value 

Returns an object of class krandtest (randomization tests). 

Author(s) 



References 

Cliff, A. D. and Ord, J. K. (1973) Spatial autocorrelation, Pion, London. 



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

108 genet 

Description 

There are multiple formats of genetic data. The functions of ade4 associated genetic data use the 

class genet. An object of the class genet is a list containing at least one data frame whose 

lines are groups of individuals (populations) and columns alleles forming blocks associated with 

the locus. They contain allelic frequencies expressed as a percentage. 

The function char2genet ensures the reading of tables crossing diploid individuals arranged by 

groups (populations) and polymorphic loci. Data frames containing only strings of characters are 

transformed in tables of allelic frequencies of the class genet. In entry a row is an individual, a 

variable is a locus and a value is a string of characters, for example ’ 012028 ’ for a heterozygote 

carrying alleles 012 and 028, ’ 020020 ’ for a homozygote carrying two alleles 020 and ’ 000000 ’ 

for a not classified locus (missing data). 

The function count2genet reads data frames containing allelic countings by populations and 

allelic forms classified by locus. 

The function freq2genet reads data frames containing allelic frequencies by populations and 

allelic forms classified by locus. 

In these two cases, use as names of variables of strings of characters xx.yyy where xx are the 

names of locus and yyy a name of allelic forms in this locus. The analyses on this kind of data 

having to use compact labels, these functions classify the names of the populations, the names of 

the loci and the names of the allelic forms in vectors and re-code in a simple way starting with P for 

population, L for locus and 1,. . . , m for the alleles. 

Usage 

char2genet(X, pop, complete) 

count2genet(PopAllCount) 

freq2genet(PopAllFreq) 

Arguments 

X 

pop 

complete 

PopAllCount 

PopAllFreq 

a data frame of strings of characters (individuals in row, locus in variables), the 

value coded ’000000’ or two alleles of 6 characters 

a factor with the same number of rows than df classifying the individuals by 

population 

a logical value indicating a complete issue or not, by default FALSE 

a data frame containing integers: the occurrences of each allelic form (column) 

in each population (row) 

a data frame containing values between 0 and 1: the frequencies of each allelic 

form (column) in each population (row) 

Details 

As a lot of formats for genetic data are published in literature, a list of class genet contains at 

least a table of allellic frequencies and an attribut loc.blocks. The populations (row) and the 

variables (column) are classified by alphabetic order. In the component comp, each individual per 

locus of m alleles is re-coded by a vector of length m: for hererozygicy 0,. . . ,1,. . . ,1,. . . ,0 and 

homozygocy 0,. . . ,2,0.

genet 109 

Value 

char2genet returns a list of class genet with : 

$tab 

$center 

a frequencies table of poplations (row) and alleles (column) 

the global frequency of each allelic form calculated on the overall individuals 

classified on each locus 

$pop.names a vector containing the names of populations present in the data re-coded P01, 

P02, . . . 

$all.names 

$loc.blocks 

$loc.fac 

$loc.names 

$pop.loc 

$comp 

$comp.pop 

a vector containing the names of the alleles present in the data re-coded L01.1, 

L01.2, . . . 

a vector containing the number of alleles by loci 

a factor sharing the alleles by loci 

a vector containing the names of loci present in the data re-coded L01, . . . , L99 

a data frame containing the number of genus allowing the calculation of frequencies 

the complete individual typing with the code 02000 or 01001 if the option 

complete is TRUE 

a factor indicating the population if the option complete is TRUE 

count2genet and freq2genet return a list of class genet which don’t contain the components 

pop.loc and complete. 

Author(s) 


Examples 

data(casitas) 

casitas[24,] 

casitas.pop

110 ggtortoises 

casitas.coa

granulo 111 

Examples 

if(require(pixmap, quiet=TRUE)){ 

data(ggtortoises) 

a1

112 gridrowcol 

gridrowcol 

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 

nrow 

ncol 

cell.names 

size of the grid (number of rows) 

size of the grid (number of columns) 

grid cell labels 

Value 

Returns a list containing the following items : 

xy 

area 

neig 

orthobasis 

: a data frame with grid cell coordinates 

: a data frame with three variables to display grid cells as areas 

: an object of class ’neig’ corresponding to a neighbouring graph of the grid 

(rook case) 

: an object of class ’orthobasis’ corresponding to the analytical solution 

for the neighbouring graph 

Author(s) 



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

hdpg 113 

Examples 

w

114 housetasks 

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. 


Extract of data prepared by the Human Diversity Panel Genotypes 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 

## Not run: 

library(ade4) 

data(hdpg) 

freq

humDNAm 115 

Usage 


Format 

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

numeric vector. 


Kroonenberg, P. M. and Lombardo, R. (1999) Nonsymmetric correspondence analysis: a tool for 

analysing contingency tables with a dependence structure. Multivariate Behavioral Research, 34, 

367–396 

Examples 


nsc1

116 ichtyo 


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

inertia.dudi 117 

inertia.dudi 

Statistics of inertia in a one-table analysis 

Description 

Prints of the statistics of inertia in a one-table analysis 

Usage 

inertia.dudi(dudi, row.inertia = FALSE, col.inertia = FALSE) 

Arguments 

dudi 

row.inertia 

col.inertia 


if TRUE, returns the statistics of the decomposition of inertia for the rows 

if TRUE, returns the statistics of the decomposition of inertia for the columns 

Details 

Contributions are printed in 1/10000 and the sign is the sign of the coordinate 

Value 

a list containing : 

TOT 

row.abs 

row.rel 

row.cum 

col.abs 

col.rel 

col.cum 

repartition of the total inertia between axes 

absolute contributions of the decomposition of inertia for the rows 

relative contributions of the decomposition of inertia for the rows 

cumulative relative contributions of the decomposition of inertia for the rows 

absolute contributions of the decomposition of inertia for the columns 

relative contributions of the decomposition of inertia for the columns 

cumulative relative contributions of the decomposition of inertia for the columns 

Author(s) 



118 irishdata 

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 


coa1

is.euclid 119 

contour is a data frame with the global polygon of all the 25 counties. 

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

area.utm is a data frame with polygons for each of the 25 contiguous counties expressed in Universal 

Transverse Mercator (UTM) coordinates. 

xy.utm is a data frame with the UTM coordinates centers of the 25 counties. 

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

tab.utm is a data frame with the 25 counties (explicitly named) and 12 variables. 

contour.utm is a data frame with the global polygon of all the 25 counties expressed in UTM 

coordinates. 


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 



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$tab, scan = FALSE)$co) 

score

120 julliot 

Arguments 

Value 

distmat 

plot 

print 

tol 

object 

an object of class ’dist’ 

a logical value indicating whether the eigenvalues bar plot of the matrix of the 

term − 1 2 d2 ij centred by rows and columns should be diplayed 

a logical value indicating whether the eigenvalues of the matrix of the term 

− 1 2 d2 ij centred by rows and columns should be printed 

a tolerance threshold : an eigenvalue is considered positive if it is larger than 

-tol*lambda1 where lambda1 is the largest eigenvalue. 

an object of class ’dist’ 


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

Author(s) 



References 



Examples 

w

julliot 121 

Format 

julliot is a list containing the 3 following objects : 

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

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

area is 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. 

Species names of julliot$tab are Pouteria torta, Minquartia guianensis, Quiina obovata, 

Chrysophyllum lucentifolium, Parahancornia fasciculata, Virola michelii, 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: 

for(k in 1:7) 

area.plot(julliot$area,val = log(julliot$tab[,k]+1), 

sub = names(julliot$tab)[k], csub = 2.5) 


if (require(splancs, quiet = TRUE)){ 


for(k in 1:7) 

s.image(julliot$xy, log(julliot$tab[,k]+1), kgrid = 3, span = 0.25, 


} 

## Not run: 


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) 

} 



for(k in 1:7) 

s.value(julliot$xy,log(julliot$tab[,k]+1), 


## Not run:

122 jv73 

if (require(spdep, quiet = TRUE)){ 


neig0

kcponds 123 

Examples 

data(jv73) 

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) 

w

124 kdist 

Examples 

data(kcponds) 


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) 

s.label(kcponds$xy,add.p = TRUE, cpoi = 0, clab = 0, 

neig = kcponds$neig, cneig = 1) 

area.plot(kcponds$area) 

s.label(kcponds$xy, add.p = TRUE, clab = 1.5) 

w

kdist 125 

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) 



References 


analysis. Biometrika, 53, 325–338. 

Examples 

# starting from a list of matrices 


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' 


fri.w

126 kdist2ktab 

d3

kdisteuclid 127 

Value 

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

representation 

Author(s) 



Examples 


fri.w

128 kdisteuclid 

Value 

returns an object of class kdist with all distances Euclidean. 

Note 

according to the program DistPCoa of P. Legendre and M.J. Anderson 

http://www.fas.umontreal.ca/BIOL/Casgrain/en/labo/distpcoa.html 

Author(s) 



References 



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

kplot 129 

kplot 

Generic Function for Multiple Graphs in a K-tables Analysis 

Description 

Usage 

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

kplot(object, ...) 

Arguments 

object 

an object used to select a method 


Examples 

methods(plot) 

methods(scatter) 

methods(kplot) 

kplot.foucart 

Multiple Graphs for the Foucart’s Correspondence Analysis 

Description 

Usage 

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

## S3 method for class 'foucart': 

kplot(object, xax = 1, yax = 2, mfrow = NULL, 

which.tab = 1:length(object$blo), clab.r = 1, clab.c = 1.25, 

csub = 2, possub = "bottomright", ...) 

Arguments 

object 

xax, yax 

mfrow 

which.tab 

clab.r 

an object of class foucart 


a vector of the form ’c(nr,nc)’, otherwise computed by as special own function 

n2mfrow 

vector of table numbers for analyzing 

a character size for the row labels

130 kplot.mcoa 

clab.c 

csub 

possub 

a character size for the column labels 

a character size for the sub-titles used with par("cex")*csub 

a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", 

"bottomright") 


Author(s) 


Examples 

data(bf88) 

fou1

kplot.mfa 131 

csub 

possub 

a character size for the sub-titles, used with par("cex")*csub 




Author(s) 


Examples 


w1

132 kplot.pta 

col.names 

a logical value indicating whether the column labels should be inserted 

traject a logical value indicating whether the trajectories of the rows should be drawn 

in a natural order 

permute.row.col 

if TRUE, the rows are represented by vectors and columns by points, otherwise 

it is the opposite 

clab 

csub 

possub 

a character size for the labels 





Author(s) 


Examples 


w1

kplot.sepan 133 

which.graph 

an option for drawing, an integer between 1 and 4. For each table of which.tab, 

are drawn : 

1 the projections of the principal axes 

2 the projections of the rows 

3 the projections of the columns 

4 the projections of the principal components onto the planes of the compromise 

clab 

cpoint 

csub 

possub 

ask 


a character size for plotting the points, used with par("cex")*cpoint. If zero, 

no points are drawn. 




a logical value indicating if the graphs requires several arrays of figures 


Author(s) 


Examples 


wit1

134 kplot.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, csub = 2, possub = "bottomright", 

show.eigen.value = TRUE, poseig = c("bottom", "top"), ...) 

Arguments 

Details 

object 

xax, yax 

which.tab 

an object of class sepan 


a numeric vector containing the numbers of the tables to analyse 

mfrow parameter for the array of figures to be drawn, otherwise use n2mfrow 

permute.row.col 

if TRUE the rows are represented by arrows and the columns by points, if 

FALSE it is the opposite 

clab.row 

clab.col 

traject.row 

csub 



a logical value indicating whether the trajectories between rows should be drawn 

in a natural order 


possub a string of characters indicating the sub-title position ("topleft", "topright", "bottomleft", 


show.eigen.value 

a logical value indicating whether the eigenvalues bar plot should be drawn 

poseig 

if "top" the eigenvalues bar plot is upside, if "bottom", it is downside 


kplot.sepan superimposes the points for the rows and the arrows for the columns using an 

adapted rescaling such as the scatter.dudi. 

kplot.sepan.coa superimposes the row coordinates and the column coordinates with the same 

scale. 

Author(s) 


Examples 


w

kplot.statis 135 

w

136 krandtest 

Author(s) 


Examples 

data(jv73) 

dudi1

ktab 137 

Value 

plot.krandtest draws the p simulated values histograms and the position of the observed 

value. 

Author(s) 

Daniel Chessel and Stephane Dray 〈dray@biomserv.univ-lyon1.fr〉 

See Also 

randtest 

Examples 

wkrandtest

138 ktab 

Usage 

## S3 method for class 'ktab': 

c(...) 


x[selection] 

is.ktab(x) 


t(x) 


row.names(x) 


col.names(x) 

tab.names(x) 

col.names(x) 

ktab.util.names(x) 

Arguments 

x 

an object of the class ktab 

... a sequence of objects of the class ktab 

selection an integer vector 

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) 



ktab.data.frame 139 

Examples 


wfri

140 ktab.list.df 

Examples 


wescopage

ktab.list.dudi 141 

Examples 

data(jv73) 

l0

142 ktab.match2ktabs 

ll

ktab.within 143 

Examples 

data(meau) 

wit1

144 lascaux 

Examples 

data(bacteria) 

w1

lingoes 145 

Examples 

data(lascaux) 


barplot(dudi.pca(lascaux$meris, scan = FALSE)$eig) 

title(main = "Meristic") 

barplot(dudi.pca(lascaux$colo, scan = FALSE)$eig) 

title(main = "Coloration") 

barplot(dudi.pca(na.omit(lascaux$morpho), scan = FALSE)$eig) 

title(main = "Morphometric") 

barplot(dudi.acm(na.omit(lascaux$orne), scan = FALSE)$eig) 

title(main = "Ornemental") 


lingoes 

Transformation of a Distance Matrix for becoming Euclidean 

Description 

transforms a distance matrix in a Euclidean one. 

Usage 

lingoes(distmat, print = FALSE) 

Arguments 

distmat 

print 


if TRUE, prints the eigenvalues of the matrix 

Details 

The function uses the smaller positive constant k which transforms the matrix of 

an Euclidean one 

√ 

d 2 ij + 2 ∗ k in 

Value 

returns an object of class dist with a Euclidean distance 

Author(s) 



References 

Lingoes, J.C. (1971) Some boundary conditions for a monotone analysis of symmetric matrices. 

Psychometrika, 36, 195–203.

146 lizards 

Examples 


d0

macaca 147 

References 

Bauwens, D., and Díaz-Uriarte, R. (1997) Covariation of life-history traits in lacertid lizards: a 

comparative study. American Naturalist, 149, 91–111. 


Examples 

data(lizards) 

w

148 mafragh 

pro2

mantel.randtest 149 


Belair, G.d. and Bencheikh-Lehocine, M. (1987) Composition et déterminisme de la végétation 

d’une plaine côtière marécageuse : La Mafragh (Annaba, Algérie). Bulletin d’Ecologie, 18, 393– 

407. 

References 


Examples 

data(mafragh) 


s.label(mafragh$xy, inc = FALSE, neig = mafragh$neig, 

sub = "Samples & Neighbourhood graph") 

coa1

150 mantel.rtest 

Description 

Performs a Mantel test between two distance matrices. 

Usage 

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

Arguments 

m1 

m2 

nrepet 




Value 

an object of class randtest (randomization tests) 

Author(s) 

Jean Thioulouse 〈ade4-jt@biomserv.univ-lyon1.fr〉 

References 

Mantel, N. (1967) The detection of disease clustering and a generalized regression approach. Cancer 

Research, 27, 209–220. 

Examples 


gen

maples 151 

Arguments 

m1 

m2 

nrepet 




Value 

an object of class rtest (randomization tests) 

Author(s) 



References 

Mantel, N. (1967) The detection of disease clustering and a generalized regression approach. Cancer 

Research, 27, 209–220. 

Examples 


gen

152 mariages 


Data were obtained from the URL http://www.stanford.edu/~dackerly/acerdata. 

html. 

References 

Ackerly, D. D. and Donoghue, M.J. (1998) Leaf size, sappling allometry, and Corner’s rules: phylogeny 

and correlated evolution in Maples (Acer). American Naturalist, 152, 767–791. 

Examples 

data(maples) 

phy

mcoa 153 


Codes for rows and columns are identical : agri (Farmers), ouva (Farm workers), pat (Company directors 

(commerce and industry)), sup (Liberal profession, executives and higher intellectual professions), 

moy (Intermediate professions), emp (Other white-collar workers), ouv (Manual workers), 

serv (Domestic staff), aut (other workers). 

Vallet, L.A. (1986) Activité professionnelle de la femme mariée et détermination de la position 

sociale de la famille. Un test empirique : la France entre 1962 et 1982. Revue Française de 

Sociologie, 27, 656–696. 

Examples 

data(mariages) 


w

154 mcoa 

"internal" 

scannf 

nf 

tol 

weighting included in X$tabw 



a tolerance threshold, an eigenvalue is considered positive if it is larger than 

-tol*lambda1 where lambda1 is the largest eigenvalue. 

x, object an object of class ’mcoa’ 


xax, yax 

eig.bottom 


a logical value indicating whether the eigenvalues bar plot should be added 

Value 

mcoa returns a list of class ’mcoa’ containing : 

pseudoeig 

call 

nf 

SynVar 

axis 

Tli 

Tl1 

Tax 

Tco 

TL 

TC 

T4 

lambda 

cov2 

a numeric vector with the all pseudo eigenvalues 

the call-up order 


a data frame with the synthetic scores 

a data frame with the co-inertia axes 

a data frame with the co-inertia coordinates 

a data frame with the co-inertia normed scores 

a data frame with the inertia axes onto co-inertia axis 

a data frame with the column coordinates onto synthetic scores 

a data frame with the factors for Tli Tl1 

a data frame with the factors for Tco 

a data frame with the factors for Tax 

a data frame with the all eigenvalues (computed on the separate analyses) 

a numeric vector with the all pseudo eigenvalues (synthetic analysis) 

Author(s) 



References 

Chessel, D. and Hanafi, M. (1996) Analyses de la co-inertie de K nuages de points, Revue de 

Statistique Appliquée, 44, 35–60.

meau 155 

Examples 


w1

156 meaudret 

meaudret 

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

Description 

This data set contains information about sites, environmental variables and species (Trichopters). 

Usage 


Format 

meaudret is a list of 3 components. 

mil is a data frame with 20 sites and 9 variables. 

fau is a data frame with 20 sites and 13 species (Trichopters). 

plan is a data frame with 20 sites and 2 factors. 

dat is a factor with 4 levels. 

sta is a factor with 5 levels. 


Pegaz-Maucet, D. (1980) Impact d’une perturbation d’origine organique sur la dérive des macroinvertébérés 

benthiques d’un cours d’eau. Comparaison avec le benthos. Thèse de troisième cycle, 

Université Lyon 1, 130 p. 

Examples 



pca1

mfa 157 

mfa 

Multiple Factorial Analysis 

Description 

Usage 

performs a multiple factorial analysis, using an object of class ktab. 

mfa(X, option = c("lambda1", "inertia", "uniform", "internal"), 


## S3 method for class 'mfa': 

plot(x, xax = 1, yax = 2, option.plot = 1:4, ...) 


print(x, ...) 


summary(object, ...) 

Arguments 

X 

K-tables, an object of class ktab 

option a string of characters for the weighting of arrays options : 

lambda1 

inertia 

uniform 

internal 

scannf 

nf 

Value 

weighting of group k by the inverse of the first eigenvalue of the k analysis 

weighting of group k by the inverse of the total inertia of the array k 

uniform weighting of groups 

weighting included in X$tabw 



x, object an object of class ’mfa’ 

xax, yax 

option.plot 


an integer between 1 and 4, otherwise the 4 components of the plot are displayed 


Returns a list including : 

tab 

rank 

eig 

li 

TL 

a data frame with the modified array 

a vector of ranks for the analyses 

a numeric vector with the all eigenvalues 

a data frame with the coordinates of rows 

a data frame with the factors associated to the rows (indicators of table)

158 microsatt 

co 

TC 

blo 

lisup 

cg 

link 

corli 

a data frame with the coordinates of columns 

a data frame with the factors associated to the columns (indicators of table) 

a vector indicating the number of variables for each table 

a data frame with the projections of normalized scores of rows for each table 

a data frame with the gravity center for the lisup 

a data frame containing the projected inertia and the links between the arrays 

and the reference array 

a data frame giving the correlations between the $lisup and the $li 

Author(s) 



References 

Escofier, B. and Pagès, J. (1994) Multiple factor analysis (AFMULT package), Computational 


Examples 


w1

microsatt 159 

Format 

microsatt is a list of 4 components. 

tab contains the allelic frequencies for 18 cattle breeds (Taurine or Zebu,French or African) and 9 

microsatellites. 

loci.names is a vector of the names of loci. 

loci.eff is a vector of the number of alleles per locus. 

alleles.names is a vector of the names of alleles. 


Extract of data prepared by D. Laloë 〈ugendla@dga2.jouy.inra.fr〉 from data used in: 

Moazami-Goudarzi, K., D. Laloë, J. P. Furet, and F. Grosclaude (1997) Analysis of genetic relationships 

between 10 cattle breeds with 17 microsatellites. Animal Genetics, 28, 338–345. 

Souvenir Zafindrajaona, P.,Zeuh V. ,Moazami-Goudarzi K., Laloë D., Bourzat D., Idriss A., and 

Grosclaude F. (1999) Etude du statut phylogénétique du bovin Kouri du lac Tchad à l’aide de marqueurs 

moléculaires. Revue d’Elevage et de Médecine Vétérinaire des pays Tropicaux, 55, 155–162. 

Moazami-Goudarzi, K., Belemsaga D. M. A., Ceriotti G., Laloë D. , Fagbohoun F., Kouagou N. T., 

Sidibé I., Codjia V., Crimella M. C., Grosclaude F. and Touré S. M. (2001) 

Caractérisation de la race bovine Somba à l’aide de marqueurs moléculaires. Revue d’Elevage et de 

Médecine Vétérinaire des pays Tropicaux, 54, 1–10. 

References 


Examples 

## Not run: 

data(microsatt) 

fac

160 mjrochet 

mjrochet 

Phylogeny and quantitative traits of teleos fishes 

Description 

This data set describes the phylogeny of 49 teleos fishes as reported by Rochet et al. (2000). It also 

gives life-history traits corresponding to these 49 species. 

Usage 


Format 

mjrochet is a list containing the 2 following objects : 


tab is a data frame with 49 rows and 7 traits. 

Details 

Variables of mjrochet$tab are the following ones : tm (age at maturity (years)), lm (length at 

maturity (cm)), l05 (length at 5 per cent survival (cm)), t05 (time to 5 per cent survival (years)), 

fb (slope of the log-log fecundity-length relationship), fm (fecundity the year of maturity), egg 

(volume of eggs (mm 3 )). 


Data taken from: 

Summary of data - Clupeiformes : http://www.ifremer.fr/maerha/clupe.html 

Summary of data - Argentiniformes : http://www.ifremer.fr/maerha/argentin.html 

Summary of data - Salmoniformes : http://www.ifremer.fr/maerha/salmon.html 

Summary of data - Gadiformes : http://www.ifremer.fr/maerha/gadi.html 

Summary of data - Lophiiformes : http://www.ifremer.fr/maerha/loph.html 

Summary of data - Atheriniformes : http://www.ifremer.fr/maerha/ather.html 

Summary of data - Perciformes : http://www.ifremer.fr/maerha/perci.html 

Summary of data - Pleuronectiformes : http://www.ifremer.fr/maerha/pleuro.html 

Summary of data - Scorpaeniformes : http://www.ifremer.fr/maerha/scorpa.html 

Phylogenetic tree : http://www.ifremer.fr/maerha/life_history.html 

References 

Rochet, M. J., Cornillon, P-A., Sabatier, R. and Pontier, D. (2000) Comparative analysis of phylogenic 

and fishing effects in life history patterns of teleos fishes. Oïkos, 91, 255–270.

mld 161 

Examples 


mjrochet.phy

162 mollusc 

References 

Mallat, S. G. (1989) A theory for multiresolution signal decomposition: the wavelet representation. 

IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, 7, 674–693. 

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

University Press. 

See Also 

gridrowcol, orthobasis, orthogram, mra for multiresolution analysis with various families 

of wavelets 

Examples 

## Not run: 

# decomposition of a time serie 

data(co2) 

x

monde84 163 

Format 


mollusc is a list of 2 objects. 

fau is a data frame with 163 samples and 32 mollusk species (abundance). 

plan contains the 163 samples and 4 variables. 

Richardot-Coulet, M., Chessel D. and Bournaud M. (1986) Typological value of the benthos of old 

beds of a large river. Methodological approach. Archiv fùr Hydrobiologie, 107, 363–383. 

Examples 

data(mollusc) 

coa1

164 morphosport 

Examples 

data(monde84) 

X

mstree 165 

mstree 

Minimal Spanning Tree 

Description 

Minimal Spanning Tree 

Usage 

mstree(xdist, ngmax = 1) 

Arguments 

xdist 

ngmax 

an object of class dist containing an observed dissimilarity 

a component number (default=1). Select 1 for getting classical MST. To add n 

supplementary edges k times: select k+1. 

Value 

returns an object of class neig 

Author(s) 


Examples 

data(mafragh) 

maf.coa = dudi.coa(mafragh$flo, scan = FALSE) 

maf.mst = mstree(dist.dudi(maf.coa), 1) 

s.label(maf.coa$li, clab = 0, cpoi = 2, neig = maf.mst, cnei = 1) 

xy = data.frame(x = runif(20), y = runif(20)) 


for (k in 1:4) { 

neig = mstree (dist.quant(xy,1), k) 

s.label(xy, xlim = c(0,1), ylim = c(0,1), addax = FALSE, neig = neig) 

}

166 multispati 

multispati 

Multivariate spatial analysis 

Description 

This function ensures a multivariate extension of the univariate method of spatial autocorrelation 

analysis. By accounting for the spatial dependence of data observations and their multivariate 

covariance simultaneously, complex interactions among many variables are analysed. Using 

a methodological scheme borrowed from duality diagram analysis, a strategy for the exploratory 

analysis of spatial pattern in the multivariate is developped. 

Usage 

multispati(dudi, listw, scannf = TRUE, nfposi = 2, nfnega = 0) 

## S3 method for class 'multispati': 

plot(x, xax = 1, yax = 2, ...) 




print(x, ...) 

Arguments 

Details 

dudi 

listw 

scannf 

nfposi 

nfnega 

an object of class dudi for the duality diagram analysis 

an object of class listw for the spatial dependence of data observations 


an integer indicating the number of kept positive axes 

an integer indicating the number of kept negative axes 

x, object an object of class multispati 

xax, yax 



This analysis generalizes the Wartenberg’s multivariate spatial correlation analysis to various duality 

diagrams created by the functions (dudi.pca, dudi.coa, dudi.acm, dudi.mix...) If dudi 

is a duality diagram created by the function dudi.pca and listw gives spatial weights created by 

a row normalized coding scheme, the analysis is equivalent to Wartenberg’s analysis. 

We note X the data frame with the variables, Q the column weights matrix and D the row weights 

matrix associated to the duality diagram dudi. We note L the neighbouring weights matrix associated 

to listw. Then, the ’multispati’ analysis gives principal axes v that maximize the product 

of spatial autocorrelation and inertia of row scores : 

I(XQv) ∗ ‖XQv‖ 2 = v t Q t X t DLXQv

multispati 167 

Value 

Returns an object of class multispati, which contains the following elements : 

eig 

nfposi 

nfnega 

c1 

li 

a numeric vector containing the eigenvalues 

integer, number of kept axes associated to positive eigenvalues 

integer, number of kept axes associated to negative eigenvalues 

principle axes (v), data frame with p rows and (nfposi + nfnega) columns 

principal components (XQv), data frame with n rows and (nfposi + nfnega) 

columns 

ls lag vector onto the principal axes (LXQv), data frame with n rows and (nfposi + 

nfnega) columns 

as 

Author(s) 

principal axes of the dudi analysis (u) onto principal axes of multispati (t(u)Qv), 

data frame with dudi$nf rows and (nfposi + nfnega) columns 


Sebastien Ollier 〈ollier@biomserv.univ-lyon1.fr〉 

Thibaut Jombart 〈jombart@biomserv.univ-lyon1.fr〉 

References 

Grunsky, E. C. and Agterberg, F. P. (1988) Spatial and multivariate analysis of geochemical data 

from metavolcanic rocks in the Ben Nevis area, Ontario. Mathematical Geology, 20, 825–861. 

Switzer, P. and Green, A.A. (1984) Min/max autocorrelation factors for multivariate spatial imagery. 

Tech. rep. 6, Stanford University. 



Wartenberg, D. E. (1985) Multivariate spatial correlation: a method for exploratory geographical 

analysis. Geographical Analysis, 17, 263–283. 

Jombart, T., Devillard, S., Dufour, A.-B. and Pontier, D. A spatially explicit multivariate method to 

disentangle global and local patterns of genetic variability. Submitted to Genetics. 

See Also 

dudi,listw 

Examples 

## Not run: 

if (require(maptools, quiet = TRUE) & require(spdep, quiet = TRUE)) { 

data(mafragh) 

maf.xy

168 multispati 

maf.coa.ms

multispati.randtest 169 

w1.msm

170 multispati.rtest 

References 

Smouse, P. E. and Peakall, R. (1999) Spatial autocorrelation analysis of individual multiallele and 

multilocus genetic structure. Heredity, 82, 561–573. 

See Also 

dudi,listw 

Examples 


data(mafragh) 

maf.listw

neig 171 

Author(s) 



References 

Smouse, P. E. and Peakall, R. (1999) Spatial autocorrelation analysis of individual multiallele and 

multilocus genetic structure. Heredity, 82, 561–573. 

See Also 

dudi,listw 

Examples 


data(mafragh) 

maf.listw

172 neig 

Usage 

neig(list = NULL, mat01 = NULL, edges = NULL, 

n.line = NULL, n.circle = NULL, area = NULL) 

scores.neig (obj) 

## S3 method for class 'neig': 

print(x, ...) 

## S3 method for class 'neig': 


nb2neig (nb) 

neig2nb (neig) 

neig2mat (neig) 

Arguments 

list 

mat01 

edges 

n.line 

n.circle 

area 

a list which each component gives the number of neighbours 

a symmetric square matrix of 0-1 values 

a matrix of 2 columns with integer values giving a list of edges 

the number of points for a linear plot 

the number of points for a circular plot 

a data frame containing a polygon set (see area.plot) 

nb 

an object of class ’nb’ 

neig, x, obj, object 

an object of class ’neig’ 


Author(s) 


References 

Thioulouse, J., D. Chessel, and S. Champely. 1995. Multivariate analysis of spatial patterns: a 


Examples 

data(mafragh) 

if (require(tripack, quietly=TRUE)) { 


provi

neig 173 

#hist(dist, nclass = 50) 

mafragh.neig

174 newick.eg 

w

newick2phylog 175 

References 

Bauwens, D. and Díaz-Uriarte, R. (1997) Covariation of life-history traits in lacertid lizards: a 

comparative study. American Naturalist, 149, 91–111. 

Cheverud, J. and Dow, M.M. (1985) An autocorrelation analysis of genetic variation due to lineal 

fission in social groups of rhesus macaques. American Journal of Physical Anthropology, 67, 113– 

122. 

Martins, E. P. and Hansen, T.F. (1997) Phylogenies and the comparative method: a general approach 

to incorporating phylogenetic information into the analysis of interspecific data. American 

Naturalist, 149, 646–667. 

Examples 

data(newick.eg) 

newick2phylog(newick.eg[[11]]) 

radial.phylog(newick2phylog(newick.eg[[7]]), circ = 1, 

clabel.l = 0.75) 

newick2phylog 

Create phylogeny 

Description 

Usage 

The first three functions ensure to create object of class phylog from either a character string in 

Newick format (newick2phylog) or an object of class ’hclust’ (hclust2phylog) or a 

taxonomy (taxo2phylog). The function newick2phylog.addtools is an internal function 

called by newick2phylog, hclust2phylog and taxo2phylog when newick2phylog.addtools 

= TRUE. It adds some items in ’phylog’ objects. 

newick2phylog(x.tre, add.tools = TRUE, call = match.call()) 

hclust2phylog(hc, add.tools = TRUE) 

taxo2phylog(taxo, add.tools = FALSE, root="Root", abbrev=TRUE) 

newick2phylog.addtools(res, tol = 1e-07) 

Arguments 

x.tre 

add.tools 

call 

hc 

taxo 

res 

a character string corresponding to a phylogenetic tree in Newick format 

(http://evolution.genetics.washington.edu/phylip/newicktree. 

html) 

if TRUE, executes the function newick2phylog.addtools 

call 

an object of class hclust 

an object of class taxo 

an object of class phylog (an internal argument of the function newick2phylog)

176 newick2phylog 

tol 

root 

abbrev 

used in case 3 of method as a tolerance threshold for null eigenvalues 

a character string for the root of the tree 

logical : if TRUE levels are abbreviated by column and two characters are added 

before 

Value 

Return object of class phylog. 

Author(s) 



See Also 

phylog, plot.phylog, as.taxo 

Examples 

w

niche 177 

w[19]

178 niche 

Description 

Usage 

performs a special multivariate analysis for ecological data. 

niche(dudiX, Y, scannf = TRUE, nf = 2) 

## S3 method for class 'niche': 

print(x, ...) 


plot(x, xax = 1, yax = 2, ...) 

niche.param(x) 


rtest(xtest,nrepet=99, ...) 

Arguments 

dudiX 

Y 

scannf 

nf 

x 

Value 

a duality diagram providing from a function dudi.coa, dudi.pca, ... using 

an array sites-variables 

a data frame sites-species according to dudiX$tab with no columns of zero 



an object of class niche 


xax, yax 

xtest 

nrepet 


an object of class niche 

the number of permutations for the testing procedure 

Returns a list of the class niche (sub-class of dudi) containing : 

rank 

nf 

RV 

eig 

lw 

tab 

li 

l1 

co 

c1 

ls 

as 

an integer indicating the rank of the studied matrix 

an integer indicating the number of kept axes 

a numeric value indicating the RV coefficient 

a numeric vector with the all eigenvalues 

a data frame with the row weigths (crossed array) 

a data frame with the crossed array (averaging species/sites) 

a data frame with the species coordinates 

a data frame with the species normed scores 

a data frame with the variable coordinates 

a data frame with the variable normed scores 

a data frame with the site coordinates 

a data frame with the axis upon niche axis

njplot 179 

Author(s) 




References 

Dolédec, S., Chessel, D. and Gimaret, C. (2000) Niche separation in community analysis: a new 

method. Ecology, 81, 2914–1927. 

Examples 

data(doubs) 

dudi1

180 olympic 

Usage 

data(njplot) 

Format 

njplot is a list containing the 2 following objects: 

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

tauxcg is a numeric vector that gives the CG rate of the 36 species. 


Data were obtained by Manolo Gouy 〈mgouy@biomserv.univ-lyon1.fr〉 

References 

Perrière, G. and Gouy, M. (1996) WWW-Query : an on-line retrieval system for biological sequence 

banks. Biochimie, 78, 364–369. 

Examples 

data(njplot) 

njplot.phy

optimEH 181 


Example 357 in: 

Hand, D.J., Daly, F., Lunn, A.D., McConway, K.J. and Ostrowski, E. (1994) A handbook of small 

data sets, Chapman & Hall, London. 458 p. 

Lunn, A. D. and McNeil, D.R. (1991) Computer-Interactive Data Analysis, Wiley, New York 

Examples 

data(olympic) 

pca1

182 oribatid 

Value 

Returns a list containing: 

value 

selected.sp 

a real value providing the amount of evolutionary history preserved. 

a data frame containing the list of the k species which optimize the amount of 

evolutionary history preserved and are the most original species in their clades. 

Author(s) 


References 

Nee, S. and May, R.M. (1997) Extinction and the loss of evolutionary history. Science 278, 692– 

694. 

Pavoine, S., Ollier, S. and Dufour, A.-B. (2005) Is the originality of a species measurable? Ecology 

Letters, 8, 579–586. 

See Also 

randEH 

Examples 

data(carni70) 

carni70.phy

originality 183 

Details 

Variables of oribatid$envir are the following ones : 

substrate: a factor with seven levels that describes the nature of the substratum 

shrubs: a factor with three levels that describes the absence/presence of shrubs 

topo: a factor with two levels that describes the microtopography 

density: substratum density (g.L −1 ) 

water: water content of the substratum (g.L −1 ) 


Data prepared by P. Legendre 〈Pierre.Legendre@umontreal.ca〉 and 

D. Borcard 〈borcardd@magellan.umontreal.ca〉 starting from 

http://www.fas.umontreal.ca/biol/casgrain/fr/labo/oribates.html 

References 

Borcard, D., and Legendre, P. (1994) Environmental control and spatial structure in ecological 

communities: an example using Oribatid mites (Acari Oribatei). Environmental and Ecological 

Statistics, 1, 37–61. 

Borcard, D., Legendre, P., and Drapeau, P. (1992) Partialling out the spatial component of ecological 

variation. Ecology, 73, 1045–1055. 


Examples 

data(oribatid) 

ori.xy

184 originality 

Usage 

originality(phyl, method = 5) 

Arguments 

phyl 


method a vector containing integers between 1 and 5. 

Details 

1 = Vane-Wright et al.’s (1991) node-counting index 2 = May’s (1990) branch-counting index 3 

= Nixon and Wheeler’s (1991) unweighted index, based on the sum of units in binary values 4 = 

Nixon and Wheeler’s (1991) weighted index 5 = QE-based index 

Value 

Returns a data frame with species in rows, and the selected indices of originality in columns. Indices 

are expressed as percentages. 

Author(s) 


References 


Letters, 8, 579–586. 

Vane-Wright, R.I., Humphries, C.J. and Williams, P.H. (1991). What to protect? Systematics and 

the agony of choice. Biological Conservation, 55, 235–254. 

May, R.M. (1990). Taxonomy as destiny. Nature, 347, 129–130. 

Nixon, K.C. & Wheeler, Q.D. (1992). Measures of phylogenetic diversity. In: Extinction and 

Phylogeny (eds. Novacek, M.J. and Wheeler, Q.D.), 216–234, Columbia University Press, New 

York. 

Examples 

data(carni70) 

carni70.phy

orisaved 185 

orisaved 

Maximal or minimal amount of originality saved under optimal conditions 

Description 

computes the maximal or minimal amount of originality saved over all combinations of species 

optimizing the amount of evolutionary history preserved. The originality of a species is measured 

with the QE-based index. 

Usage 

orisaved(phyl, rate = 0.1, method = 1) 

Arguments 

phyl 

rate 

method 


a real value (between 0 and 1) indicating how many species will be saved for 

each calculation. For example, if the total number of species is 70 and ’rate = 

0.1’ then the calculations will be done at a rate of 10 % i.e. for 0 (= 0 %), 7 

(= 10 %), 14 (= 20 %), 21 (= 30 %), ..., 63 (= 90 %) and 70(= 100 %) species 

saved. If ’rate = 0.5’ then the calculations will be done for only 0 (= 0 %), 35 (= 

50 %) and 70(= 100 %) species saved. 

an integer either 1 or 2 (see details). 

Details 

1 = maximum amount of originality saved 2 = minimum amount of originality saved 

Value 

Returns a numeric vector. 

Author(s) 


References 


Letters, 8, 579–586.

186 orthobasis 

Examples 

data(carni70) 

carni70.phy

orthobasis 187 

Value 

All the functions excepted print.ortobasis return an object of class orthobasis containing 

a data frame. This data frame defines an orthonormal basis with n-1 vectors of length n. Various 

attributes are associated to it : 

names 

row.names 

class 

values 

weights 

call 

: names of the vectors 

: row names of the data frame 

: class 

: row weights (uniform weights) 

: numeric values to class vectors according to their quadratic forms (Moran 

ones) 

: call 

Note 

the function orthobasis.haar uses function wavelet.filter from package waveslim. 

Author(s) 



References 

Misiti, M., Misiti, Y., Oppenheim, G. and Poggi, J.M. (1993) Analyse de signaux classiques par 

décomposition en ondelettes. Revue de Statistique Appliquée, 41, 5–32. 

Cornillon, P.A. (1998) Prise en compte de proximités en analyse factorielle et comparative. Thèse, 

Ecole Nationale Supérieure Agronomique, Montpellier. 

See Also 

gridrowcol that defines an orthobasis for square grid, phylog that defines an orthobasis for 

phylogenetic tree, orthogram and mld 

Examples 

# a 2D spatial orthobasis 


w

188 orthobasis 

w

orthogram 189 

orthogram 

Orthonormal decomposition of variance 

Description 

This function performs the orthonormal decomposition of variance of a quantitative variable on an 

orthonormal basis. It also returns the results of five non parametric tests associated to the variance 

decomposition. It thus provides tools (graphical displays and test) for analysing phylogenetic, 

spatial and temporal pattern of one quantitative variable. 

Usage 

orthogram(x, orthobas = NULL, neig = NULL, phylog = NULL, 

nrepet = 999, posinega = 0, tol = 1e-07, na.action = c("fail", 

"mean"), cdot = 1.5, cfont.main = 1.5, lwd = 2, nclass, 

high.scores = 0,alter=c("greater", "less", "two-sided")) 

Arguments 

x 

orthobas 

neig 

phylog 

nrepet 

posinega 

tol 

na.action 

cdot 

cfont.main 

lwd 

nclass 

high.scores 

alter 

a numeric vector corresponding to the quantitative variable 

an object of class ’orthobasis’ 

an object of class ’neig’ 

an object of class ’phylog’ 

an integer giving the number of permutations 

a parameter for the ratio test. If posinega > 0, the function computes the ratio 

test. 

a tolerance threshold for orthonormality condition 

if ’fail’ stops the execution of the current expression when z contains any missing 

value. If ’mean’ replaces any missing values by mean(z) 

a character size for points on the cumulative decomposition display 

a character size for titles 

a character size for dash lines 

a single number giving the number of cells for the histogram 

a single number giving the number of vectors to return. If > 0, the function 

returns labels of vectors that explains the larger part of variance. 

a character string specifying the alternative hypothesis, must be one of "greater" 

(default), "less" or "two-sided"

190 orthogram 

Details 

The function computes the variance decomposition of a quantitative vector x on an orthonormal 

basis B. The variable is normalized given the uniform weight to eliminate problem of scales. It 

plots the squared correlations R 2 between x and vectors of B (variance decomposition) and the 

cumulated squared correlations SR 2 (cumulative decomposition). The function also provides five 

non parametric tests to test the existence of autocorrelation. The tests derive from the five following 

statistics : 

R2Max =max(R 2 ). It takes high value when a high part of the variability is explained by one score. 

SkR2k = ∑ n−1 

i=1 (iR2 i ). It compares the part of variance explained by internal nodes to the one explained 

by end nodes. 

Dmax =max m=1,...,n−1 ( ∑ m 

scores. 

j=1 R2 j − m 

n−1 

). It examines the accumulation of variance for a sequence of 

SCE = ∑ n−1 

m=1 (∑ m 

j=1 R2 j − m 

n−1 )2 . It examines also the accumulation of variance for a sequence of scores. 

ratio depends of the parameter posinega. If posinega > 0, the statistic ratio exists and equals ∑ posinega 

i=1 

R 2 i . 

It compares the part of variance explained by internal nodes to the one explained by end nodes when 

we can define how many vectors correspond to internal nodes. 

Value 

If (high.scores = 0), returns an object of class ’krandtest’ (randomization tests) corresponding 

to the five non parametric tests. 

If (high.scores > 0), returns a list containg : 

w 

: an object of class ’krandtest’ (randomization tests) 

scores.order : a vector which terms give labels of vectors that explain the larger part of variance 

Author(s) 



References 

Ollier, S., Chessel, D. and Couteron, P. (2005) Orthonormal Transform to Decompose the Variance 

of a Life-History Trait across a Phylogenetic Tree. Biometrics, 62, 471–477. 

See Also 

gridrowcol, orthobasis, mld

ours 191 

Examples 

# a phylogenetic example 

data(ungulates) 

ung.phy

192 ours 

Usage 

data(ours) 

Format 

This data frame contains the following columns: 

altit importance of the altitudinal area inhabited by bears, a factor with levels: 1 less than 50% of 

the area between 800 and 2000 meters 2 between 50 and 70% 3 more than 70% 

deniv importance of the average variation in level by square of 50 km2, a factor with levels: 1 less 

than 700m 2 between 700 and 900 m 3 more than 900 m 

cloiso partitioning of the massif, a factor with levels: 1 a great valley or a ridge isolates at least a 

quarter of the massif 2 less than a quarter of the massif is isolated 3 the massif has no split 

domain importance of the national forests on contact with the massif, a factor with levels: 1 less than 

400 km2 2 between 400 and 1000 km2 3 more than 1000 km2 

boise rate of afforestation, a factor with levels: 1 less than 30% 2 between 30 and 50% 3 more than 

50% 

hetra importance of plantations and mixed forests, a factor with levels: 1 less than 5% 2 between 5 

and 10% 3 more than 10% of the massif 

favor importance of favorable forests, plantations, mixed forests, fir plantations, a factor with levels: 

1 less than 5% 2 between 5 and 10% 3 more than 10% of the massif 

inexp importance of unworked forests, a factor with levels: 1 less than 4% 2 between 4 and 8% 3 

more than 8% of the total area 

citat presence of the bear before its disappearance, a factor with levels: 1 no quotation since 1840 

2 1 to 3 quotations before 1900 and none after 3 4 quotations before 1900 and none after 4 at 

least 4 quotations before 1900 and at least 1 quotation between 1900 and 1940 

depart district, a factor with levels: AHP Alpes-de-Haute-Provence AM Alpes-Maritimes D Drôme HP 

Hautes-Alpes HS Haute-Savoie I Is"re S Savoie 


Erome, G. (1989) L’ours brun dans les Alpes françaises. Historique de sa disparition. Centre 

Ornithologique Rhône-Alpes, Villeurbanne. 120 p. 

Examples 

data(ours) 

boxplot(dudi.acm(ours, scan = FALSE))

palm 193 

palm 

Phylogenetic and quantitative traits of amazonian palm trees 

Description 

Usage 

Format 

Details 

This data set describes the phylogeny of 66 amazonian palm trees. It also gives 7 traits corresponding 

to these 66 species. 

data(palm) 

palm is a list containing the 2 following objects: 


traits is a data frame with 66 species (rows) and 7 traits (columns). 

Variables of palm$traits are the following ones: 

rord: specific richness with five ordered levels 

h: height in meter (squared transform) 

dqual: diameter at breast height in centimeter with five levels sout : subterranean, d1(0, 

5 cm), d2(5, 15 cm), d3(15, 30 cm) and d4(30, 100 cm) 

vfruit: fruit volume in mm 3 (logged transform) 

vgrain: seed volume in mm 3 (logged transform) 

aire: spatial distribution area (km 2 ) 

alti: maximum altitude in meter (logged transform) 


This data set was obtained by Clémentine Gimaret-Carpentier 

〈gimaret@biomserv.univ-lyon1.fr〉. 

Examples 

## Not run: 

data(palm) 

palm.phy

194 pap 

scalewt((palm$traits[,4]))) 

names(w)[6]

pcaiv 195 

pcaiv 

Principal component analysis with respect to instrumental variables 

Description 

Usage 

performs a principal component analysis with respect to instrumental variables. 

pcaiv(dudi, df, scannf = TRUE, nf = 2) 

## S3 method for class 'pcaiv': 

plot(x, xax = 1, yax = 2, ...) 

## S3 method for class 'pcaiv': 

print(x, ...) 

Arguments 

dudi 

df 

scannf 

nf 

x 

Value 

xax 

yax 


a data frame with the same rows 



an object of class pcaiv 




returns an object of class pcaiv, sub-class of class dudi 

rank 

nf 

eig 

lw 

cw 

Y 

X 

tab 

c1 

as 

ls 

li 



a vector with the all eigenvalues 

a numeric vector with the row weigths (from dudi) 

a numeric vector with the column weigths (from dudi) 

a data frame with the dependant variables 

a data frame with the explanatory variables 

a data frame with the modified array (projected variables) 

a data frame with the Pseudo Principal Axes (PPA) 

a data frame with the Principal axes of dudi$tab on PPA 

a data frame with the projections of lines of dudi$tab on PPA 

a data frame dudi$ls with the predicted values by X

196 pcaivortho 

fa 

l1 

co 

cor 

a data frame with the loadings (Constraint Principal Components as linear combinations 

of X 

data frame with the Constraint Principal Components (CPC) 

a data frame with the inner products between the CPC and Y 

a data frame with the correlations between the CPC and X 

Author(s) 



References 

Rao, C. R. (1964) The use and interpretation of principal component analysis in applied research. 

Sankhya, A 26, 329–359. 

Obadia, J. (1978) L’analyse en composantes explicatives. Revue de Statistique Appliquée, 24, 5–28. 

Lebreton, J. D., Sabatier, R., Banco G. and Bacou A. M. (1991) Principal component and correspondence 

analyses with respect to instrumental variables : an overview of their role in studies of 

structure-activity and speciesenvironment relationships. In J. Devillers and W. Karcher, editors. 

Applied Multivariate Analysis in SAR and Environmental Studies, Kluwer Academic Publishers, 

85–114. 

Examples 

data(rhone) 

pca1

pcaivortho 197 

Arguments 

dudi 

df 

scannf 

nf 


a data frame with the same rows 



Value 

an object of class ’pcaivortho’ sub-class of class dudi 

rank 

nf 

eig 

lw 

cw 

Y 

X 

tab 

c1 

as 

ls 

li 

l1 

co 

param 



a vector with the all eigenvalues 

a numeric vector with the row weigths (from dudi) 

a numeric vector with the column weigths (from dudi) 

a data frame with the dependant variables 

a data frame with the explanatory variables 

a data frame with the modified array (projected variables) 

a data frame with the Pseudo Principal Axes (PPA) 

a data frame with the Principal axis of dudi$tab on PAP 

a data frame with the projection of lines of dudi$tab on PPA 

a data frame dudi$ls with the predicted values by X 

a data frame with the Constraint Principal Components (CPC) 

a data frame with the inner product between the CPC and Y 

a data frame containing a summary 

Author(s) 



References 

Rao, C. R. (1964) The use and interpretation of principal component analysis in applied research. 

Sankhya, A 26, 329–359. 

Sabatier, R., Lebreton J. D. and Chessel D. (1989) Principal component analysis with instrumental 

variables as a tool for modelling composition data. In R. Coppi and S. Bolasco, editors. Multiway 

data analysis, Elsevier Science Publishers B.V., North-Holland, 341–352

198 pcoscaled 

Examples 

## Not run: 


data(avimedi) 

cla

perthi02 199 

Author(s) 


References 


analysis. Biometrika, 53, 325–338. 

Examples 

a

200 phylog 

Examples 

data(perthi02) 

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

phylog 

Phylogeny 

Description 

Create and use objects of class phylog. 

phylog.extract returns objects of class phylog. It extracts sub-trees from a tree. 

phylog.permut returns objects of class phylog. It creates the different representations compatible 

with tree topology. 

Usage 

## S3 method for class 'phylog': 

print(x, ...) 

phylog.extract(phylog, node, distance = TRUE) 

phylog.permut(phylog, list.nodes = NULL, distance = TRUE) 

Arguments 

Value 

x, phylog : an object of class phylog 

... : further arguments passed to or from other methods 

node 

distance 

list.nodes 

Returns a list of class phylog : 

: a string of characters giving a node name. The functions extracts the tree 

rooted at this node. 

: if TRUE, both functions retain branch lengths. If FALSE, they returns tree 

with arbitrary branch lengths (each branch length equals one) 

: a list which elements are vectors of string of character corresponding to direct 

descendants of nodes. This list defines one representation compatible with tree 

topology among the set of possibilities. 

tre 

leaves 

nodes 

parts 

: a character string of the phylogenetic tree in Newick format whithout branch 

length values 

: a vector which names corresponds to leaves and values gives the distance 

between leaves and nodes closest to these leaves 

: a vector which names corresponds to nodes and values gives the distance between 

nodes and nodes closest to these leaves 

: a list which elements gives the direct descendants of each nodes

phylog 201 

paths 

droot 

call 

Wmat 

Wdist 

Wvalues 

Wscores 

Amat 

Avalues 

Adim 

Ascores 

Aparam 

Bindica 

Bscores 

Bvalues 

Blabels 

: a list which elements gives the path leading from the root to taxonomic units 

(leaves and nodes) 

: a vector which names corresponds to taxonomic units and values gives distance 

between taxonomic units and the root 

: call 

: a phylogenetic link matrix, generally called the covariance matrix. Matrix 

values W mat ij correspond to path length that lead from root to the first common 

ancestor of the two leaves i and j 

: a phylogenetic distance matrix of class ’dist’. Matrix values W dist ij correspond 

to √ d ij where d ij is the classical distance between two leaves i and 

j 

: a vector with the eigen values of Wmat 

: a data frame with eigen vectors of Wmat. This data frame defines an orthobasis 

that could be used to calculate the orthonormal decomposition of a biological 

trait on a tree. 

: a phylogenetic link matrix stemed from Abouheif’s test and defined in Ollier 

et al. (submited) 

: a vector with the eigen values of Amat 

: number of positive eigen values 

: a data frame with eigen vectors of Amat. This data frame defines an orthobasis 

that could be used to calculate the orthonormal decomposition of a biological 

trait on a tree. 

: a data frame with attributes associated to nodes. 

: a data frame giving for some taxonomic units the partition of leaves that is 

associated to its 

: a data frame giving an orthobasis defined by Ollier et al. (submited) that could 

be used to calculate the orthonormal decomposition of a biological trait on a 

tree. 

: a vector giving the degree of phylogenetic autocorrelation for each vectors of 

Bscores (Moran’s form calculated with the matrix Wmat) 

: a vector giving for each nodes the name of the vector of Bscores that is associated 

to its 

Author(s) 



References 

Ollier, S., Couteron, P. and Chessel, D. (2005) Orthonormal transforms to detect and describe phylogenetic 

autocorrelation. Biometrics (in press).

202 plot.phylog 

See Also 

newick2phylog, plot.phylog 

Examples 

marthans.tre

plot.phylog 203 

f.phylog 

circle 

cleaves 

cnodes 

a size coefficient for tree size (a parameter to draw the tree in proportion to 

leaves label) 

a size coefficient for the outer circle 

a character size for plotting the points that represent the leaves, used with par("cex")*cleaves. 

If zero, no points are drawn 

a character size for plotting the points that represent the nodes, used with par("cex")*cnodes. 

If zero, no points are drawn 

labels.leaves 

a vector of strings of characters for the leaves labels 

clabel.leaves 

a character size for the leaves labels, used with par("cex")*clabel.leaves. 

If zero, no leaves labels are drawn 

labels.nodes a vector of strings of characters for the nodes labels 

clabel.nodes a character size for the nodes labels, used with par("cex")*clabel.nodes. 

If zero, no nodes labels are drawn 

sub 

csub 

possub 

draw.box 





if TRUE draws a box around the current plot with the function box() 


no.over 

a size coefficient for the number of representations 

Details 

The vector y is an argument of the function plot.phylog that ensures to plot one of the possible 

representations of a phylogeny. The vector y is a permutation of the set of leaves {1,2,. . . ,f} 

compatible with the phylogeny’s topology. 

Value 

The function enum.phylog returns a matrix with as many columns as leaves. Each row gives a 

permutation of the set of leaves {1,2,. . . ,f} compatible with the phylogeny’s topology. 

Author(s) 



See Also 

phylog

204 plot.phylog 

Examples 



for(i in 1:6) plot.phylog(newick2phylog(newick.eg[[i]], FALSE), 

clea = 2, clabel.l = 3, cnod = 2.5) 


## Not run: 


plot.phylog(newick2phylog(newick.eg[[11]], FALSE), clea = 1.5, 

clabel.l = 1.5, clabel.nod = 0.75, f = 0.8) 

plot.phylog(newick2phylog(newick.eg[[10]], FALSE), clabel.l = 0, 

clea = 0, cn = 0, f = 1) 




w7

presid2002 205 

## Not run: 

# plot all the possible representations of a phylogenetic tree 

a

206 procella 

See Also 

This dataset is compatible with elec88 and cnc2003 

Examples 

data(presid2002) 

all((presid2002$tour2$Chirac + presid2002$tour2$Le_Pen) == presid2002$tour2$exprimes) 

## Not run: 

data(elec88) 

data(cnc2003) 

w1 = area.util.class(elec88$area, cnc2003$reg) 


par(mar = c(0.1,0.1,0.1,0.1)) 

area.plot(w1) 

w = scale(elec88$tab$Chirac) 

s.value(elec88$xy, w, add.plot = TRUE) 

scatterutil.sub("Chirac 1988 T1", csub = 2, "topleft") 

area.plot(w1) 

w = scale(presid2002$tour1$Chirac/ presid2002$tour1$exprimes) 



area.plot(w1) 

w = scale(elec88$tab$Mitterand) 


scatterutil.sub("Mitterand 1988 T1", csub = 2, "topleft") 

area.plot(w1) 

w = scale(presid2002$tour2$Chirac/ presid2002$tour2$exprimes) 




procella 

Phylogeny and quantitative traits of birds 

Description 

This data set describes the phylogeny of 19 birds as reported by Bried et al. (2002). It also gives 6 

traits corresponding to these 19 species. 

Usage 

data(procella)

procuste 207 

Format 

procella is a list containing the 2 following objects: 


traits is a data frame with 19 species and 6 traits 

Details 

Variables of procella$traits are the following ones: 

site.fid: a numeric vector that describes the percentage of site fidelity 

mate.fid: a numeric vector that describes the percentage of mate fidelity 

mass: an integer vector that describes the adult body weight (g) 

ALE: a numeric vector that describes the adult life expectancy (years) 

BF: a numeric vector that describes the breeding frequencies 

col.size: an integer vector that describes the colony size (no nests monitored) 

References 

Bried, J., Pontier, D. and Jouventin, P. (2002) Mate fidelity in monogamus birds: a re-examination 

of the Procellariiformes. Animal Behaviour, 65, 235–246. 


Examples 

data(procella) 

pro.phy

208 procuste 

Usage 

procuste(df1, df2, scale = TRUE, nf = 4, tol = 1e-07) 

## S3 method for class 'procuste': 

plot(x, xax = 1, yax = 2, ...) 

## S3 method for class 'procuste': 

print(x, ...) 

Arguments 

df1, df2 

two data frames with the same rows 

scale a logical value indicating whether a transformation by the Gower’s scaling (1971) 

should be applied 

nf 

tol 

x 

Value 

xax 

yax 




is the largest eigenvalue. 

an objet of class procuste 




returns a list of the class procuste with 9 components 

d 

rank 

nfact 

tab1 

tab2 

a numeric vector of the singular values 

an integer indicating the rank of the crossed matrix 


a data frame with the array 1, possibly scaled 

a data frame with the array 2, possibly scaled 

rot1 a data frame with the result of the rotation from array 1 to array 2 

rot2 a data frame with the result of the rotation from array 2 to array 1 

load1 a data frame with the loadings of array 1 

load2 a data frame with the loadings of array 2 

scor1 a data frame with the scores of array 1 

scor2 a data frame with the scores of array 2 

call 

Author(s) 

a call order of the analysis 



procuste.randtest 209 

References 

Digby, P. G. N. and Kempton, R. A. (1987) Multivariate Analysis of Ecological Communities. Population 

and Community Biology Series, Chapman and Hall, London. 

Gower, J.C. (1971) Statistical methods of comparing different multivariate analyses of the same 

data. In Mathematics in the archaeological and historical sciences, Hodson, F.R, Kendall, D.G. & 

Tautu, P. (Eds.) University Press, Edinburgh, 138–149. 

Schönemann, P.H. (1968) On two-sided Procustes problems. Psychometrika, 33, 19–34. 

Torre, F. and Chessel, D. (1994) Co-structure de deux tableaux totalement appariés. Revue de 

Statistique Appliquée, 43, 109–121. 

Dray, S., Chessel, D. and Thioulouse, J. (2003) Procustean co-inertia analysis for the linking of 

multivariate datasets. Ecoscience, 10, 1, 110-119. 

Examples 

data(macaca) 


pro1

210 procuste.rtest 

Description 

performs a Monte-Carlo Test on the sum of the singular values of a procustean rotation. 

Usage 

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

Arguments 

df1 

df2 

nrepet 

a data frame 

a data frame 


Value 

returns a list of class randtest 

Author(s) 


References 

Jackson, D.A. (1995) PROTEST: a PROcustean randomization TEST of community environment 

concordance. Ecosciences, 2, 297–303. 

Examples 

data(doubs) 

pca1

pta 211 

Arguments 

df1 

df2 

nrepet 

a data frame 

a data frame 


Value 

returns a list of class rtest 

Author(s) 



References 

Jackson, D.A. (1995) PROTEST: a PROcustean randomization TEST of community environment 

concordance. Ecosciences, 2, 297–303. 

Examples 

data(doubs) 

pca1

212 pta 

Arguments 

X 

scannf 

nf 

x 

xax, yax 

option 

an object of class ktab where the arrays have 1) the same dimensions 2) the 

same names for columns 3) the same column weightings 



an object of class ’pta’ 


an integer between 1 and 4, otherwise the 4 components of the plot are displayed 


Value 

returns a list of class ’pta’, sub-class of ’dudi’ containing : 

RV 

RV.eig 

RV.coo 

tab.names 

nf 

rank 

tabw 

cw 

lw 

eig 

cos2 

tab 

li 

l1 

co 

c1 

Tli 

Tco 

Tcomp 

Tax 

TL 

TC 

T4 

a matrix with the all RV coefficients 

a numeric vector with the all eigenvalues (interstructure) 

a data frame with the scores of the arrays 

a vector of characters with the array names 



a numeric vector with the array weights 

a numeric vector with the column weights 

a numeric vector with the row weights 

a numeric vector with the all eigenvalues (compromis) 

a numeric vector with the cos 2 between compromise and arrays 

a data frame with the modified array 


a data frame with the row normed scores 


a data frame with the column normed scores 

a data frame with the row coordinates (each table) 

a data frame with the column coordinates (each table) 

a data frame with the principal components (each table) 

a data frame with the principal axes (each table) 

a data frame with the factors for Tli 

a data frame with the factors for Tco 

a data frame with the factors for Tax and Tcomp

quasieuclid 213 

Author(s) 

Pierre Bady 〈pierre.bady@univ-lyon1.fr〉 


References 

Blanc, L., Chessel, D. and Dolédec, S. (1998) Etude de la stabilité temporelle des structures spatiales 

par Analyse d’une série de tableaux faunistiques totalement appariés. Bulletin Français de la 

Pêche et de la Pisciculture, 348, 1–21. 

Thioulouse, J., and D. Chessel. 1987. Les analyses multi-tableaux en écologie factorielle. I De 

la typologie d’état à la typologie de fonctionnement par l’analyse triadique. Acta Oecologica, Oecologia 

Generalis, 8, 463–480. 

Examples 


wit1

214 randEH 

Value 

object of class dist containing a Euclidean distance matrice 

Author(s) 



Examples 


geo

andtest-internal 215 

References 

Nee, S. and May, R.M. (1997) Extinction and the loss of evolutionary history. Science 278, 692– 

694. 


Letters, 8, 579–586. 

See Also 

optimEH 

Examples 

data(carni70) 

carni70.phy

216 randtest 

randtest 

Class of the Permutation Tests (in C). 

Description 

randtest is a generic function. It proposes methods for the following objects between, discrimin, 

coinertia ... 

Usage 

randtest(xtest, ...) 

## S3 method for class 'randtest': 

plot(x, nclass = 10, coeff = 1, ...) 

as.randtest (sim, obs,alter=c("greater", "less", "two-sided"), call = match.cal 

## S3 method for class 'randtest': 

print(x, ...) 

Arguments 

xtest 

x 


an object of class randtest 

... ... further arguments passed to or from other methods; in plot.randtest 

to hist 

nclass 

coeff 

sim 

obs 

alter 

call 

a number of intervals for the histogram 

to fit the magnitude of the graph 

a numeric vector of simulated values 

a numeric vector of an observed value 

a character string specifying the alternative hypothesis, must be one of "greater" 

(default), "less" or "two-sided" 

a call order 

Value 

as.randtest returns a list of class randtest 

plot.randtest draws the simulated values histograms and the position of the observed value 

See Also 

mantel.randtest, procuste.randtest, rtest

andtest.amova 217 

Examples 


for (x0 in c(2.4,3.4,5.4,20.4)) { 

l0

218 randtest.between 

randtest.between 

Monte-Carlo Test on the between-groups inertia percentage (in C). 

Description 

Performs a Monte-Carlo test on the between-groups inertia percentage. 

Usage 


randtest(xtest, nrepet = 999, ...) 

Arguments 

xtest an object of class between 

nrepet the number of permutations 


Value 

a list of the class randtest 

Author(s) 


References 

Romesburg, H. C. (1985) Exploring, confirming and randomization tests. Computers and Geosciences, 

11, 19–37. 

Examples 


pca1

andtest.coinertia 219 

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

Description 

Performs a Monte-Carlo test on a Co-inertia analysis. 

Usage 


randtest(xtest, nrepet = 999, fixed=0, ...) 

Arguments 

Value 

xtest 

nrepet 

fixed 

an object of class coinertia 


when non uniform row weights are used in the coinertia analysis, this parameter 

must be the number of the table that should be kept fixed in the permutations 


a list of the class randtest 

Note 

A testing procedure based on the total coinertia of the analysis is available by the function randtest.coinertia. 

The function allows to deal with various analyses for the two tables. The test is based on random 

permutations of the rows of the two tables. If the row weights are not uniform, mean and variances 

are recomputed for each permutation (PCA); for MCA, tables are recentred and column weights are 

recomputed. If weights are computed using the data contained in one table (e.g. COA), you must 

fix this table and permute only the rows of the other table. The case of decentred PCA (PCA where 

centers are entered by the user) is not yet implemented. If you want to use the testing procedure for 

this case, you must firstly center the table and then perform a non-centered PCA on the modified 

table. The case where one table is treated by hill-smith analysis (mix of quantitative and qualitative 

variables) will be soon implemented. 

Author(s) 

Jean Thioulouse 〈ade4-jt@biomserv.univ-lyon1.fr〉 modified by Stephane Dray 〈dray@biomserv.univlyon1.fr〉 

References 

Dolédec, S. and Chessel, D. (1994) Co-inertia analysis: an alternative method for studying speciesenvironment 

relationships. Freshwater Biology, 31, 277–294.

220 randtest.discrimin 

Examples 

data(doubs) 

dudi1

ankrock 221 

# Df Pillai approx F num Df den Df Pr(>F) 

# meaudret$plan$dat 3 2.73 11.30 27 30 1.6e-09 *** 

# Residuals 16 

# --- 

# Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 

# 2.731/9 = 0.3034 

rankrock 

Ordination Table 

Description 

This data set gives the classification in order of preference of 10 music groups by 51 students. 

Usage 

data(rankrock) 

Format 

A data frame with 10 rows and 51 columns. 

Each column contains the rank (1 for the favorite, . . . , 10 for the less appreciated) 

attributed to the group by a student. 

Examples 

data(rankrock) 

dudi1

222 reconst 

Arguments 

dudi 

an object of class dudi used to select a method: pca or coa 

nf 

an integer indicating the number of kept axes for the reconstitution 


Value 

returns a data frame containing the reconstituted data 

Author(s) 



References 

Gabriel, K.R. (1978) Least-squares approximation of matrices by additive and multiplicative models. 

Journal of the Royal Statistical Society, B , 40, 186–196. 

Examples 

data(rhone) 

dd1

hone 223 

rhone 

Physico-Chemistry Data 

Description 

This data set gives for 39 water samples a physico-chemical description with the number of sample 

date and the flows of three tributaries. 

Usage 

data(rhone) 

Format 

rhone is a list of 3 components. 

tab is a data frame with 39 water samples and 15 physico-chemical variables. 

date is a vector of the sample date (in days). 

disch is a data frame with 39 water samples and the flows of the three tributaries. 


Carrel, G., Barthelemy, D., Auda, Y. and Chessel, D. (1986) Approche graphique de l’analyse 

en composantes principales normée : utilisation en hydrobiologie. Acta Oecologica, Oecologia 

Generalis, 7, 189–203. 

Examples 

data(rhone) 

pca1

224 rlq 

rlq 

RLQ analysis 

Description 

Usage 

RLQ analysis performs a double inertia analysis of two arrays (R and Q) with a link expressed by a 

contingency table (L). The rows of L correspond to the rows of R and the columns of Q correspond 

to the rows of Q. 

rlq(dudiR, dudiL, dudiQ, scannf = TRUE, nf = 2) 

## S3 method for class 'rlq': 

print(x, ...) 


plot(x, xax = 1, yax = 2, ...) 




randtest(xtest,nrepet = 999, ...) 

Arguments 

dudiR 

dudiL 

dudiQ 

scannf 

nf 

x 

Value 

xax 

yax 

object 

xtest 

nrepet 

a duality diagram providing from one of the functions dudi.hillsmith, dudi.pca, 

. . . 

a duality diagram of the function dudi.coa 

a duality diagram providing from one of the functions dudi.hillsmith, dudi.pca, 

. . . 



an rlq object 



an rlq object 

an rlq object 



Returns a list of class ’dudi’, sub-class ’rlq’ containing: 

call 

rank 

call 

rank

lq 225 

nf 

RV 

eig 

lw 

cw 

tab 

li 

l1 

co 

c1 

lR 

mR 

lQ 

mQ 

aR 

aQ 


a numeric value, the RV coefficient 


a numeric vector with the rows weigths (crossed array) 

a numeric vector with the columns weigths (crossed array) 

a crossed array (CA) 

R col = CA row: coordinates 

R col = CA row: normed scores 

Q col = CA column: coordinates 

Q col = CA column: normed scores 

the row coordinates (R) 

the normed row scores (R) 

the row coordinates (Q) 

the normed row scores (Q) 

the axis onto co-inertia axis (R) 

the axis onto co-inertia axis (Q) 

WARNING 

Note 

IMPORTANT : row weights for dudiR and dudiQ must be taken from dudiL. 

A testing procedure based on the total coinertia of the RLQ analysis is available by the function 

randtest.rlq. The function allows to deal with various analyses for tables R and Q. Means and 

variances are recomputed for each permutation (PCA); for MCA, tables are recentred and column 

weights are recomputed.The case of decentred PCA (PCA where centers are entered by the user) 

for R or Q is not yet implemented. If you want to use the testing procedure for this case, you must 

firstly center the table and then perform a non-centered PCA on the modified table. 

Author(s) 


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. 

Dray, S., Pettorelli, N., Chessel, D. (2002) Matching data sets from two different spatial samplings. 

Journal of Vegetation Science, 13, 867–874. 

See Also 

coinertia

226 rpjdl 

Examples 

data(aviurba) 

coa1

test 227 

Examples 

## Not run: 

data(rpjdl) 

xy

228 rtest.between 

Value 

as.rtest returns a list of class rtest 

plot.rtest draws the simulated values histograms and the position of the observed value 

Author(s) 

See Also 


RV.rtest, mantel.rtest, procuste.rtest, randtest 

Examples 


for (x0 in c(2.4,3.4,5.4,20.4)) { 

l0

test.discrimin 229 

References 

Romesburg, H. C. (1985) Exploring, confirming and randomization tests. Computers and Geosciences, 

11, 19–37. 

Examples 


pca1

230 s.arrow 

#Based on 999 replicates 

#Simulated p-value: 0.001 

plot(rand1, main = "Monte-Carlo test") 

summary.manova(manova(as.matrix(meaudret$mil)~meaudret$plan$dat), "Pillai") 

# Df Pillai approx F num Df den Df Pr(>F) 

# meaudret$plan$dat 3 2.73 11.30 27 30 1.6e-09 *** 

# Residuals 16 

# --- 

# Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 

# 2.731/9 = 0.3034 

s.arrow 

Plot of the factorial maps for the projection of a vector basis 

Description 

Usage 

performs the scatter diagrams of the projection of a vector basis. 

s.arrow(dfxy, xax = 1, yax = 2, label = row.names(dfxy), 

clabel = 1, pch = 20, cpoint = 0, boxes = TRUE, edge = TRUE, origin = c(0,0), 

xlim = NULL, ylim = NULL, grid = TRUE, addaxes = TRUE, cgrid = 1, 

sub = "", csub = 1.25, possub = "bottomleft", pixmap = NULL, 

contour = NULL, area = NULL, add.plot = FALSE) 

Arguments 

dfxy 

xax 

yax 

label 

clabel 

pch 

cpoint 

boxes 

edge 

origin 

xlim 

ylim 

grid 

a data frame containing the two columns for the axes 

the column number of x in dfxy 

the column number of y in dfxy 

a vector of strings of characters for the point labels 

if not NULL, a character size for the labels used with par("cex")*clabel 

if cpoint > 0, an integer specifying the symbol or the single character to be 

used in plotting points 

a character size for plotting the points, used with par("cex")*cpoint. If zero, 

no points are drawn. 

if TRUE, labels are framed 

a logical value indicating whether the arrows should be plotted 

the fixed point in the graph space, by default c(0,0) the origin of axes. The 

arrows begin at cent. 

the ranges to be encompassed by the x-axis, if NULL they are computed 

the ranges to be encompassed by the y-axis, if NULL they are computed 

a logical value indicating whether a grid in the background of the plot should be 

drawn

s.chull 231 

addaxes 

cgrid 

sub 

csub 

possub 

pixmap 

contour 

area 

add.plot 

a logical value indicating whether the axes should be plotted 

a character size, parameter used with par("cex")*cgrid, to indicate the 

mesh of the grid 



a string of characters indicating the legend position ("topleft", "topright", "bottomleft", 


an object ’pixmap’ displayed in the map background 

a data frame with 4 columns to plot the contour of the map : each row gives a 

segment (x1,y1,x2,y2) 

a data frame of class ’area’ to plot a set of surface units in contour 

if TRUE uses the current graphics window 

Value 

The matched call. 

Author(s) 


Examples 

s.arrow(cbind.data.frame(runif(55,-2,3), runif(55,-3,2))) 

s.chull 

Plot of the factorial maps with polygons of contour by level of a factor 

Description 

performs the scatter diagrams with polygons of contour by level of a factor. 

Usage 

s.chull(dfxy, fac, xax = 1, yax = 2, 

optchull = c(0.25, 0.5, 0.75, 1), label = levels(fac), clabel = 1, 

cpoint = 0, col = rep(1, length(levels(fac))), xlim = NULL, ylim = NULL, 

grid = TRUE, addaxes = TRUE, origin = c(0,0), include.origin = TRUE, 

sub = "", csub = 1, possub = "bottomleft", cgrid = 1, pixmap = NULL, 

contour = NULL, area = NULL, add.plot = FALSE)

232 s.chull 

Arguments 

dfxy 

fac 

xax 

yax 

optchull 

label 

clabel 

cpoint 

col 

xlim 

ylim 

grid 

addaxes 

origin 

a data frame containing the two columns for the axes 

a factor partioning the rows of the data frame in classes 

the column number of x in dfxy 

the column number of y in dfxy 

the number of convex hulls and their interval 


if not NULL, a character size for the labels, used with par("cex")*clabel 

a character size for plotting the points, used with par("cex")*cpoint. If 

zero, no points are drawn 

a vector of colors used to draw each class in a different color 

the ranges to be encompassed by the x axis, if NULL, they are computed 

the ranges to be encompassed by the y axis, if NULL they are computed 


drawn 


the fixed point in the graph space, for example c(0,0) the origin axes 

include.origin 

a logical value indicating whether the point "origin" should be belonged to the 

graph space 

sub 

csub 

possub 

cgrid 

pixmap 

contour 

area 

add.plot 





a character size, parameter used with par("cex")* cgrid to indicate the mesh 

of the grid 






Value 


Author(s) 

Daniel Chessel

s.class 233 

Examples 

xy 0) 

coul 0, an integer specifying the symbol or the single character to be 


a vector of colors used to draw each class in a different color 

the ranges to be encompassed by the x, if NULL they are computed 

the ranges to be encompassed by the y, if NULL they are computed

234 s.class 

Value 

grid 

addaxes 


drawn 


origin the fixed point in the graph space, for example c(0,0) the origin axes 



graph space 

sub 

csub 

possub 

cgrid 

pixmap 

contour 

area 

add.plot 


Author(s) 


Examples 






of the grid 






xy 0) 

coul

s.corcircle 235 

possub = "bottomright", cell = 0, cstar = 0.5, cgrid = 0, csub = 1.5) 

s.class(dudi1$li, banque[,20], csta = 0, cell = 2, cgrid = 0, 

clab = 1.5) 

s.class(dudi1$li, banque[,20], sub = names(banque)[20], 

possub = "topright", cgrid = 0, col = coul) 


par(mfrow = n2mfrow(ncol(banque))) 

for (i in 1:(ncol(banque))) 

s.class(dudi1$li, banque[,i], clab = 1.5, sub = names(banque)[i], 

csub = 2, possub = "topleft", cgrid = 0, csta = 0, cpoi = 0) 

s.label(dudi1$li, clab = 0, sub = "Common background") 



s.corcircle 

Plot of the factorial maps of a correlation circle 

Description 

performs the scatter diagram of a correlation circle. 

Usage 

s.corcircle(dfxy, xax = 1, yax = 2, label = row.names(df), 

clabel = 1, grid = TRUE, sub = "", csub = 1, possub = "bottomleft", 

cgrid = 0, fullcircle = TRUE, box = FALSE, add.plot = FALSE) 

Arguments 

dfxy 

xax 

yax 

label 

clabel 

grid 

sub 

csub 

possub 

cgrid 

fullcircle 

box 

add.plot 

a data frame with two coordinates 






drawn 





a character size, parameter used with par("cex")*cgrid to indicate the mesh of 

the grid 

a logical value indicating whether the complete circle sould be drawn 

a logical value indcating whether a box should be drawn 

if TRUE uses the current graphics window

236 s.distri 

Value 


Author(s) 


Examples 

data (olympic) 

dudi1

s.distri 237 

Value 

clabel 

cpoint 

pch 

xlim 

ylim 

grid 

addaxes 







the ranges to be encompassed by the y, if NULL they are computed 


drawn 





graph space 

sub 

csub 

possub 

cgrid 

pixmap 

contour 

area 

add.plot 


Author(s) 


Examples 






of the grid 






xy 0)) 

w2 0) & (xy$y < 0)) * (1 - xy$y) * xy$x 

w3 0)) * (1 - xy$x) * xy$y 

w4

238 s.hist 

distri

s.image 239 

Examples 

data(rpjdl) 

coa1

240 s.image 

Value 

sub 

csub 

possub 

neig 

cneig 

image.plot 





an object of class neig 

a size for the neighbouring graph lines used with par("lwd")*cneig 

if TRUE, the image is traced 

contour.plot if TRUE, the contour lines are plotted 

pixmap 

contour 

area 

add.plot 


Author(s) 


Examples 







wxy=data.frame(expand.grid(-3:3,-3:3)) 

names(wxy)=c("x","y") 

z=(1/sqrt(2))*exp(-(wxy$x^2+wxy$y^2)/2) 


s.value(wxy,z) 

s.image(wxy,z) 

s.image(wxy,z,kgrid=5) 

s.image(wxy,z,kgrid=15) 

} 

## Not run: 

data(t3012) 



for(k in 1:12) s.image(t3012$xy,scalewt(t3012$temp[,k]), kgrid = 3) 


} 

data(elec88) 



for(k in 1:12)

s.kde2d 241 

s.image(t3012$xy, scalewt(t3012$temp[,k]), kgrid = 3, sub = names(t3012$temp)[k], 

csub = 3, area = elec88$area) 


} 


s.kde2d 

Scatter Plot with Kernel Density Estimate 

Description 

Usage 

performs a scatter of points without labels by a kernel Density Estimation in One or Two Dimensions 

s.kde2d(dfxy, xax = 1, yax = 2, pch = 20, cpoint = 1, neig = NULL, cneig = 2, 

xlim = NULL, ylim = NULL, grid = TRUE, addaxes = TRUE, cgrid = 1, 

include.origin = TRUE, origin = c(0, 0), sub = "", csub = 1.25, 

possub = "bottomleft", pixmap = NULL, contour = NULL, 

area = NULL, add.plot = FALSE) 

Arguments 

dfxy 

xax 

yax 

pch 

cpoint 

neig 

cneig 

xlim 

ylim 

grid 

addaxes 

a data frame with at least two coordinates 







a neighbouring graph 



the ranges to be encompassed by the y axis, if NULL, they are computed 


drawn 


cgrid a character size, parameter used with par("cex")* ’cgrid’ to indicate the mesh of 

the grid 



graph space 

origin 

the fixed point in the graph space, for example c(0,0) the origin axes

242 s.label 

sub 

csub 

possub 

pixmap 

contour 

area 

add.plot 





an object pixmap displayed in the map background 





Value 


Author(s) 


Examples 

# To recognize groups of points 

data(casitas) 

casitas.fuz = fuzzygenet(casitas) 

casitas.pop

s.label 243 

Arguments 

Value 

dfxy 

xax 

yax 

label 

clabel 

pch 

cpoint 

boxes 

neig 

cneig 

xlim 

ylim 

grid 

addaxes 

a data frame with at least two coordinates 









if TRUE, labels are framed 






drawn 


cgrid a character size, parameter used with par("cex")* cgrid to indicate the mesh 

of the grid 



graph space 

origin 

sub 

csub 

possub 

pixmap 

contour 

area 

add.plot 


Author(s) 












244 s.logo 

Examples 

layout(matrix(c(1,2,3,2), 2, 2)) 

data(atlas) 

s.label(atlas$xy, lab = atlas$names.district, 

area = atlas$area, inc = FALSE, addax = FALSE) 

data(mafragh) 

s.label(mafragh$xy, inc = FALSE, neig = mafragh$neig, addax = FALSE) 


s.label(irishdata$xy, inc = FALSE, contour = irishdata$contour, 

addax = FALSE) 


cha

s.logo 245 

Value 

xax 

yax 

neig 

cneig 

xlim 

ylim 

grid 

addaxes 








drawn 



of the grid 



graph space 

origin 

sub 

csub 

possub 

pixmap 

contour 

area 

add.plot 


Author(s) 











Daniel Chessel and Thibaut Jombart 〈jombart@biomserv.univ-lyon1.fr〉 

Examples 

if(require(pixmap, quiet=TRUE)){ 

data(ggtortoises) 

a1

246 s.match 

} 


index

s.multinom 247 

Value 


of the grid 



graph space 

origin 

sub 

csub 

possub 

pixmap 

contour 

area 

add.plot 







aan object pixmap displayed in the map background 





Author(s) 


Examples 

X

248 s.multinom 

Usage 

s.multinom(dfxy, dfrowprof, translate = FALSE, xax = 1, yax = 2, 

labelcat = row.names(dfxy), clabelcat = 1, cpointcat = if (clabelcat == 0) 2 els 

labelrowprof = row.names(dfrowprof), clabelrowprof = 0.75, 

cpointrowprof = if (clabelrowprof == 0) 2 else 0, pchrowprof = 20, 

coulrowprof = grey(0.8), proba = 0.95, n.sample = apply(dfrowprof, 1, sum), 

axesell = TRUE, ...) 

Arguments 

dfxy 

dfrowprof 

translate 

xax 

yax 

labelcat 

clabelcat 

cpointcat 

dfxy is a data frame containing at least two numerical variables. The rows of 

dfxy are categories such as 1,2 and 3 in the triangular plot. 

dfrowprof is a data frame whose the columns are the rows of dfxy. The 

rows of dfxy are profiles or frequency distributions on the categories. The 

column number of dfrowprof must be equal to the row number of dfxy. 

row.names(dfxy) and names(dfrowprof) must be identical. 

a logical value indicating whether the plot should be translated(TRUE) or not. 

The origin becomes the gravity center weighted by profiles. 

the column number of dfxy for the x-axis 

the column number of dfxy for the y-axis 

a vector of strings of characters for the labels of categories 

an integer specifying the character size for the labels of categories, used with 

par("cex")*clabelcat 

an integer specifying the character size for the points showing the categories, 

used with par("cex")*cpointcat 

labelrowprof a vector of strings of characters for the labels of profiles (rows of dfrowprof) 

clabelrowprof 

an integer specifying the character size for the labels of profiles used with par("cex")*clabelrowprof 

cpointrowprof 

an integer specifying the character size for the points representative of the profiles 

used with par("cex")*cpointrowprof 

pchrowprof 

coulrowprof 

proba 

either an integer specifying a symbol or a single character to be used for the 

profile labels 

a vector of colors used for ellipses, possibly recycled 

a value lying between 0.500 and 0.999 to draw a confidence interval 

n.sample a vector containing the sample size, possibly recycled. Used n.sample = 0 

if the profiles are not issued from a multinomial distribution and that confidence 

intervals have no sense. 

axesell 

a logical value indicating whether the ellipse axes should be drawn 

... further arguments passed from the s.label for the initial scatter plot.

s.traject 249 

Value 

Returns in a hidden way a list of three components : 

tra 

ell 

call 

a vector with two values giving the done original translation. 

a matrix, with 5 columns and for rows the number of profiles, giving the means, 

the variances and the covariance of the profile for the used numerical codes 

(column of dfxy) 

the matched call 

Author(s) 


Examples 


par(mar = c(0.1,0.1,0.1,0.1)) 

proba

250 s.traject 

Arguments 

Value 

dfxy 

fac 

ord 

xax 

yax 

label 

clabel 

cpoint 

pch 

xlim 

ylim 

grid 

addaxes 

edge 

a data frame containing two columns for the axes 

a factor partioning the rows of the data frame in classes 

a vector of length equal to fac. The trajectory is drawn in an ascending order of 

the ord values 












drawn 


if TRUE the arrows are plotted, otherwhise only the segments 




graph space 

sub 

csub 

possub 

cgrid 

pixmap 

contour 

area 

add.plot 


Author(s) 






a character size, parameter used with par("cex")*cgrid to indicate the 


aan object ’pixmap’ displayed in the map background 





s.value 251 

Examples 

rw

252 s.value 

Value 

clegend 

neig 

cneig 

xlim 

ylim 

grid 

addaxes 

a character size for the legend used by par("cex")*clegend 


a size for the neighbouring graph lines used with par("lwd")*\code{cneig} 




drawn 


cgrid a character size, parameter used with par("cex")*cgrid to indicate the 




graph space 

origin 

sub 

csub 

possub 

pixmap 

contour 

area 

add.plot 


Author(s) 


Examples 











xy

santacatalina 253 


irq0

254 sarcelles 

sarcelles 

Array of Recapture of Rings 

Description 

The data frame sarcelles$tab contains the number of the winter teals (Anas C. Crecca) for 

which the ring was retrieved in the area i during the month j (n=3049). 

Usage 

data(sarcelles) 

Format 

sarcelles is a list of 4 components. 

tab is a data frame with 14 rows-areas and 12 columns-months. 

xy is a data frame with the 2 spatial coordinates of the 14 region centers. 

neig is the neighbouring graph between areas, object of the class neig. 

col.names is a vector containing the month items 


Lebreton, J.D. (1973) Etude des déplacements saisonniers des Sarcelles d’hiver, Anas c. crecca 

L., hivernant en Camargue à l’aide de l’analyse factorielle des correspondances. Compte rendu 

hebdomadaire des séances de l’Académie des sciences, Paris, D, III, 277, 2417–2420. 

Examples 

## Not run: 

# depends of pixmap 

if (require(pixmap, quietly=TRUE)) { 

bkgnd.pnm

scalewt 255 

scalewt 

Centring and Scaling a Matrix of Any Weighting 

Description 

transforms a numeric matrix in a centred and scaled matrix for any weighting. 

Usage 

scalewt(X, wt = rep(1, nrow(X)), center = TRUE, scale = TRUE) 

Arguments 

X 

wt 

center 

scale 

a numeric matrix (like object) 

a vector of weighting 

a logical value indicating whether the array should be centred 

a logical value indicating whether the array should be scaled 

Value 

returns a centred, scaled matrix 

Note 

The norms are calculated with 1/n and the columns of null variance are still equal to zero. 

Author(s) 


Examples 

scalewt(matrix(1:12,4,3)) 

scale((matrix(1:12,4,3))) 

scale(matrix(1,4,3)) 

scalewt(matrix(1,4,3))

256 scatter 

scatter 

Scatter Plot 

Description 

scatter is a generic function. It has methods for the classes coa, dudi, fca, acm and pco. 

The scale of the grid is situated on the right-top of the graph. 

The points are in the middle of the labels. 

This process plots the graphs of the multivariate analyses. 

The two axes have the same scale. 

Usage 

scatter(x, ...) 

Arguments 

x 



Details 

The functions scatter use some utilities functions : 

scatterutil.base defines the bottom of the plot for all scatters 

scatterutil.chull plots the polygons of the external contour 

scatterutil.eigen plots the eigenvalues bar plot 

scatterutil.ellipse plots an inertia ellipse for a weighting distribution 

scatterutil.eti.circ puts labels on a correlation circle 

scatterutil.eti puts labels centred on the points 

scatterutil.grid plots a grid and adds a legend 

scatterutil.legend.bw.square puts a legend of values by square size 

scatterutil.legend.square.grey puts a legend by squares and grey levels 

scatterutil.legendgris adds a legend of grey levels for the areas 

scatterutil.scaling to fit a plot on a background bipmap 

scatterutil.star plots a star for a weighting distribution 

scatterutil.sub adds a string of characters in sub-title of a graph

scatter 257 

Author(s) 

See Also 


s.arrow, s.chull, s.class, s.corcircle, s.distri, s.label, s.match, s.traject, 

s.value, add.scatter 

Examples 


plot.new() 

scatterutil.legendgris(1:20, 4, 1.6) 

plot.new() 

scatterutil.sub("lkn5555555555lkn", csub = 2, possub = "bottomleft") 

scatterutil.sub("lkn5555555555lkn", csub = 1, possub = "topleft") 

scatterutil.sub("jdjjl", csub = 3, possub = "topright") 

scatterutil.sub("**", csub = 2, possub = "bottomright") 

x

258 scatter.coa 

scatter.acm 

Plot of the factorial maps in a Multiple Correspondence Analysis 

Description 

Usage 

performs the scatter diagrams of a Multiple Correspondence Analysis. 


scatter(x, xax = 1, yax = 2, mfrow=NULL, csub = 2, possub = "topleft", ...) 

Arguments 

x 

xax 

yax 

mfrow 

csub 

possub 




a vector of the form "c(nr,nc)", if NULL (the default) is computed by n2mfrow 


a string of characters indicating the legend position ("topleft", "topright", "bottomleft", 

"bottomright") in a array of figures 


Author(s) 


Examples 

data(lascaux) 

scatter(dudi.acm(lascaux$ornem, sca = FALSE), csub = 3) 

scatter.coa 

Plot of the factorial maps for a correspondence analysis 

Description 

Usage 

performs the scatter diagrams of a correspondence analysis. 

## S3 method for class 'coa': 

scatter(x, xax = 1, yax = 2, method = 1:3, clab.row = 0.75, 

clab.col = 1.25, posieig = "top", sub = NULL, csub = 2, ...)

scatter.dudi 259 

Arguments 

x 

xax 

yax 

an object of class coa 



method an integer between 1 and 3 

1 Rows and columns with the coordinates of lambda variance 

2 Rows variance 1 and columns by averaging 

3 Columns variance 1 and rows by averaging 

clab.row 

clab.col 

posieig 

sub 

csub 

a character size for the rows 

a character size for the columns 

if "top" the eigenvalues bar plot is upside,vif "bottom" it is downside, if "none" 

no plot 




Author(s) 


References 

Oksanen, J. (1987) Problems of joint display of species and site scores in correspondence analysis. 

Vegetatio, 72, 51–57. 

Examples 



w

260 scatter.dudi 

Usage 


scatter(x, xax = 1, yax = 2, clab.row = 0.75, clab.col = 1, 

permute = FALSE, posieig = "top", sub = NULL, ...) 

Arguments 

x 

xax 

yax 

clab.row 

clab.col 

permute 

posieig 

sub 

an object of class dudi 



a character size for the rows 

a character size for the columns 

if FALSE, the rows are plotted by points and the columns by arrows. If TRUE 

it is the opposite. 

if "top" the eigenvalues bar plot is upside, if "bottom" it is downside, if "none" 

no plot 



Details 

scatter.dudi is a factorial map of individuals and the projection of the vectors of the canonical 

basis multiplied by a constante of rescaling. In the eigenvalues bar plot,the used axes for the plot 

are in black, the other kept axes in grey and the other in white. 

Author(s) 


Examples 

data(deug) 

scatter(dd1

scatter.fca 261 

scatter.fca 

Plot of the factorial maps for a fuzzy correspondence analysis 

Description 

performs the scatter diagrams of a fuzzy correspondence analysis. 

Usage 

## S3 method for class 'fca': 

scatter(x, xax = 1, yax = 2, clab.moda = 1, labels = names(x$tab), 

sub = NULL, csub = 2, ...) 

Arguments 

x 

an object of class fca 

xax 


yax 


clab.moda the character size to write the modalities 

labels a vector of strings of characters for the labels of the modalities 

sub 

a vector of strings of characters to be inserted as legend in each figure 

csub 



Author(s) 


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 

data(coleo) 

coleo.fuzzy

262 sco.boxplot 

sco.boxplot 

Representation of the link between a variable and a set of qualitative 

variables 

Description 

Usage 

represents the link between a variable and a set of qualitative variables. 

sco.boxplot(score, df, labels = names(df), clabel = 1, xlim = NULL, 

grid = TRUE, cgrid = 0.75, include.origin = TRUE, origin = 0, 

sub = NULL, csub = 1) 

Arguments 

score 

df 

labels 

clabel 

xlim 

grid 

a numeric vector 

a data frame with only factors 

a vector of strings of characters for the labels of variables 


the ranges to be encompassed by the x axis, if NULL they are computed 

a logical value indicating whether the scale vertical lines should be drawn 

cgrid a character size, parameter used with par("cex")*cgrid to indicate the 

mesh of the scale 



graph space 

origin 

sub 

csub 

Author(s) 


Examples 

the fixed point in the graph space, for example 0 the origin axis 



w1

sco.distri 263 

banque.acm

264 sco.quant 

Author(s) 


Examples 

w

score 265 

Arguments 

score 

df 

fac 

clabel 

abline 

sub 

csub 

possub 

a numeric vector 

a data frame which rows equal to the score length 

a factor with the same length than the score 

character size for the class labels (if any) used with par("cex")*clabel 

a logical value indicating whether a regression line should be added 

a vector of strings of characters for the labels of variables 




Author(s) 


Examples 

w

266 score.acm 



graph space 

origin 

sub 

csub 

the fixed point in the graph space, for example 0 the origin axis 



Details 

scoreutil.base is a utility function - not for the user - to define the bottom of the layout of all 

score. 

Author(s) 


See Also 

sco.boxplot, sco.distri, sco.quant 

Examples 

## Not run: 

par(mar = c(1,1,1,1)) 

scoreutil.base (runif(20,3,7), xlim = NULL, grid = TRUE, cgrid = 0.8, 

include.origin = TRUE, origin = 0, sub = "Uniform", csub = 1) 


# returns the value of the user coordinate of the low line. 

# The user window id defined with c(0,1) in ordinate. 

# box() 

score.acm 

Graphs to study one factor in a Multiple Correspondence Analysis 

Description 

performs the canonical graph of a Multiple Correspondence Analysis. 

Usage 


score(x, xax = 1, which.var = NULL, mfrow = NULL, 

sub = names(oritab), csub = 2, possub = "topleft", ...)

score.coa 267 

Arguments 

x 

xax 

which.var 

mfrow 

sub 

csub 

possub 


the column number for the used axis 

the numbers of the kept columns for the analysis, otherwise all columns 

a vector of the form "c(nr,nc)", otherwise computed by a special own function 

n2mfrow 

a vector of strings of characters to be inserted as sub-titles, otherwise the variable 

names of the initial array 

a character size for the sub-titles 




Author(s) 


Examples 

data(banque) 

banque.acm 0.2), csub = 3) 

score.coa 

Reciprocal scaling after a correspondence analysis 

Description 

Usage 

performs the canonical graph of a correspondence analysis. 

## S3 method for class 'coa': 

score(x, xax = 1, dotchart = FALSE, clab.r = 1, clab.c = 1, 

csub = 1, cpoi = 1.5, cet = 1.5, ...) 

reciprocal.coa(x) 

Arguments 

x 

xax 

dotchart 

clab.r 

clab.c 



if TRUE the graph gives a "dual scaling", if FALSE a "reciprocal scaling" 

a character size for row labels 

a character size for column labels

268 score.coa 

csub 

cpoi 

cet 

Details 

Value 


a character size for the points 

a coefficient for the size of segments in standard deviation 


In a "reciprocal scaling", the reference score is a numeric code centred and normalized of the non 

zero cells of the array which both maximizes the variance of means by row and by column. The 

bars are drawn with half the length of this standard deviation. 

return a data.frame with the scores, weights and factors of correspondences (non zero cells) 

Author(s) 


References 

Thioulouse, J. and Chessel D. (1992) A method for reciprocal scaling of species tolerance and 

sample diversity. Ecology, 73, 670–680. 

Examples 

layout(matrix(c(1,1,2,3), 2, 2), resp = FALSE) 

data(aviurba) 

dd1 averaging -> species amplitude 

# 3 species score -> averaging -> sample diversity 

layout(matrix(c(1,1,2,3), 2, 2), resp = FALSE) 

data(rpjdl) 

rpjdl1

score.mix 269 


score(rpjdl1, dotchart = TRUE, clab.r = 0) 

score.mix 

Graphs to Analyse a factor in a Mixed Analysis 

Description 

performs the canonical graph of a mixed analysis. 

Usage 

## S3 method for class 'mix': 

score(x, xax = 1, csub = 2, mfrow = NULL, which.var = NULL, ...) 

Arguments 

x 

xax 

csub 

mfrow 

which.var 

an object of class mix 




n2mfrow 



Author(s) 


Examples 

data(lascaux) 

w

270 score.pca 

score.pca 

Graphs to Analyse a factor in PCA 

Description 

performs the canonical graph of a Principal Component Analysis. 

Usage 

## S3 method for class 'pca': 

score(x, xax = 1, which.var = NULL, mfrow = NULL, csub = 2, 

sub = names(x$tab), abline = TRUE, ...) 

Arguments 

x 

xax 

which.var 

mfrow 

csub 

sub 

abline 

an object of class pca 




n2mfrow 

a character size for sub-titles, used with par("cex")*csub 

a vector of string of characters to be inserted as sub-titles, otherwise the names 

of the variables 

a logical value indicating whether a regression line should be added 


Author(s) 


Examples 

data(deug) 

dd1

seconde 271 

seconde 

Students and Subjects 

Description 

The seconde data frame gives the marks of 22 students for 8 subjects. 

Usage 

data(seconde) 

Format 

This data frame (22,8) contains the following columns: - HGEO: History and Geography - FRAN: 

French literature - PHYS: Physics - MATH: Mathematics - BIOL: Biology - ECON: Economy - 

ANGL: English language - ESPA: Spanish language 


Personal communication 

Examples 

data(seconde) 

scatter(dudi.pca(seconde, scan = FALSE), clab.r = 1, clab.c = 1.5) 

sepan 

Separated Analyses in a K-tables 

Description 

performs K separated multivariate analyses of an object of class ktab containing K tables. 

Usage 

sepan(X, nf = 2) 

## S3 method for class 'sepan': 

plot(x, mfrow = NULL, csub = 2, ...) 




print(x, ...)

272 sepan 

Arguments 

X 

nf 

Details 

Value 

an object of class ktab 

an integer indicating the number of kept axes for each separated analysis 

x, object an object of class ’sepan’ 

mfrow 

csub 


n2mfrow 



The function plot on a sepan object allows to compare inertias and structures between arrays. In 

black, the eigenvalues of kept axes in the object ’sepan’. 

returns a list of class ’sepan’ containing : 

call 

tab.names 

blo 

rank 

Eig 

Li 

L1 

Co 

C1 

TL 

TC 

a call order 

a vector of characters with the names of tables 

a numeric vector with the numbers of columns for each table 

a numeric vector with the rank of the studied matrix for each table 



a data frame with the row normed scores 


a data frame with the column normed coordinates 

a data frame with the factors for Li L1 

a data frame with the factors for Co C1 

Author(s) 


Examples 


w

skulls 273 

skulls 

Morphometric Evolution 

Description 

Usage 

Format 

Details 

This data set gives four anthropometric measures of 150 Egyptean skulls belonging to five different 

historical periods. 

data(skulls) 

The skulls data frame has 150 rows (egyptean skulls) and 4 columns (anthropometric measures). 

The four variables are the maximum breadth (V1), the basibregmatic height (V2), the basialveolar 

length (V3) and the nasal height (V4). All measurements were taken in millimeters. 

The measurements are made on 5 groups and 30 Egyptian skulls. The groups are defined as follows 

: 

1 - the early predynastic period (circa 4000 BC) 

2 - the late predynastic period (circa 3300 BC) 

3 - the 12th and 13th dynasties (circa 1850 BC) 

4 - the Ptolemiac period (circa 200 BC) 

5 - the Roman period (circa 150 BC) 


Thompson, A. and Randall-Maciver, R. (1905) Ancient races of the Thebaid, Oxford University 

Press. 

References 

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

London. 1–215. 

The example is treated pp. 6, 13, 51, 64, 72, 107, 112 and 117. 

Examples 

data(skulls) 

pca1

274 statis 

statis 

STATIS, a method for analysing K-tables 

Description 

Usage 

performs a STATIS analysis of a ktab object. 

statis(X, scannf = TRUE, nf = 3, tol = 1e-07) 

## S3 method for class 'statis': 

plot(x, xax = 1, yax = 2, option = 1:4, ...) 

## S3 method for class 'statis': 

print(x, ...) 

Arguments 

X 

scannf 

nf 

tol 

x 

Value 

xax, yax 

option 

an object of class ’ktab’ 

a logical value indicating whether the number of kept axes for the compromise 

should be asked 

if scannf FALSE, an integer indicating the number of kept axes for the compromise 



is the largest eigenvalue 

an object of class ’statis’ 


an integer between 1 and 4, otherwise the 4 components of the plot are dispayed 


statis returns a list of class ’statis’ containing : 

$RV 

RV.eig 

RV.coo 

tab.names 

$RV.tabw 

nf 

rank 

C.li 

C.Co 

a matrix with the all RV coefficients 


a data frame with the array scores 

a vector of characters with the names of the arrays 

a numeric vector with the array weigths 




a data frame with the column coordinates

steppe 275 

C.T4 

TL 

TC 

T4 

a data frame with the principal vectors (for each table) 

a data frame with the factors (not used) 

a data frame with the factors for Co 

a data frame with the factors for T4 

Author(s) 


References 

Lavit, C. (1988) Analyse conjointe de tableaux quantitatifs, Masson, Paris. 

Lavit, C., Escoufier, Y., Sabatier, R. and Traissac, P. (1994) The ACT (Statis method). Computational 


Examples 

data(jv73) 

kta1

276 supcol 

Format 


steppe is a list of 2 components. 

tab is a data frame with 512 rows (sites) and 37 variables (species) in presence-absence. 

esp.names is a vector of the species names. 

Estève, J. (1978) Les méthodes d’ordination : éléments pour une discussion. in J. M. Legay and R. 

Tomassone, editors. Biométrie et Ecologie, Société Française de Biométrie, Paris, 223–250. 

Examples 


data(steppe) 

w1

suprow 277 

Details 

If supcol.default is used, the column vectors of Xsup are projected without prior modification 

onto the principal components of dudi with the scalar product associated to the row weightings 

of dudi. 

Value 

returns a list of two components : $tabsup data frame containing the array with the supplementary 

columns transformed or not $cosup data frame containing the coordinates of the supplementary 

projections 

Author(s) 



Examples 

data(rpjdl) 

rpjdl.coa

278 suprow 

Arguments 

x 

Details 

Value 

Xsup 

an object of class dudi 

an array with the supplementary rows (Xsup and x$tab have the same column 

number) 


If suprow.default is used, the column vectors of Xsup are projected without prior modifications 

onto the principal components of dudi with the scalar product associated to the row weightings 

of dudi. 

returns a data frame containing the coordinates of the supplementary projections 

Author(s) 



References 

Gower, J. C. (1967) Multivariate analysis and multivariate geometry. The statistician, 17, 13–28. 

Examples 

data(euro123) 


w

symbols.phylog 279 

symbols.phylog 

Representation of a quantitative variable in front of a phylogenetic 

tree 

Description 

symbols.phylog draws the phylogenetic tree and represents the values of the variable by symbols 

(squares or circles) which size is proportional to value. White symbols correspond to values 

which are below the mean, and black symbols correspond to values which are over. 

Usage 

symbols.phylog(phylog, circles, squares, csize = 1, clegend = 1, 

sub = "", csub = 1, possub = "topleft") 

Arguments 

phylog 

circles 

squares 

csize 

clegend 

sub 

csub 

possub 


a vector giving the radii of the circles 

a vector giving the length of the sides of the squares 

a size coefficient for symbols 

a character size for the legend used by par("cex")*clegend 





Author(s) 



See Also 

table.phylog and dotchart.phylog for many variables 

Examples 


mjrochet.phy

280 t3012 

syndicats 

Two Questions asked on a Sample of 1000 Respondents 

Description 

This data set is extracted from an opinion poll (period 1970-1980) on 1000 respondents. 

Usage 

data(syndicats) 

Format 

The syndicats data frame has 5 rows and 4 columns. 

"Which politic family are you agreeing about ?" has 5 response items : extgauche (extreme left) 

left center right and extdroite (extreme right) 

"What do you think of the trade importance ?" has 4 response items : trop (too important) 

adequate insufficient nesaispas (no opinion) 


unknown 

Examples 

data(syndicats) 


dudi1

table.cont 281 

Format 


t3012 is a list of 3 objects: 

xy is a data frame with 30 rows (cities) and 2 coordinates (x,y). 

temp is a data frame with 30 rows (cities) and 12 columns (months). Each column contains the 

average temperature in tenth of degree Celsius. 

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

Besse, P. (1979) Etude descriptive d’un processus; approximation, interpolation. Thèse de troisième 

cycle, Université Paul Sabatier, Toulouse. 

Examples 

data(t3012) 

data(elec88) 

area.plot(elec88$area) 

s.arrow(t3012$xy, ori = as.numeric(t3012$xy["Paris",]), 

add.p = TRUE) 

table.cont 

Plot of Contingency Tables 

Description 

presents a graph for viewing contingency tables. 

Usage 

table.cont(df, x = 1:ncol(df), y = 1:nrow(df), 

row.labels = row.names(df), col.labels = names(df), 

clabel.row = 1, clabel.col = 1, abmean.x = FALSE, abline.x = FALSE, 

abmean.y = FALSE, abline.y = FALSE, csize = 1, clegend = 0, grid = TRUE) 

Arguments 

df 

x 

y 

row.labels 

col.labels 

clabel.row 

clabel.col 

a data frame with only positive or null values 

a vector of values to position the columns 

a vector of values to position the rows 

a character vector for the row labels 

a character vetor for the column labels 


a character size for the column labels

282 table.dist 

abmean.x 

abline.x 

abmean.y 

abline.y 

csize 

clegend 

grid 

a logical value indicating whether the column conditional means should be 

drawn 

a logical value indicating whether the regression line of y onto x should be plotted 

a logical value indicating whether the row conditional means should be drawn 

a logical value indicating whether the regression line of x onto y should be plotted 

a coefficient for the square size of the values 

if not NULL, a character size for the legend used with par("cex")*clegend 


drawn 

Author(s) 


Examples 

data(chats) 

chatsw

table.paint 283 

Usage 

table.dist(d, x = 1:(attr(d, "Size")), labels = as.character(x), 

clabel = 1, csize = 1, grid = TRUE) 

Arguments 

d 

x 

labels 

clabel 

csize 

grid 


a vector of the row and column positions 

a vector of strings of characters for the labels 


a coefficient for the circle size 


drawn 

Author(s) 


Examples 

data(eurodist) 

table.dist(eurodist, labels = attr(eurodist, "Labels")) 

table.paint 

Plot of the arrays by grey levels 

Description 

presents a graph for viewing the numbers of a table by grey levels. 

Usage 

table.paint(df, x = 1:ncol(df), y = nrow(df):1, 

row.labels = row.names(df), col.labels = names(df), 

clabel.row = 1, clabel.col = 1, csize = 1, clegend = 1) 

Arguments 

df 

x 

y 

row.labels 

col.labels 

clabel.row 

clabel.col 

csize 

clegend 

a data frame 

a vector of values to position the columns, used only for the ordered values 

a vector of values to position the rows, used only for the ordered values 


a character vector for the column labels 



if ’clegend’ not NULL, a coefficient for the legend size 

a character size for the legend, otherwise no legend

284 table.phylog 

Author(s) 


Examples 

data(rpjdl) 

X

table.value 285 

labels.col 

clabel.col 

labels.nod 

clabel.nod 

cleaves 

cnodes 

csize 

grid 

clegend 

: a vector of strings of characters for columns labels 

: a character size for the leaves labels, used with par("cex")*clabel.col. 

If zero, no column labels are drawn 

: a vector of strings of characters for the nodes labels 

: a character size for the nodes labels, used with par("cex")*clabel.nodes. 

If zero, no nodes labels are drawn 

: a character size for plotting the points that represent the leaves, used with 

par("cex")*cleaves. If zero, no points are drawn 

: a character size for plotting the points that represent the nodes, used with 

par("cex")*cnodes. If zero, no points are drawn 

: a size coefficient for symbols 

: a logical value indicating whether the grid should be plotted 

: a character size for the legend (if 0, no legend) 

Details 

The function verifies that sort(row.names(df))==sort(names(phylog$leaves)). If 

df is a matrix the function uses as.data.frame(df). 

Author(s) 



See Also 

symbols.phylog for one variable 

Examples 


w.phy

286 tarentaise 

Usage 

table.value(df, x = 1:ncol(df), y = nrow(df):1, 

row.labels = row.names(df), col.labels = names(df), clabel.row = 1, 

clabel.col = 1, csize = 1, clegend = 1, grid = TRUE) 

Arguments 

df 

x 

y 

row.labels 

col.labels 

clabel.row 

clabel.col 

csize 

clegend 

grid 

a data frame 

a vector of values to position the columns 

a vector of values to position the rows 


a character vector for the column labels 



a coefficient for the square size of the values 

a character size for the legend (if 0, no legend) 

a logical value indicating whether the grid should be plotted 

Author(s) 


Examples 

data(olympic) 

w

tarentaise 287 

Format 

tarentaise is a list of 5 components. 

ecol is a data frame with 376 sites and 98 bird species. 

frnames is a vector of the 98 French names of the species. 

alti is a vector giving the altitude of the 376 sites in m. 

envir is a data frame with 14 environmental variables. 

traits is a data frame with 29 biological variables of the 98 species. 

Details 

The attribute col.blocks of the data frame tarentaise$traits indicates it is composed of 

6 units of variables. 


Original data from Hubert Tournier, University of Savoie and Philippe Lebreton, University of Lyon 

1. 

References 

Lebreton, P., Tournier H. and Lebreton J. D. (1976) Etude de l’avifaune du Parc National de la 

Vanoise VI Recherches d’ordre quantitatif sur les Oiseaux forestiers de Vanoise. Travaux Scientifiques 

du parc National de la vanoise, 7, 163–243. 

Lebreton, Ph. and Martinot, J.P. (1998) Oiseaux de Vanoise. Guide de l’ornithologue en montagne. 

Libris, Grenoble. 1–240. 

Lebreton, Ph., Lebrun, Ph., Martinot, J.P., Miquet, A. and Tournier, H. (1999) Approche écologique 

de l’avifaune de la Vanoise. Travaux scientifiques du Parc national de la Vanoise, 21, 7–304. 


Examples 

data(tarentaise) 

coa1

288 taxo.eg 

taxo.eg 

Examples of taxonomy 

Description 

This data sets contains two taxonomies. 

Usage 

data(taxo.eg) 

Format 

taxo.eg is a list containing the 2 following objects: 

taxo.eg[[1 ]] is a data frame with 15 species and 3 columns. 

taxo.eg[[2 ]] is a data frame with 40 species and 2 columns. 

Details 

Variables of the first data frame are : genre (a factor genre with 8 levels), famille (a factor familiy 

with 5 levels) and ordre (a factor order with 2 levels). 

Variables of the second data frame are : gen(a factor genre with 29 levels), fam (a factor family with 

19 levels). 

Examples 

data(taxo.eg) 

taxo.eg[[1]] 

as.taxo(taxo.eg[[1]]) 

class(taxo.eg[[1]]) 

class(as.taxo(taxo.eg[[1]])) 

tax.phy

testdim 289 

testdim 

Function to perform a test of dimensionality 

Description 

This functions allow to test for the number of axes in multivariate analysis. The procedure is only 

implemented for principal component analysis on correlation matrix. The procedure is based on the 

computation of the RV coefficient. 

Usage 

testdim(dudi, ...) 

## S3 method for class 'pca': 

testdim(dudi, nrepet = 99, nbax = dudi$rank, alpha = 0.05, ...) 

Arguments 

dudi 

a duality diagram (an object of class dudi) 

nrepet the number of repetitions for the permutation procedure 

nbax 

the number of axes to be tested, by default all axes 

alpha the significance level 

... other arguments 

Value 

An object of the class krandtest. It contains also: 

nb 

nb.cor 

The estimated number of axes to keep 

The number of axes to keep estimated using a sequential Bonferroni procedure 

Author(s) 


References 

Dray, S. (2007) On the number of principal components: A test of dimensionality based on measurements 

of similarity between matrices. Computational Statistics and Data Analysis, in press. 

See Also 

dudi.pca, RV.rtest

290 tintoodiel 

Examples 

tab

tithonia 291 

s.label(tintoodiel$xy,pixmap = estuary.pnm, neig = tintoodiel$neig, 

clab = 0, cpoi = 2, cneig = 3, addax = FALSE, cgrid = 0, grid = FALSE) 

} 


estuary.pca

292 tortues 

demo11: is a numeric vector that describes the viability (per cent) 

demo12: is a numeric vector that describes the germination (per cent) 

demo13: is a integer vector that describes the resource allocation 

demo14: is a numeric vector that describes the adult height (m) 


Data were obtained from Morales, E. (2000) Estimating phylogenetic inertia in Tithonia (Asteraceae) 

: a comparative approach. Evolution, 54, 2, 475–484. 

Examples 

data(tithonia) 

phy

toxicity 293 

points(ref,xyz[,2], pch = pch0) 

abline(lm(xyz[,2]~ -1 + ref)) 

points(ref,xyz[,3], pch = pch0) 

abline(lm(xyz[,3]~ -1 + ref)) 

toxicity 

Homogeneous Table 

Description 

This data set gives the toxicity of 7 molecules on 16 targets expressed in -log(mol/liter) 

Usage 

data(toxicity) 

Format 

toxicity is a list of 3 components. 

tab is a data frame with 7 columns and 16 rows 

species is a vector of the names of the species in the 16 targets 

chemicals is a vector of the names of the 7 molecules 


Devillers, J., Thioulouse, J. and Karcher W. (1993) Chemometrical Evaluation of Multispecies- 

Multichemical Data by Means of Graphical Techniques Combined with Multivariate Analyses. 

Ecotoxicology and Environnemental Safety, 26, 333–345. 

Examples 

data(toxicity) 

table.paint(toxicity$tab, row.lab = toxicity$species, 

col.lab = toxicity$chemicals) 

table.value(toxicity$tab, row.lab = toxicity$species, 

col.lab = toxicity$chemicals)

294 triangle.class 

triangle.class 

Triangular Representation and Groups of points 

Description 

Usage 

A concise (1-5 lines) description of what the function does. 

triangle.class(ta, fac, col = rep(1, length(levels(fac))), 

wt = rep(1, length(fac)), cstar = 1, cellipse = 0, axesell = TRUE, 

label = levels(fac), clabel = 1, cpoint = 1, pch = 20, draw.line = TRUE, 

addaxes = FALSE, addmean = FALSE, labeltriangle = TRUE, sub = "", csub = 1, 

possub = "bottomright", show.position = TRUE, scale = TRUE, min3 = NULL, 

max3 = NULL) 

Arguments 

ta 

fac 

col 

wt 

cstar 

cellipse 

axesell 

label 

clabel 

cpoint 

pch 

draw.line 

addaxes 

a data frame with 3 columns of null or positive numbers 

a factor of length the row number of ta 

a vector of color for showing the groups 

a vector of row weighting for the computation of the gravity centers by class 

a character size for plotting the stars between 0 (no stars) and 1 (complete star) 

for a line linking a point to the gravity center of its belonging class. 

a positive coefficient for the inertia ellipse size 

a logical value indicating whether the ellipse axes should be drawn 

a vector of strings of characters for the labels of gravity centers 






a logical value indicating whether the triangular lines should be drawn 


addmean a logical value indicating whether the mean point should be plotted 

labeltriangle 

a logical value indicating whether the varliable labels of ta should be drawn on 

the triangular sides 

sub 

csub 

possub 

a string of characters for the graph title 

a character size for plotting the graph title 


"bottomright")

triangle.plot 295 

show.position 

a logical value indicating whether the sub-triangle containing the data should be 

put back in the total triangle 

scale 

a logical value for the graph representation : the total triangle (FALSE) or the 

sub-triangle (TRUE) 

min3 if not NULL, a vector with 3 numbers between 0 and 1 

max3 

Author(s) 


Examples 

if not NULL, a vector with 3 numbers between 0 and 1. Let notice that min3+max3 

must equal c(1,1,1) 

data(euro123) 


x = rbind.data.frame(euro123$in78, euro123$in86, euro123$in97) 

triangle.plot(x) 

triangle.class(x, as.factor(rep("G",36)), csta = 0.5, cell = 1) 

triangle.class(x, euro123$plan$an) 

triangle.class(x, euro123$plan$pays) 

triangle.class(x, euro123$plan$an, cell = 1, axesell = TRUE) 

triangle.class(x, euro123$plan$an, cell = 0, csta = 0, 

col = c("red", "green", "blue"), axesell = TRUE, clab = 2, cpoi = 2) 

triangle.class(x, euro123$plan$an, cell = 2, csta = 0.5, 

axesell = TRUE, clab = 1.5) 

triangle.class(x, euro123$plan$an, cell = 0, csta = 1, scale = FALSE, 

draw.line = FALSE, show.posi = FALSE) 

triangle.plot 

Triangular Plotting 

Description 

Usage 

Graphs for a dataframe with 3 columns of positive or null values 

triangle.plot is a scatterplot 

triangle.biplot is a paired scatterplots 

triangle.posipoint, triangle.param, add.position.triangle are utilitaries functions. 

triangle.plot(ta, label = as.character(1:nrow(ta)), clabel = 0, 

cpoint = 1, draw.line = TRUE, addaxes = FALSE, addmean = FALSE, 

labeltriangle = TRUE, sub = "", csub = 0, possub = "topright", 

show.position = TRUE, scale = TRUE, min3 = NULL, max3 = NULL,

296 triangle.plot 

box = FALSE) 

triangle.biplot (ta1, ta2, label = as.character(1:nrow(ta1)), 

draw.line = TRUE, show.position = TRUE, scale = TRUE) 

Arguments 

ta, ta1, ta2, 

data frame with three columns, will be transformed in percentages by rows 

label 

clabel 

cpoint 

draw.line 

addaxes 

addmean 





a logical value indicating whether the lines into the triangle should be drawn 

a logical value indicating whether the principal axes should be drawn 

a logical value indicating whether the mean should be plotted 

labeltriangle 

a logical value indicating whether the variable names should be wrote 

sub 

csub 

possub 





show.position 

a logical value indicating whether the used triangle should be shown in the complete 

one 

scale 

min3 

max3 

box 

a logical value indicating whether the smaller equilateral triangle containing the 

plot should be used 

If scale is FALSE, a vector of three values for the minima e.g. c(0.1,0.1,0.1) can 

be used 

If scale is FALSE a vector of three values for the maxima e.g. c(0.9,0.9,0.9) can 

be used 

a logical value indicating whether a box around the current plot should be drawn 

Value 

triangle.plot returns an invisible matrix containing the coordinates used for the plot. The 

graph can be supplemented in various ways. 

Author(s) 

Daniel Chessel

trichometeo 297 

Examples 

data (euro123) 

tot

298 ungulates 


Data from P. Usseglio-Polatera 

References 

Usseglio-Polatera, P. and Auda, Y. (1987) Influence des facteurs météorologiques sur les résultats 

de piégeage lumineux. Annales de Limnologie, 23, 65–79. (code des espèces p. 76) 


Examples 

data(trichometeo) 

faulog

uniquewt.df 299 


Data were obtained from Pélabon, C., Gaillard, J.M., Loison, A. and Portier, A. (1995) Is sex-biased 

maternal care limited by total maternal expenditure in polygynous ungulates? Behavioral Ecology 

and Sociobiology, 37, 311–319. 

Examples 

data(ungulates) 

ung.phy

300 variance.phylog 

Examples 

data(ecomor) 

forsub.r

vegtf 301 

Value 

Returns a list containing 

lm : an object of class lm that corresponds to the linear regression of z on A. 

anova : an object of class anova that corresponds to the anova of the precedent model. 

smry 

: an object of class table that is a summary of the precedent object. 

Author(s) 



References 

Grafen, A. (1989) The phylogenetic regression. Philosophical Transactions of the Royal Society 

London B, 326, 119–156. 

Diniz-Filho, J. A. F., Sant’Ana, C.E.R. and Bini, L.M. (1998) An eigenvector method for estimating 

phylogenetic inertia. Evolution, 52, 1247–1262. 

See Also 

phylog, lm 

Examples 

data(njplot) 

njplot.phy

302 veuvage 

Format 


vegtf is a list containing the following objects : 

veg is a data.frame with the abundance values of 80 species (columns) in 337 sites (rows). 

xy is a data.frame with the spatial coordinates of the sites. 

area is data.frame (area) which define the boundaries of the study site. 

nb is a neighborhood object (class nb defined in package spdep) 

Dray, S., Said, S. and Debias, F. (2007) Spatial ordination of vegetation data using a generalization 

of Wartenberg’s multivariate spatial correlation. Journal of vegetation science. in press. 

Examples 

if (require(spdep, quiet=TRUE)){ 

data(vegtf) 

coa1

westafrica 303 

Details 

The columns contain the socioprofessional categories: 

1- Farmers, 2- Craftsmen, 3- Executives and higher intellectual professions, 

4- Intermediate Professions, 5- Others white-collar workers and 6- Manual workers. 


unknown 

Examples 

data(veuvage) 


for (j in 1:6) plot(veuvage$age, veuvage$tab[,j], 

xlab = "âge", ylab = "pourcentage de veufs", 

type = "b", main = names(veuvage$tab)[j]) 

westafrica 

Freshwater fish zoogeography in west Africa 

Description 

This data set contains informations about faunal similarities between river basins in West africa. 

Usage 

data(westafrica) 

Format 

westafrica is a list containing the following objects : 

tab : a data frame with absence/presence of 268 species (rows) at 33 embouchures (columns) 

spe.names : a vector of string of characters with the name of species 

spe.binames : a data frame with the genus and species (columns) of the 256 species (rows) 

riv.names : a vector of string of characters with the name of rivers 

atlantic : a data frame with the coordinates of a polygon that represents the limits of atlantic (see 

example) 

riv.xy : a data frame with the coordinates of embouchures 

lines : a data frame with the coordinates of lines to complete the representation (see example) 

cadre : a data frame with the coordinates of points used to make the representation (see example)

304 westafrica 


Data provided by B. Hugueny 〈hugueny@biomserv.univ-lyon1.fr〉. 

Paugy, D., Traoré, K. and Diouf, P.F. (1994) Faune ichtyologique des eaux douces d’Afrique de 

l’Ouest. In Diversité biologique des poissons des eaux douces et saumâtres d’Afrique. Synthèses 

géographiques, Teugels, G.G., Guégan, J.F. and Albaret, J.J. (Editors). Annales du Musée Royal de 

l’Afrique Centrale, Zoologie, 275, Tervuren, Belgique, 35–66. 

Hugueny, B. (1989) Biogéographie et structure des peuplements de Poissons d’eau douce de l’Afrique 

de l’ouest : approches quantitatives. Thèse de doctorat, Université Paris 7. 

References 

Hugueny, B., and Lévêque, C. (1994) Freshwater fish zoogeography in west Africa: faunal similarities 

between river basins. Environmental Biology of Fishes, 39, 365–380. 

Examples 

data(westafrica) 

s.label(westafrica$cadre, xlim = c(30,500), ylim = c(50,290), 

cpoi = 0, clab = 0, grid = FALSE, addax = 0) 

old.par

within 305 

afri.ms

306 within 

Value 

Returns a list of the sub-class within in the class dudi 

call 

origine 

nf 

number of axis saved 

rank 

rank 

ratio percentage of within inertia 

eig 

numeric eigen values 

lw 

numeric row weigths 

cw 

numeric col weigths 

tabw 

numeric table weigths 

fac 

factor for grouping 

tab 

data frame class-variables 

li 

data frame row coordinates 

l1 

data frame row normed scores 

co 

data frame column coordinates 

$c1 data frame column normed scores 

ls 

data frame supplementary row coordinates 

as 

data frame inertia axis onto within axis 

Author(s) 



References 

Benzécri, J. P. (1983) Analyse de l’inertie intra-classe par l’analyse d’un tableau de correspondances. 

Les Cahiers de l’Analyse des données, 8, 351–358. 

Dolédec, S. and Chessel, D. (1987) Rythmes saisonniers et composantes stationnelles en milieu 

aquatique I- Description d’un plan d’observations complet par projection de variables. Acta Oecologica, 

Oecologia Generalis, 8, 3, 403–426. 

Examples 



pca1

withinpca 307 

withinpca 

Normed within Principal Component Analysis 

Description 

Usage 

performs a normed within Principal Component Analysis. 

withinpca(df, fac, scaling = c("partial", "total"), 


Arguments 

df 

fac 

a data frame with quantitative variables 

a factor distributing the rows of df in classes 

scaling a string of characters as a scaling option : 

if "partial", for each factor level, the sub-array is centred and normed. 

If "total", for each factor level, the sub-array is centred and the total array is then 

normed. 

scannf 

nf 

Value 



returns a list of the sub-class within of class dudi’. See within 

Author(s) 



References 

Bouroche, J. M. (1975) Analyse des données ternaires: la double analyse en composantes principales. 

Thèse de 3ème cycle, Université de Paris VI. 

Examples 


wit1

308 witwit.coa 

witwit.coa 

Internal Correspondence Analysis 

Description 

Usage 

witwit.coa performs an Internal Correspondence Analysis. witwitsepan gives the computation 

and the barplot of the eigenvalues for each separated analysis in an Internal Correspondence 

Analysis. 

witwit.coa(dudi, row.blocks, col.blocks, scannf = TRUE, nf = 2) 

## S3 method for class 'witwit': 


witwitsepan(ww, mfrow = NULL, csub = 2, plot = TRUE) 

Arguments 

dudi 

row.blocks 

col.blocks 

scannf 

nf 

object 


a numeric vector indicating the row numbers for each block of rows 

a numeric vector indicating the column numbers for each block of columns 



an object of class witwit 


ww 

Value 

mfrow 

csub 

plot 

an object of class witwit 

if FALSE, numeric results are returned 

returns a list of class witwit, coa and dudi (see as.dudi) containing 

rbvar 

lbw 

cvar 

cbw 

a data frame with the within variances of the rows of the factorial coordinates 

a data frame with the marginal weighting of the row classes 

a data frame with the within variances of the columns of the factorial coordinates 

a data frame with the marginal weighting of the column classes 

Author(s) 

Daniel Chessel Anne B Dufour 〈dufour@biomserv.univ-lyon1.fr〉 Correction by Campo Elías PARDO 

〈cepardot@cable.net.co〉

worksurv 309 

References 

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) 

coa1

310 yanomama 


Rouanet, H. and Le Roux, B. (1993) Analyse des données multidimensionnelles. Dunod, Paris. 

References 

Le Roux, B. and Rouanet, H. (1997) Interpreting axes in multiple correspondence analysis: method 

of the contributions of points and deviation. Pages 197-220 in B. J. and M. Greenacre, editors. 

Visualization of categorical data, Acamedic Press, London. 

Examples 

data(worksurv) 

acm1

zealand 311 

Examples 


gen

312 zealand 

s.label(cmdscale(d0), lab = as.character(1:13), neig = zealand$neig, 

sub = "Distance routiere", csub = 2) 


sub = "Distance routiere / Cailliez", csub = 2) 


sub = "Distance routiere / Lingoes", csub = 2)

Index 

∗Topic array 

cailliez, 36 

dist.binary, 58 

dist.dudi, 59 

dist.neig, 62 

dist.prop, 63 

dist.quant, 64 

dudi.pco, 86 

is.euclid, 113 

lingoes, 139 

mantel.randtest, 143 

mantel.rtest, 144 

pcoscaled, 192 

quasieuclid, 207 

∗Topic chron 

arrival, 17 

∗Topic datasets 

abouheif.eg, 5 

acacia, 6 

aminoacyl, 10 

apis108, 12 

ardeche, 13 

arrival, 17 

atlas, 19 

atya, 20 

avijons, 21 

avimedi, 23 

aviurba, 24 

bacteria, 25 

banque, 26 

baran95, 27 

bf88, 30 

bordeaux, 32 

bsetal97, 32 

buech, 34 

butterfly, 35 

capitales, 37 

carni19, 38 

carni70, 38 

carniherbi49, 39 

casitas, 40 

chatcat, 43 

chats, 44 

chazeb, 45 

chevaine, 45 

clementines, 46 

cnc2003, 47 

coleo, 51 

corvus, 53 

deug, 54 

doubs, 71 

dunedata, 88 

ecg, 89 

ecomor, 90 

elec88, 91 

escopage, 93 

euro123, 93 

fission, 94 

friday87, 97 

fruits, 97 

ggtortoises, 104 

granulo, 105 

hdpg, 107 

housetasks, 108 

humDNAm, 109 

ichtyo, 110 

irishdata, 112 

julliot, 114 

jv73, 116 

kcponds, 117 

lascaux, 138 

lizards, 140 

macaca, 141 

macon, 142 

mafragh, 142 

maples, 145 

mariages, 146 

meau, 149 

313

314 INDEX 

meaudret, 150 

microsatt, 152 

mjrochet, 154 

mollusc, 156 

monde84, 157 

morphosport, 158 

newick.eg, 168 

njplot, 173 

olympic, 174 

oribatid, 176 

ours, 185 

palm, 187 

pap, 188 

perthi02, 193 

presid2002, 199 

procella, 200 

rankrock, 215 

rhone, 217 

rpjdl, 220 

santacatalina, 247 

sarcelles, 248 

seconde, 265 

skulls, 267 

steppe, 269 

syndicats, 274 

t3012, 274 

tarentaise, 280 

taxo.eg, 282 

tintoodiel, 284 

tithonia, 285 

tortues, 286 

toxicity, 287 

trichometeo, 291 

ungulates, 292 

vegtf, 295 

veuvage, 296 

westafrica, 297 

worksurv, 303 

yanomama, 304 

zealand, 305 

∗Topic hplot 

add.scatter, 7 

area.plot, 14 

dotchart.phylog, 68 

dotcircle, 70 

kplot, 123 

kplot.foucart, 123 

kplot.mcoa, 124 

kplot.mfa, 125 

kplot.pta, 126 

kplot.sepan, 127 

kplot.statis, 129 

plot.phylog, 196 

s.arrow, 224 

s.chull, 225 

s.class, 227 

s.corcircle, 229 

s.distri, 230 

s.hist, 232 

s.image, 233 

s.kde2d, 235 

s.label, 236 

s.logo, 238 

s.match, 240 

s.multinom, 241 

s.traject, 243 

s.value, 245 

scatter, 250 

scatter.acm, 252 

scatter.coa, 252 

scatter.dudi, 253 

scatter.fca, 255 

sco.boxplot, 256 

sco.distri, 257 

sco.quant, 258 

score, 259 

score.acm, 260 

score.coa, 261 

score.mix, 263 

score.pca, 264 

symbols.phylog, 273 

table.cont, 275 

table.dist, 276 

table.paint, 277 

table.phylog, 278 

table.value, 279 

triangle.class, 288 

triangle.plot, 289 

∗Topic internal 

randtest-internal, 209 

∗Topic manip 

as.taxo, 18 

newick2phylog, 169 

phylog, 194 

PI2newick, 2 

∗Topic methods

INDEX 315 

krandtest, 130 

randtest, 210 

rtest, 221 

∗Topic models 

variance.phylog, 294 

∗Topic multivariate 

add.scatter, 7 

amova, 11 

between, 29 

cca, 41 

coinertia, 49 

disc, 55 

discrimin, 56 

discrimin.coa, 57 

dist.binary, 58 

dist.dudi, 59 

dist.genet, 60 

dist.neig, 62 

dist.prop, 63 

dist.quant, 64 

divc, 66 

divcmax, 67 

dpcoa, 72 

dudi, 74 

dudi.acm, 75 

dudi.coa, 77 

dudi.dec, 78 

dudi.fca, 79 

dudi.hillsmith, 81 

dudi.mix, 82 

dudi.nsc, 84 

dudi.pca, 85 

dudi.pco, 86 

EH, 1 

foucart, 95 

fuzzygenet, 99 

genet, 101 

inertia.dudi, 111 

kdist, 118 

kdist2ktab, 120 

kdisteuclid, 121 

kplot, 123 







ktab, 131 

ktab.data.frame, 133 

ktab.list.df, 134 

ktab.list.dudi, 135 

ktab.match2ktabs, 136 

ktab.within, 137 

lingoes, 139 

mcoa, 147 

mfa, 151 

multispati, 160 

multispati.randtest, 163 

multispati.rtest, 164 

niche, 171 

optimEH, 175 

originality, 177 

orisaved, 179 

pcaiv, 189 

pcaivortho, 190 

procuste, 201 

procuste.randtest, 203 

procuste.rtest, 204 

pta, 205 

randEH, 208 

randtest.amova, 211 

randtest.between, 212 

randtest.coinertia, 213 

randtest.discrimin, 214 

reconst, 215 

rlq, 218 

rtest.between, 222 

rtest.discrimin, 223 

RV.rtest, 3 

RVdist.randtest, 4 

s.arrow, 224 

s.chull, 225 

s.class, 227 

s.corcircle, 229 

s.distri, 230 

s.hist, 232 

s.kde2d, 235 

s.label, 236 

s.logo, 238 

s.match, 240 

s.multinom, 241 

s.traject, 243 

s.value, 245 

scatter, 250 

scatter.acm, 252

316 INDEX 

scatter.coa, 252 

scatter.dudi, 253 

scatter.fca, 255 

sco.boxplot, 256 

sco.distri, 257 

sco.quant, 258 

score, 259 





sepan, 265 

statis, 268 

supcol, 270 

suprow, 271 

testdim, 283 

within, 299 

withinpca, 301 

witwit.coa, 302 

∗Topic nonparametric 

corkdist, 52 

mantel.randtest, 143 

mantel.rtest, 144 



procuste.randtest, 203 

procuste.rtest, 204 

randtest.amova, 211 




rtest.between, 222 

rtest.discrimin, 223 

RV.rtest, 3 

RVdist.randtest, 4 

∗Topic spatial 

gearymoran, 100 

gridrowcol, 106 

mld, 155 

multispati, 160 



orthobasis, 180 

orthogram, 183 

rlq, 218 

∗Topic ts 

gearymoran, 100 

mld, 155 

orthobasis, 180 

orthogram, 183 

∗Topic utilities 

ade4toR, 9 

bicenter.wt, 31 


mstree, 159 

neig, 165 

scalewt, 249 

uniquewt.df, 293 

[.kdist (kdist), 118 

[.ktab (ktab), 131 

abouheif.eg, 5 

acacia, 6 

acm.burt (dudi.acm), 75 

acm.disjonctif (dudi.acm), 75 

add.position.triangle 

(triangle.plot), 289 

add.scatter, 7, 251 

ade4toR, 9 

aminoacyl, 10 

amova, 11 

apis108, 12 

ardeche, 13 

area.plot, 14, 166 

area.util.class (area.plot), 14 

area.util.contour (area.plot), 14 

area.util.xy (area.plot), 14 

area2link (area.plot), 14 

area2poly (area.plot), 14 

arrival, 17 

as.data.frame.kdist (kdist), 118 

as.dudi, 302 

as.dudi (dudi), 74 

as.krandtest (krandtest), 130 

as.randtest (randtest), 210 

as.rtest (rtest), 221 

as.taxo, 18, 170 

atlas, 19 

atya, 20 

avijons, 21 

avimedi, 23 

aviurba, 24 

bacteria, 25 

banque, 26 

baran95, 27 

between, 29

INDEX 317 

bf88, 30 

bicenter.wt, 31 

bordeaux, 32 

boxplot.acm (dudi.acm), 75 

bsetal97, 32 

buech, 34 

butterfly, 35 

c.kdist (kdist), 118 

c.ktab (ktab), 131 

cailliez, 36 

capitales, 37 

carni19, 38 

carni70, 38 

carniherbi49, 39 

casitas, 40 

cca, 41, 42 

char2genet, 100 

char2genet (genet), 101 

chatcat, 43 

chats, 44 

chazeb, 45 

chevaine, 45 

circ.plot, 70 

clementines, 46 

cnc2003, 47 

coinertia, 49, 219 

col.names (ktab), 131 

col.names

318 INDEX 

is.ktab (ktab), 131 

julliot, 114 

jv73, 116 

kcponds, 117 

kdist, 118 

kdist2ktab, 120 


kplot, 123 







krandtest, 130 

ktab, 131, 133–137 

ktab.data.frame, 132, 133 

ktab.list.df, 132, 134 

ktab.list.dudi, 132, 135 

ktab.match2ktabs, 132, 136 

ktab.util.addfactor

INDEX 319 

plot.procuste (procuste), 201 

plot.pta (pta), 205 

plot.randtest (randtest), 210 

plot.rlq (rlq), 218 

plot.rtest (rtest), 221 

plot.sepan (sepan), 265 

plot.statis (statis), 268 

plot.within (within), 299 

poly2area (area.plot), 14 

prep.fuzzy.var (dudi.fca), 79 

presid2002, 199 

print.amova (amova), 11 

print.between (between), 29 

print.coinertia (coinertia), 49 

print.corkdist (corkdist), 52 

print.discrimin (discrimin), 56 

print.dpcoa (dpcoa), 72 

print.dudi (dudi), 74 

print.foucart (foucart), 95 

print.kdist (kdist), 118 

print.krandtest (krandtest), 130 

print.ktab (ktab), 131 

print.mcoa (mcoa), 147 

print.mfa (mfa), 151 

print.multispati (multispati), 160 

print.neig (neig), 165 

print.niche (niche), 171 

print.orthobasis (orthobasis), 180 

print.pcaiv (pcaiv), 189 

print.phylog (phylog), 194 

print.procuste (procuste), 201 

print.pta (pta), 205 

print.randtest (randtest), 210 

print.rlq (rlq), 218 

print.rtest (rtest), 221 

print.sepan (sepan), 265 

print.statis (statis), 268 

print.within (within), 299 

procella, 200 

procuste, 201 

procuste.randtest, 203, 210 

procuste.rtest, 204, 222 

pta, 136, 205 

quasieuclid, 207 

radial.phylog (plot.phylog), 196 

randEH, 176, 208 

randtest, 131, 210, 222 

randtest-internal, 209 

randtest.amova, 12, 211 




randtest.rlq (rlq), 218 

rankrock, 215 

reciprocal.coa (score.coa), 261 

reconst, 215 

redo.dudi (dudi), 74 

rhone, 217 

rlq, 218 

row.names.ktab (ktab), 131 

row.names

320 INDEX 

scatterutil.eigen (scatter), 250 

scatterutil.ellipse (scatter), 250 

scatterutil.eti (scatter), 250 

scatterutil.grid (scatter), 250 

scatterutil.legend.bw.square 

(scatter), 250 

scatterutil.legend.square.grey 

(scatter), 250 

scatterutil.legendgris (scatter), 

250 

scatterutil.logo (s.logo), 238 

scatterutil.scaling (scatter), 250 

scatterutil.star (scatter), 250 

scatterutil.sub (scatter), 250 

sco.boxplot, 256, 260 

sco.distri, 257, 260 

sco.quant, 258, 260 

score, 259 





scores.neig (neig), 165 

scoreutil.base (score), 259 

seconde, 265 

sepan, 265 

skulls, 267 

statis, 268 

steppe, 269 

summary.coinertia (coinertia), 49 

summary.corkdist (corkdist), 52 

summary.dist (is.euclid), 113 

summary.mcoa (mcoa), 147 

summary.mfa (mfa), 151 

summary.multispati (multispati), 

160 

summary.neig (neig), 165 

summary.rlq (rlq), 218 

summary.sepan (sepan), 265 

summary.witwit (witwit.coa), 302 

supcol, 270 

suprow, 271 

symbols.phylog, 69, 273, 279 

syndicats, 274 

t.dudi (dudi), 74 

t.ktab (ktab), 131 

t3012, 274 

tab.names (ktab), 131 

tab.names

INDEX 321 

wavelet.filter, 181 

westafrica, 297 

within, 299, 301 

withinpca, 301 

witwit.coa, 302 

witwitsepan (witwit.coa), 302 

worksurv, 303 

yanomama, 304 

zealand, 305

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