R : Copyright 2005, The R Foundation for Statistical Computing
Version 2.1.1  (2005-06-20), ISBN 3-900051-07-0

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> ### * <HEADER>
> ###
> attach(NULL, name = "CheckExEnv")
> assign(".CheckExEnv", as.environment(2), pos = length(search())) # base
> ## add some hooks to label plot pages for base and grid graphics
> setHook("plot.new", ".newplot.hook")
> setHook("persp", ".newplot.hook")
> setHook("grid.newpage", ".gridplot.hook")
> 
> assign("cleanEx",
+        function(env = .GlobalEnv) {
+ 	   rm(list = ls(envir = env, all.names = TRUE), envir = env)
+            RNGkind("default", "default")
+ 	   set.seed(1)
+    	   options(warn = 1)
+ 	   delayedAssign("T", stop("T used instead of TRUE"),
+ 		  assign.env = .CheckExEnv)
+ 	   delayedAssign("F", stop("F used instead of FALSE"),
+ 		  assign.env = .CheckExEnv)
+ 	   sch <- search()
+ 	   newitems <- sch[! sch %in% .oldSearch]
+ 	   for(item in rev(newitems))
+                eval(substitute(detach(item), list(item=item)))
+ 	   missitems <- .oldSearch[! .oldSearch %in% sch]
+ 	   if(length(missitems))
+ 	       warning("items ", paste(missitems, collapse=", "),
+ 		       " have been removed from the search path")
+        },
+        env = .CheckExEnv)
> assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now
> assign("ptime", proc.time(), env = .CheckExEnv)
> grDevices::postscript("ade4-Examples.ps")
> assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv)
> options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly"))
> options(warn = 1)    
> library('ade4')
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "PI2newick"
> 
> ### * PI2newick
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: PI2newick
> ### Title: Import data files from Phylogenetic Independance Package
> ### Aliases: PI2newick
> ### Keywords: manip
> 
> ### ** Examples
> 
> x <- c(2.0266, 0.5832, 0.2460, 1.2963, 0.2460, 0.1565, -99.0000,
+         -99.0000, 10.1000, -99.0000,  20.2000,  28.2000, -99.0000, 
+         14.1000, 11.2000, -99.0000, 21.3000, 27.5000, 1.0000, 2.0000,
+         -1.0000, 4.0000, -1.0000, -1.0000, 3.0000, -1.0000, -1.0000,
+         5.0000, -1.0000, -1.0000, 0.0000, 0.0000, 0.0000, 0.0000,
+         0.0000, 0.0000)
> x <- matrix(x, nrow = 6)
> x <- as.data.frame(x)
> res <- PI2newick(x)
> dotchart.phylog(newick2phylog(res$tre), res$trait)
> 
> 
> 
> cleanEx(); ..nameEx <- "RV.rtest"
> 
> ### * RV.rtest
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: RV.rtest
> ### Title: Monte-Carlo Test on the sum of eigenvalues of a co-inertia
> ###   analysis (in R).
> ### Aliases: RV.rtest
> ### Keywords: multivariate nonparametric
> 
> ### ** Examples
> 
> data(doubs)
> pca1 <- dudi.pca(doubs$mil, scal = TRUE, scann = FALSE)
> pca2 <- dudi.pca(doubs$poi, scal = FALSE, scann = FALSE)
> rv1 <- RV.rtest(pca1$tab, pca2$tab, 99)
> rv1
Monte-Carlo test
Observation: 0.4505569 
Call: RV.rtest(df1 = pca1$tab, df2 = pca2$tab, nrepet = 99)
Based on 99 replicates
Simulated p-value: 0.01 
> plot(rv1)
> 
> 
> 
> cleanEx(); ..nameEx <- "RVdist.randtest"
> 
> ### * RVdist.randtest
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: RVdist.randtest
> ### Title: Tests of randomization on the correlation between two distance
> ###   matrices (in R).
> ### Aliases: RVdist.randtest
> ### Keywords: multivariate nonparametric
> 
> ### ** Examples
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "abouheif.eg"
> 
> ### * abouheif.eg
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: abouheif.eg
> ### Title: Phylogenies and quantitative traits from Abouheif
> ### Aliases: abouheif.eg
> ### Keywords: datasets
> 
> ### ** 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)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "acacia"
> 
> ### * acacia
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: acacia
> ### Title: Spatial pattern analysis in plant communities
> ### Aliases: acacia
> ### Keywords: datasets
> 
> ### ** 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))
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "ade4toR"
> 
> ### * ade4toR
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ade4toR
> ### Title: Format Change Utility
> ### Aliases: ade4toR Rtoade4
> ### Keywords: utilities
> 
> ### ** Examples
> 
> data(tarentaise)
> traits <- tarentaise$traits
> Rtoade4(traits)
File creation traits.txt 
File creation traits_col_lab.txt 
File creation traits_row_lab.txt 
File creation traits_col_bloc.txt 
File creation traits_col_bloc_lab.txt 
> # File creation traits.txt 
> # File creation traits_col_lab.txt 
> # File creation traits_row_lab.txt 
> # File creation traits_col_bloc.txt 
> # File creation traits_col_bloc_lab.txt  
> 
> mil <- tarentaise$envir
> Rtoade4(mil)
File creation mil.txt 
File creation mil_var_lab.txt 
File creation mil_moda_lab.txt 
> #File creation mil.txt 
> #File creation mil_var_lab.txt 
> #File creation mil_moda_lab.txt 
> 
> 
> 
> cleanEx(); ..nameEx <- "aminoacyl"
> 
> ### * aminoacyl
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: aminoacyl
> ### Title: Codon usage
> ### Aliases: aminoacyl
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(aminoacyl)
> aminoacyl$genes
 [1] "YCR024C" "YDR268W" "YGR094W" "YGR171C" "YHR011W" "YHR091C" "YKL194C"
 [8] "YLR382C" "YNL073W" "YOL033W" "YPL040C" "YPL097W" "YPL104W" "YPR033C"
[15] "YPR047W" "YNL247W" "YPR081C" "YBL076C" "YBR121C" "YDR023W" "YDR037W"
[22] "YDR341C" "YER087W" "YFL022C" "YGL245W" "YGR185C" "YGR264C" "YHR019C"
[29] "YIL078W" "YLR060W" "YOL097C" "YOR335C" "YPL160W" "YHR020W" "YOR168W"
[36] "YLL018C"
> aminoacyl$usage.codon
    YCR024C YDR268W YGR094W YGR171C YHR011W YHR091C YKL194C YLR382C YNL073W
GCT       4       8      46       9       9       3      12      19       3
GCC       4       4      16       5       6      13       4       6       3
GCA       7       7       7       3      12      14       2      21       9
GCG       2       4       2       2       5       2       2       3       6
CGT       3       3      13       4       1       9       4       5       3
CGC       2       3       1       0       3       4       3       3       2
CGA       1       0       1       5       2       3       1       4       2
CGG       1       0       1       0       1       2       0       0       0
AGA      13       8      20      12       7      15      10      17      11
AGG       3       3       6       7       7       4       5       6      12
AAT      17      11      23      24      19      23      17      30      19
AAC       7       7      26      10       8      15      12      17      16
GAT      13      17      45      20      15      21      17      34      18
GAC       5       5      24      11       6      16      11      17      13
TGT       5       2      10       6       5       3       2       6       4
TGC       3       0       3       2       3       3       3       6       5
CAA      18       9      29      20      14      16      12      22      17
CAG       5       6       4       6       8      16       7      12      10
GAA      24      18      73      24      17      19      22      45      20
GAG       9       4      19      11      16       8       9      11      15
GGT       8       3      49       9       6      10      13      12       9
GGC       3       8       7      11       3       8       3      10      11
GGA       8       4       2       7      10      10       5      14       5
GGG       5       2       1       1       3       9       1      11       3
CAT       5       5      17      10       3      10      10       7       6
CAC       9       7       7       5       1       5       2       9       3
ATT      11      12      46      22      13      17      15      30      15
ATC       5       6      27       6       7       7       7      10       9
ATA      15       5       1      16      15      22       7      20      12
TTA      20      11      30      15      11      21       9      21      16
TTG      10       8      42      13      10      12      10      21      19
CTT       5       7       8       4       5      12       7       5       8
CTC       3       0       2       6       2       2       3       7       4
CTA       9       3       6       8       8      14       7       7       8
CTG       4       6       8       8       6       5       6      10      17
AAA      24      24      50      35      26      34      23      58      16
AAG      10      11      63      13      16      13      18      22      32
ATG       8      10      16       9      10      20      14      24      12
TTT      21      11      19      15       9      17      15      28      19
TTC       9       5      24      14       5      16      16      18      11
CCT       9       9      17       9       6       5       7      20      11
CCC       3       0       2       3       1       4       4       9       6
CCA      11       6      31      11      10      10       8      14       3
CCG       2       2       2       5       2       3       4      11       7
TCT       9       7      27       9       4      10      13      14       6
TCC       9       4      16      16       1      10       4       6       9
TCA      14       3       6       8      10      13       3      13       9
TCG       5       8       5       4       4       9       6       4       6
AGT       4       8       6       7       6       4       1      12       9
AGC       3       3       7       4       6       5       1      10       5
TAA       0       0       0       0       1       1       1       1       1
TAG       0       0       1       0       0       0       0       0       0
TGA       1       1       0       1       0       0       0       0       0
ACT      15       5      35       6       4       5      11      18       9
ACC       9       1      17       4       7       6       0      11       5
ACA       8       7       8       8       6       8       4      11       5
ACG       3       4       2       5       5       6       5       6      10
TGG       8       4      25      11       4       8      10      19       4
TAT      13       8      16      16       6      15       7      16       9
TAC       7       4      19       9       9       9       8      11      11
GTT      12      12      45      15       8      14       7      18       9
GTC       3       4      17       3       2       6       2      14       9
GTA       3       9       6       8       9      11       6      15       7
GTG       4       4       1       6       3       9       5      14       4
    YOL033W YPL040C YPL097W YPL104W YPR033C YPR047W YNL247W YPR081C YBL076C
GCT       8      12      12      12      29       5      23       6      26
GCC       5       8       3      11      10       4      14       4      18
GCA       9      17       3       9      10       1      11       9      11
GCG       5       5       5       3       3       3       1       2       1
CGT       2       4       3       3       4       4       3       3      15
CGC       1       7       0       1       0       3       3       2       1
CGA       3       4       5       2       1       1       0       5       0
CGG       3       0       2       2       0       0       0       1       1
AGA      14      18       7      17      11      16      16      16      28
AGG       6      13      11       9       6       4       2       9       1
AAT      12      29      19      26      11      16      27      22      19
AAC      14      22      10      11       5      11      23      12      27
GAT      22      36      20      25      24      23      34      23      48
GAC       6      16       9      14      11       9      19      13      30
TGT      10      11       6       3       5       1       4       6       9
TGC       1       3       1       5       0       5       2       2       0
CAA       9      32      11      13      12       9      28      13      20
CAG       6      17      11      10       4       3       9      12       4
GAA      22      53      15      28      31      24      38      32      59
GAG      13      14       5      18       9      11      15      14      20
GGT      11      12      12      14      30       2      17      15      43
GGC       4      12       5       7       7       6      11       4       7
GGA       7      18      13       7       2       8       5      10       6
GGG       6       5       4       4       1       3       1       8       1
CAT       9      25       5      10       2       9      15       9      13
CAC       7       9       3       2       4       5       1       4       7
ATT       9      29      13      18      20      16      29      22      44
ATC       8      20      11      13      15       8      19      10      20
ATA      18      23      11      12       4      11       3      12       3
TTA      15      34      16      16      14       9      25      16      34
TTG      22      26      16      20      18      12      28      23      46
CTT      10      16       6       4       4       8       3       4       7
CTC       1      10       4       6       1       4       0       2       1
CTA      10      23       8       8       6       6       8       7       7
CTG       7      10       5       8       1       4       2       6       4
AAA      28      50      23      37      25      26      40      29      50
AAG      23      29      11      24      29      14      37      17      43
ATG      12      15       7      15      12      14      12      18      18
TTT      21      25      15      31      10      13      23      24      22
TTC       5      13       7       8      10       6      15      15      31
CCT      12      11       6       8       7       6       9       8      14
CCC       3       9       5       4       1       4       5       4       2
CCA       9      13       5      18       7       7      13       8      28
CCG       2       8       0       4       0       3       3       5       1
TCT      17      14       8      11      21       9      14       5      26
TCC       3      15       6       6       8       4      16       7      21
TCA       6      20      11       9       9      14       9      10      12
TCG       6       6       4       4       2       0       4      11       1
AGT       3      11      11      12       5       3       6       6       8
AGC       4      13       3       3       1       3       5       1       3
TAA       1       0       1       0       1       0       0       1       0
TAG       0       0       0       0       0       1       1       0       0
TGA       0       1       0       1       0       0       0       0       1
ACT       5      17       9       7      14       9      19      16      25
ACC       7      11       1       8       5       7      11       5      17
ACA       5      16       9      10       7       9       8      10      12
ACG       7       8       6       5       2       4       5       7       4
TGG       5      18       3       5       4      10      14       6      22
TAT      15      25      12      13      10      10       9       5      25
TAC       5      17       7       9       7       5      12       8      22
GTT       6      19       9      12      19      10      19      10      44
GTC       6       9       4       5      10       4       9       6      24
GTA       4       8      10       8       2      11       3       9       5
GTG       2       9      10      11       4       5       8      10      11
    YBR121C YDR023W YDR037W YDR341C YER087W YFL022C YGL245W YGR185C YGR264C
GCT      25      16      21      18      10      10      35      11      22
GCC      17       6      14      10       3      12      13       9      11
GCA       7       1       1      11       6       4       3       6      13
GCG       0       1       1       4       5       1       1       5       1
CGT       5       4       9       7       4       4      10       0      10
CGC       2       0       1       1       2       0       0       0       1
CGA       0       0       0       0       3       0       0       0       0
CGG       0       0       0       0       1       0       0       0       0
AGA      26      12      23      19      12      15      23       8      12
AGG       3       1       2       1       6       0       3       2       1
AAT      13      11      10      13      24       9      15       9      30
AAC      10      12      11      13      11      17      21      12      24
GAT      29      17      24      22      31      15      43      10      30
GAC      20       7      17      12      10      20      21       8      15
TGT       6       5       8       2      11       1       6       3      10
TGC       0       3       1       1       4       0       1       1       0
CAA      15      17      24      20      11      20      16      15      24
CAG       1       4       2       4      11       5       0       2       3
GAA      52      40      46      36      27      26      44      23      44
GAG       9      11       9      14      10      12       2       7       7
GGT      30      21      29      28      14      21      24      17      29
GGC       5       2       2       7       8       2       6       4       2
GGA       4       3       1       3      11       1       6       1       5
GGG       2       0       1       0       3       4       1       1       1
CAT      12       5       7       7       8       4       4       3      17
CAC       3       4       9       7       5       8      10       2       5
ATT      21      12      17      22      13       9      28       9      23
ATC      14      13      10      18      11      17      18      12      15
ATA       2       1       1       1      14       1       4       0       3
TTA      18      17      12      24      17       7      19      10      22
TTG      32      17      27      18      15      23      32      19      34
CTT       3       2       5       6       5       9       1       4       4
CTC       0       1       1       1       2       4       1       2       1
CTA       2       4       5       6       6       7       7       5       8
CTG       1       0       2       6      11       8       1       1       6
AAA      30      24      23      29      35      20      27      21      34
AAG      30      26      25      22      15      21      49      21      28
ATG      12       6      20      19      16      14      17       9      10
TTT      22       7      13      15      18       7       8      12      18
TTC      11      13      17      14       5      19      21       8      14
CCT       7       3       7       5       6       8       8       6       9
CCC       0       1       0       3       4       4       2       2       1
CCA      23      15      23      11       9       8      20      17      24
CCG       0       0       3       1       1       0       0       1       0
TCT      19      12      11      15       5      11      13       5      22
TCC      11      10       7       5       4      12       6       5       7
TCA       4       3       3       5      14       3       5       3      10
TCG       0       0       1       3       6       4       1       1       5
AGT       5       3       0       1      11       0       2       3       7
AGC       0       1       1       2       5       1       3       2       3
TAA       1       1       1       1       0       1       1       1       1
TAG       0       0       0       0       1       0       0       0       0
TGA       0       0       0       0       0       0       0       0       0
ACT      13       7      12      11       8       5      20       5      11
ACC       9       4      10       5       4      16      13       8       2
ACA       4       1       7       8       8       6       2       2      12
ACG       2       1       0       1       2       3       2       2       7
TGG       7       6       2       9      10       6      12       1      13
TAT      10       8       7      13       8       3       8       7      20
TAC       8      13      13      11      10      12      15       6      15
GTT      33      15      15      24      10       9      22      14      32
GTC      17       8      14       8       7      12      22       8       7
GTA       1       4       3       3       6       0       1       1       1
GTG       0       1       1       2       4       3       6       3       6
    YHR019C YIL078W YLR060W YOL097C YOR335C YPL160W YHR020W YOR168W YLL018C
GCT      20      17      16       6      39      43      25      20      21
GCC      12      17      16       7      19      24      17       7      14
GCA       9       3       3       7       8      24       7      15       8
GCG       0       2       2       2       2       6       0       6       0
CGT       9      10       7       4      12      15       5       3      10
CGC       1       0       0       1       1       1       2       0       1
CGA       0       0       0       0       0       0       0       0       0
CGG       0       0       0       0       0       0       0       0       0
AGA      17      27      17      10      20      20      26      14      19
AGG       0       2       1       1       2       5       3       5       6
AAT       4      12      14       7      27      18      13      15       4
AAC       8      20      18       4      23      21      10      12      12
GAT      11      30      22      20      49      50      29      16      10
GAC      26      14      18      15      24      14      20       9      23
TGT       8       9       8       5       6      11       6       7       3
TGC       1       3       1       1       1       2       0       2       0
CAA      20      23      15      19      23      31      15      14      18
CAG       4       4       3       3       3       6       1       1       5
GAA      34      56      31      22      66      81      52      29      43
GAG      10       8      16       6      12      18       9       8      14
GGT      27      31      18      17      60      42      30      17      27
GGC       6       5       6       4      10       9       5       3       1
GGA       3       6       0       2       3       3       4      13       0
GGG       1       5       4       2       3       6       2       3       4
CAT       8      13       7       7      11      12      12       9       4
CAC       2       6       8       2       5       8       3       5       8
ATT      13      21      20      11      29      46      21      24      16
ATC      14      13      19       6      24      22      14      11      12
ATA       1       1       4       2       3       6       2       9       0
TTA       7      21      15       7      20      28      18      13       6
TTG      35      31      25      16      38      30      32      21      36
CTT       1       1       5       3       8       5       3       8       3
CTC       2       0       0       0       1       1       0       1       2
CTA       5       6       8       4       6       9       2       9       7
CTG       4       3       2       3       6       4       1       4       2
AAA      13      32      23      18      39      47      30      24      16
AAG      30      33      23      26      52      53      30      15      32
ATG      11      22      13       8      10      27      12      16      11
TTT       7      18      13      16      26      30      22      10      12
TTC      15      21      16      19      27      29      11       5      16
CCT       3       4       7       7      11      19       7      11       7
CCC       2       0       0       1       4       1       0       3       5
CCA      17      22      21      12      22      29      23      15      14
CCG       0       2       2       1       1       3       0       3       0
TCT      13      28      11       3      25      28      19      16       7
TCC      13       7      13       9      11       6      11       9      10
TCA       3       3       5       4       8      12       4       6       2
TCG       1       2       4       2       1       2       0       1       1
AGT       2       6       2       1       3       3       5       5       5
AGC       3       1       3       5       2       5       0       2       3
TAA       0       1       1       1       1       1       1       1       0
TAG       0       0       0       0       0       0       0       0       0
TGA       1       0       0       0       0       0       0       0       1
ACT      12      18       9       9      26      23      13       9       5
ACC      17      10      14      10      13      15       9       4      13
ACA       5       2       4       6       8      13       4      12       6
ACG       2       0       3       2       1       6       2       6       1
TGG       4      13       4       3       8      13       7       1       1
TAT      10       8       4       3      13      26      10       9       6
TAC      13      16      13       9      22      26       8       7      12
GTT      13      24      17      12      36      25      33      18      11
GTC      15      18      12       8      16      17      17      11      11
GTA       4       3       6       5       3       8       1      10       4
GTG       3       1       4       7       6       3       0      10       7
> dudi.coa(aminoacyl$usage.codon, scannf = FALSE)
Duality diagramm
class: coa dudi
$call: dudi.coa(df = aminoacyl$usage.codon, scannf = FALSE)

$nf: 2 axis-components saved
$rank: 34
eigen values: 0.07774 0.01301 0.008387 0.007261 0.006383 ...
  vector length mode    content       
1 $cw    36     numeric column weights
2 $lw    64     numeric row weights   
3 $eig   34     numeric eigen values  

  data.frame nrow ncol content             
1 $tab       64   36   modified array      
2 $li        64   2    row coordinates     
3 $l1        64   2    row normed scores   
4 $co        36   2    column coordinates  
5 $c1        36   2    column normed scores
other elements: N 
> 
> 
> 
> cleanEx(); ..nameEx <- "amova"
> 
> ### * amova
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: amova
> ### Title: Analysis of molecular variance
> ### Aliases: amova print.amova
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(humDNAm)
> amovahum <- amova(humDNAm$samples, sqrt(humDNAm$distances), humDNAm$structures)
> amovahum
$call
amova(samples = humDNAm$samples, distances = sqrt(humDNAm$distances), 
    structures = humDNAm$structures)

$results
                                Df     Sum Sq    Mean Sq
Between regions                  4  78.238115 19.5595288
Between samples Within regions   5   9.284744  1.8569488
Within samples                 662 316.197379  0.4776395
Total                          671 403.720238  0.6016695

$componentsofcovariance
                                                Sigma          %
Variations  Between regions                0.13380659  21.119144
Variations  Between samples Within regions 0.02213345   3.493396
Variations  Within samples                 0.47763955  75.387459
Total variations                           0.63357958 100.000000

$statphi
                          Phi
Phi-samples-total   0.2461254
Phi-samples-regions 0.0442870
Phi-regions-total   0.2111914

> 
> 
> 
> cleanEx(); ..nameEx <- "apis108"
> 
> ### * apis108
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: apis108
> ### Title: Allelic frequencies in ten honeybees populations at eight
> ###   microsatellites loci
> ### Aliases: apis108
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(apis108)
> apis <- count2genet(as.data.frame(t(apis108)))
> apis.pca <- dudi.pca(apis$tab, center = apis$center,
+     scale = FALSE, scannf = FALSE, nf = 3)
> 
> 
> 
> cleanEx(); ..nameEx <- "ardeche"
> 
> ### * ardeche
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ardeche
> ### Title: Fauna Table with double (row and column) partitioning
> ### Aliases: ardeche
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(ardeche)
> dudi1 <- dudi.coa(ardeche$tab, scan = FALSE)
> s.class(dudi1$co, ardeche$dat.fac)
> s.label(dudi1$co, clab = 0.5, add.p = TRUE)
> 
> 
> 
> cleanEx(); ..nameEx <- "area.plot"
> 
> ### * area.plot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: area.plot
> ### Title: Graphical Display of Areas
> ### Aliases: area.plot poly2area area2poly area2link area.util.contour
> ###   area.util.xy area.util.class
> ### Keywords: hplot
> 
> ### ** Examples
> 
> data(elec88)
> par(mfrow = c(2,2))
> area.plot(elec88$area, cpoint = 1)
> area.plot(elec88$area, lab = elec88$lab, clab = 0.75)
> area.plot(elec88$area, clab = 0.75)
> # elec88$neig <- neig(area = elec88$area)
> area.plot(elec88$area, graph = elec88$neig, 
+ sub = "Neighbourhood graph", possub = "topright")
> par(mfrow = c(1,1))
> 
> ## Not run: 
> ##D par(mfrow = c(3,3))
> ##D for (i in 1:9) {
> ##D     x <- elec88$tab[,i]
> ##D     area.plot(elec88$area, val=x,
> ##D     sub = names(elec88$tab)[i], csub = 3, cleg = 1.5)
> ##D }
> ##D 
> ##D par(mfrow = c(3,3))
> ##D for (i in 1:9) {
> ##D     x <- elec88$tab[,i]
> ##D     s.value(elec88$xy, elec88$tab[,i], contour = elec88$contour,
> ##D     meth = "greylevel", sub = names(elec88$tab)[i], csub = 3, 
> ##D     cleg = 1.5, incl = FALSE)
> ##D }
> ##D 
> ##D data(irishdata)
> ##D par(mfrow = c(2,2))
> ##D w <- area.util.contour(irishdata$area)
> ##D xy <- area.util.xy(irishdata$area)
> ##D area.plot(irishdata$area, cpoint = 1)
> ##D apply(w, 1, function(x) segments(x[1],x[2],x[3],x[4], lwd = 3))
> ##D area.plot(irishdata$area, clabel = 1)
> ##D s.label(xy, area = irishdata$area, incl = FALSE, clab = 0, 
> ##D cpoi = 3, addax = FALSE, contour = w)
> ##D s.label(xy, area = irishdata$area, incl = FALSE, 
> ##D addax = FALSE, contour = w)
> ##D if (require(maptools, quiet = TRUE) & require(spdep, quiet = TRUE)) {
> ##D     data(columbus)
> ##D     par(mfrow = c(2,2))
> ##D     plot(col.gal.nb, coords, pch = 20, cex = 2)
> ##D     col.gal.neig <- nb2neig(col.gal.nb)
> ##D     s.label(data.frame(coords), neig = col.gal.neig,
> ##D     inc = FALSE, addax = FALSE, clab = 0, cneig = 1, cpo = 2)
> ##D      plot.polylist(polys,bbs)
> ##D     area.plot(poly2area(polys))
> ##D 
> ##D     # 1
> ##D     crime.f <- as.ordered(cut(columbus$CRIME,
> ##D     breaks = quantile(columbus$CRIME, probs = seq(0,1,0.2)),
> ##D     include.lowest = TRUE))
> ##D     colours <- c("salmon1", "salmon2", "red3", "brown", "black")
> ##D     plot(bbs[,1], bbs[,4], xlab = "", ylab = "", asp = 1, type = "n",
> ##D     xlim = range(c(bbs[,1], bbs[,3])), ylim = range(c(bbs[,2],
> ##D      bbs[,4])))
> ##D     for (i in 1:length(polys)) 
> ##D     polygon(polys[[i]], col = colours[unclass(crime.f[i])])
> ##D     legend(x = c(6, 7.75), y = c(13.5, 15), legend = levels(crime.f),
> ##D     fill = colours, cex = 0.7)
> ##D     title(sub = paste("Columbus OH: residential burglaries and ",
> ##D     "vehicle\nthefts", "per thousand households, 1980"))
> ##D     
> ##D     # 2
> ##D     area1 <- poly2area(polys)
> ##D     w <- area.util.contour(area1)
> ##D     wxy <- area.util.xy(area1)
> ##D     area.plot(area1, values = columbus$CRIME, sub = paste("Columbus ",
> ##D     "OH: residential burglaries and vehicle\nthefts",
> ##D     "per thousand households, 1980"))
> ##D     apply(w, 1, function(x) segments(x[1], x[2], x[3], x[4], lwd = 2))
> ##D 
> ##D     # 3
> ##D     data(elec88)
> ##D     fr.area <- elec88$area
> ##D     fr.xy <- area.util.xy(fr.area)
> ##D     fr.neig <- elec88$neig # neig(area = fr.area)
> ##D 
> ##D     # 4
> ##D     fr.poly <- area2poly(fr.area)
> ##D     fr.nb <- neig2nb(fr.neig)
> ##D     plot.polylist(fr.poly, attr(fr.poly, "region.rect"), border = "grey")
> ##D     plot(fr.nb, fr.xy, add = TRUE)
> ##D     s.label(fr.xy, clab = 0, area = fr.area, neig = fr.neig, 
> ##D     cneig = 1, cpo = 2, inc = FALSE, addax = FALSE)
> ##D par(mfrow=c(1,1))
> ##D }
> ## End(Not run)
> 
> data(irishdata)
> w <- irishdata$area[c(42:53,18:25),]
> w
   poly   x  y
42  S07  16 50
43  S07  16 44
44  S07  44 47
45  S07  21 34
46  S07  20 20
47  S07  28 19
48  S07  46 23
49  S07  30  8
50  S07  45 12
51  S07  68 35
52  S07  68 59
53  S07  63 78
18  S04  68 35
19  S04  68 59
20  S04 112 55
21  S04 119 50
22  S04 132 25
23  S04  74  0
24  S04  45  0
25  S04  45 12
> w$poly <- as.factor(as.character(w$poly))
> area.plot(w, clab = 2)
>  
> points(68, 59, pch = 20, col = "red", cex = 3)
> points(68, 35, pch = 20, col = "red", cex = 3)
> points(45, 12, pch = 20, col = "red", cex = 3)
> sqrt((59-35)^2)+sqrt((68-45)^2+(35-12)^2)
[1] 56.52691
> area2link(w)
         S07      S04
S07  0.00000 56.52691
S04 56.52691  0.00000
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "arrival"
> 
> ### * arrival
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: arrival
> ### Title: Arrivals at an intensive care unit
> ### Aliases: arrival
> ### Keywords: datasets chron
> 
> ### ** Examples
> 
> data(arrival)
> dotcircle(arrival$hours, pi/2 + pi/12)
> 
> 
> 
> cleanEx(); ..nameEx <- "as.taxo"
> 
> ### * as.taxo
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: as.taxo
> ### Title: Taxonomy
> ### Aliases: as.taxo
> ### Keywords: manip
> 
> ### ** Examples
> 
> data(taxo.eg)
> tax <- as.taxo(taxo.eg[[1]])
> tax.phy <- taxo2phylog(as.taxo(taxo.eg[[1]]))
> par(mfrow = c(1,2))
> plot.phylog(tax.phy, clabel.l = 1.25, clabel.n = 1.25, f = 0.75)
> plot.phylog(taxo2phylog(as.taxo(taxo.eg[[1]][sample(15),])),
+  clabel.l = 1.25, clabel.n = 1.25, f = 0.75)
> par(mfrow = c(1,1))
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "atlas"
> 
> ### * atlas
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: atlas
> ### Title: Small Ecological Dataset
> ### Aliases: atlas
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(atlas)
> op <- par(no.readonly = TRUE) 
> par(mfrow = c(2,2))
> area.plot(atlas$area, cpoin = 1.5)
> area.plot(atlas$area, lab = atlas$names.district, clab = 1)
> x <- atlas$meteo$mini.jan
> 
> names(x) <- row.names(atlas$meteo)
> area.plot(atlas$area, val = x, ncl = 12, sub = "Temp Mini January", 
+     csub = 2, cleg = 1)
> s.corcircle((dudi.pca(atlas$meteo, scann = FALSE)$co), 
+     clab = 1)
> 
> area.plot(atlas$area, val = dudi.pca(atlas$meteo,scann=FALSE)$li[,1], 
+     ncl = 12, sub = "Principal Component Analysis analysis", csub = 1.5, 
+     cleg = 1)
> birds.coa <- dudi.coa(atlas$birds, sca = FALSE, nf = 1)
> x <- birds.coa$li$Axis1
> area.plot(atlas$area, val = x, ncl = 12, 
+     sub = "Correspondence analysis", csub = 1.5, cleg = 1)
> 
> s.value(atlas$xy, x, contour = atlas$contour, csi = 2, 
+     incl = FALSE, addax = FALSE)
> triangle.plot(atlas$alti)
> par(op)
> par(mfrow=c(1,1))
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "atya"
> 
> ### * atya
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: atya
> ### Title: Genetic variability of Cacadors
> ### Aliases: atya
> ### Keywords: datasets
> 
> ### ** Examples
> 
> ## Not run: 
> ##D data(atya)
> ##D if (require(pixmap, quiet = TRUE)) {
> ##D     atya.digi <- read.pnm(system.file("pictures/atyadigi.pnm",
> ##D         package = "ade4"))
> ##D     atya.carto <- read.pnm(system.file("pictures/atyacarto.pnm",
> ##D         package = "ade4"))
> ##D     par(mfrow = c(1,2))
> ##D     plot(atya.digi)
> ##D     plot(atya.carto)
> ##D     points(atya$xy, pch = 20, cex = 2)
> ##D }
> ##D if (require(maptools, quiet = TRUE) & require(spdep, quiet = TRUE)) {
> ##D     plot(neig2nb(atya$neig), atya$xy, col = "red", add = TRUE, lwd = 2)
> ##D     par(mfrow = c(1,1))
> ##D }
> ## End(Not run)
> 
> 
> cleanEx(); ..nameEx <- "avijons"
> 
> ### * avijons
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: avijons
> ### Title: Bird species distribution
> ### Aliases: avijons
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(avijons)
> w1=dudi.coa(avijons$fau,scannf=FALSE)$li
> area.plot(avijons$area,center=avijons$xy,val=w1[,1],clab=0.75,sub="CA Axis 1",csub=3)
> ## Not run: 
> ##D data(avijons)
> ##D if (require(pixmap,quiet=TRUE)) {
> ##D     pnm.eau <- read.pnm(system.file("pictures/avijonseau.pnm",
> ##D         package = "ade4"))
> ##D     pnm.rou <- read.pnm(system.file("pictures/avijonsrou.pnm",
> ##D         package = "ade4"))
> ##D     pnm.veg <- read.pnm(system.file("pictures/avijonsveg.pnm",
> ##D         package = "ade4"))
> ##D     pnm.vil <- read.pnm(system.file("pictures/avijonsvil.pnm",
> ##D         package = "ade4"))
> ##D     jons.coa <- dudi.coa(avijons$fau, scan = FALSE, nf = 4)
> ##D     par(mfcol = c(3,2))
> ##D     s.value(avijons$xy, jons.coa$li[,1], pixmap = pnm.rou, inclu = FALSE,
> ##D     grid = FALSE, addax = FALSE, cleg = 0, sub = "F1+ROADS", csub = 3)
> ##D     s.value(avijons$xy, jons.coa$li[,1], pixmap = pnm.veg, inclu = FALSE,
> ##D     grid = FALSE, addax = FALSE, cleg = 0, sub = "F1+TREES", csub = 3)
> ##D     s.value(avijons$xy, jons.coa$li[,1], pixmap = pnm.eau, inclu = FALSE,
> ##D     grid = FALSE, addax = FALSE, cleg = 0, sub = "F1+WATER", csub = 3)
> ##D     s.value(avijons$xy, jons.coa$li[,2], pixmap = pnm.rou, inclu = FALSE,
> ##D     grid = FALSE, addax = FALSE, cleg = 0, sub = "F2+ROADS", csub = 3)
> ##D     s.value(avijons$xy, jons.coa$li[,2], pixmap = pnm.veg, inclu = FALSE,
> ##D     grid = FALSE, addax = FALSE, cleg = 0, sub = "F2+TREES", csub = 3)
> ##D     s.value(avijons$xy, jons.coa$li[,2], pixmap = pnm.eau, inclu = FALSE,
> ##D     grid = FALSE, addax = FALSE, cleg = 0, sub = "F2+WATER", csub = 3)
> ##D par(mfrow=c(1,1))}
> ##D 
> ##D if (require(maptools, quiet = TRUE) & require(spdep, quiet = TRUE) 
> ##D         & require( pixmap, quiet = TRUE) ) {
> ##D     link1 <- area2link(avijons$area)
> ##D     lw1 <- apply(link1,1,function(x) x[x>0])
> ##D     neig1 <- neig(mat01=1*(link1>0))
> ##D     nb1 <- neig2nb(neig1)
> ##D     listw1 <- nb2listw(nb1,lw1)
> ##D     jons.ms <- multispati(jons.coa, listw1, scan = FALSE, nfp = 3,
> ##D      nfn = 2)
> ##D     summary(jons.ms)
> ##D     par(mfrow = c(2,2))
> ##D     barplot(jons.coa$eig)
> ##D     barplot(jons.ms$eig)
> ##D     s.corcircle(jons.ms$as)
> ##D     plot(jons.coa$li[,1], jons.ms$li[,1])
> ##D 
> ##D     par(mfcol=c(3,2))
> ##D     s.value(avijons$xy, jons.ms$li[,1], pixmap = pnm.rou, inclu = FALSE,
> ##D     grid = FALSE, addax = FALSE, cleg = 0, sub = "F1+ROADS", csub = 3)
> ##D     s.value(avijons$xy, jons.ms$li[,1], pixmap = pnm.veg, inclu = FALSE,
> ##D     grid = FALSE, addax = FALSE, cleg = 0, sub = "F1+TREES", csub = 3)
> ##D     s.value(avijons$xy, jons.ms$li[,1], pixmap = pnm.eau, inclu = FALSE,
> ##D     grid = FALSE, addax = FALSE, cleg = 0, sub = "F1+WATER", csub = 3)
> ##D     s.value(avijons$xy, jons.ms$li[,2], pixmap = pnm.rou, inclu = FALSE,
> ##D     grid = FALSE, addax = FALSE, cleg = 0, sub = "F2+ROADS", csub = 3)
> ##D     s.value(avijons$xy, jons.ms$li[,2], pixmap = pnm.veg, inclu = FALSE,
> ##D     grid = FALSE, addax = FALSE, cleg = 0, sub = "F2+TREES", csub = 3)
> ##D     s.value(avijons$xy, jons.ms$li[,2], pixmap = pnm.eau, inclu = FALSE,
> ##D     grid = FALSE, addax = FALSE, cleg = 0, sub = "F2+WATER", csub = 3)
> ##D par(mfrow=c(1,1))
> ##D }
> ## End(Not run)
> 
> 
> cleanEx(); ..nameEx <- "avimedi"
> 
> ### * avimedi
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: avimedi
> ### Title: Fauna Table for Constrained Ordinations
> ### Aliases: avimedi
> ### Keywords: datasets
> 
> ### ** Examples
> 
> ## Not run: 
> ##D data(avimedi)
> ##D par(mfrow = c(2,2))
> ##D coa1 <- dudi.coa(avimedi$fau, scan = FALSE, nf = 3)
> ##D s.class(coa1$li,avimedi$plan$str:avimedi$plan$reg,
> ##D     sub = "Correspondences Analysis")
> ##D bet1 <- between(coa1, avimedi$plan$str, scan = FALSE)
> ##D s.class(bet1$ls, avimedi$plan$str,
> ##D     sub = "Between Analysis")
> ##D wit1 <- within(coa1, avimedi$plan$reg, scan=FALSE)
> ##D s.class(wit1$li, avimedi$plan$str,
> ##D     sub = "Within Analysis")
> ##D pcaiv1 <- pcaiv(coa1, avimedi$plan, scan = FALSE)
> ##D s.match(pcaiv1$li, pcaiv1$ls, clab = 0,
> ##D     sub = "Canonical Correspondences Analysis")
> ##D s.class(pcaiv1$li, avimedi$plan$str:avimedi$plan$reg, 
> ##D     add.plot = TRUE)
> ##D par(mfrow=c(1,1))
> ## End(Not run)
> 
> 
> cleanEx(); ..nameEx <- "aviurba"
> 
> ### * aviurba
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: aviurba
> ### Title: Ecological Tables Triplet
> ### Aliases: aviurba
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(aviurba)
> a1 <- dudi.coa(aviurba$fau, scan = FALSE, nf=4)
> a2 <- dudi.acm(aviurba$mil, row.w = a1$lw, scan = FALSE, nf = 4)
> plot(coinertia(a1, a2, scan = FALSE))
> 
> 
> 
> cleanEx(); ..nameEx <- "bacteria"
> 
> ### * bacteria
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: bacteria
> ### Title: Genomes of 43 Bacteria
> ### Aliases: bacteria
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(bacteria)
> names(bacteria$espcodon)
 [1] "GCT" "GCC" "GCA" "GCG" "CGT" "CGC" "CGA" "CGG" "AGA" "AGG" "AAT" "AAC"
[13] "GAT" "GAC" "TGT" "TGC" "CAA" "CAG" "GAA" "GAG" "GGT" "GGC" "GGA" "GGG"
[25] "CAT" "CAC" "ATT" "ATC" "ATA" "TTA" "TTG" "CTT" "CTC" "CTA" "CTG" "AAA"
[37] "AAG" "ATG" "TTT" "TTC" "CCT" "CCC" "CCA" "CCG" "TCT" "TCC" "TCA" "TCG"
[49] "AGT" "AGC" "TAA" "TAG" "TGA" "ACT" "ACC" "ACA" "ACG" "TGG" "TAT" "TAC"
[61] "GTT" "GTC" "GTA" "GTG"
> names(bacteria$espaa)
 [1] "Ala" "Arg" "Asn" "Asp" "Cys" "Gln" "Glu" "Gly" "His" "Ile" "Leu" "Lys"
[13] "Met" "Phe" "Pro" "Ser" "Stp" "Thr" "Trp" "Tyr" "Val"
> names(bacteria$espbase)
[1] "A" "C" "G" "T"
> sum(bacteria$espcodon) # 22,619,749 codons
[1] 22619749
> scatter.coa(dudi.coa(bacteria$espcodon, scann = FALSE), 
+     posi = "bottom")
> 
> 
> 
> cleanEx(); ..nameEx <- "banque"
> 
> ### * banque
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: banque
> ### Title: Table of Factors
> ### Aliases: banque
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(banque)
> banque.acm <- dudi.acm(banque, scann = FALSE, nf = 3)
> apply(banque.acm$cr, 2, mean)
       RS1        RS2        RS3 
0.17346599 0.11838319 0.09825814 
> banque.acm$eig[1:banque.acm$nf] # the same thing
[1] 0.17346599 0.11838319 0.09825814
> s.arrow(banque.acm$c1, clab = 0.75)
> 
> 
> 
> cleanEx(); ..nameEx <- "baran95"
> 
> ### * baran95
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: baran95
> ### Title: African Estuary Fishes
> ### Aliases: baran95
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(baran95)
> w <- dudi.pca(log(baran95$fau+1), scal = FALSE, scann = FALSE, 
+     nf = 3)
> w1 <- within(w, baran95$plan$date, scann = FALSE)
> fatala <- ktab.within(w1)
> stat1 <- statis(fatala, scan = FALSE, nf = 3)
> w1 <- split(stat1$C.Co, baran95$plan$date)
> w2 <- split(baran95$plan$site, baran95$plan$date)
> par(mfrow = c(3,2))
> for (j in 1:6) {
+     s.label(stat1$C.Co[,1:2], clab = 0,
+     sub = tab.names(fatala)[j], csub = 3)
+     s.class(w1[[j]][,1:2], w2[[j]], clab = 2, axese = FALSE,
+     add.plot = TRUE)
+ }
> par(mfrow = c(1,1))
> 
> kplot(stat1, arrow = FALSE, traj = FALSE, clab = 2, uni = TRUE, 
+     class = baran95$plan$site) #simpler
> 
> mfa1 <- mfa(fatala, scan = FALSE, nf = 3)
> w1 <- split(mfa1$co, baran95$plan$date)
> w2 <- split(baran95$plan$site, baran95$plan$date)
> par(mfrow = c(3,2))
> for (j in 1:6) {
+     s.label(mfa1$co[,1:2], clab = 0,
+     sub = tab.names(fatala)[j], csub = 3)
+     s.class(w1[[j]][,1:2], w2[[j]], clab = 2, axese=FALSE,
+     add.plot = TRUE)
+ }
> par(mfrow = c(1,1))
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "between"
> 
> ### * between
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: between
> ### Title: Between-Class Analysis
> ### Aliases: between print.between plot.between
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(meaudret)
> par(mfrow = c(2,2))
> pca1 <- dudi.pca(meaudret$mil, scan = FALSE, nf = 4)
> s.class(pca1$li, meaudret$plan$sta,
+     sub = "Principal Component Analysis (mil)", csub = 1.75)
> pca2 <- dudi.pca(meaudret$fau, scal = FALSE, scan = FALSE, nf = 4)
> s.class(pca2$li, meaudret$pla$sta,
+     sub = "Principal Component Analysis (fau)", csub = 1.75)
> bet1 <- between(pca1, meaudret$plan$sta, scan = FALSE, nf = 2)
> bet2 <- between(pca2, meaudret$plan$sta, scan = FALSE, nf = 2)
> s.class(bet1$ls, meaudret$plan$sta,
+     sub = "Between sites PCA (mil)", csub = 1.75)
> s.class(bet2$ls, meaudret$plan$sta,
+     sub = "Between sites PCA (fau)", csub = 1.75)
> 
> par(mfrow = c(1,1))
> coib <- coinertia(bet1, bet2, scann = FALSE)
> plot(coib)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "bf88"
> 
> ### * bf88
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: bf88
> ### Title: Cubic Ecological Data
> ### Aliases: bf88
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(bf88)
> fou1 <- foucart(bf88, scann = FALSE, nf = 3)
> fou1
Foucart's  COA
class: foucart coa dudi
$call: foucart(X = bf88, scannf = FALSE, nf = 3)
table  number: 6 

$nf: 3 axis-components saved
$rank: 3
eigen values: 0.5278 0.3591 0.3235
blo    vector       6      blocks
 vector length mode    content       
 $cw    4      numeric column weights
 $lw    79     numeric row weights   
 $eig   3      numeric eigen values  

 data.frame nrow ncol content             
 $tab       79   4    modified array      
 $li        79   3    row coordinates     
 $l1        79   3    row normed scores   
 $co        4    3    column coordinates  
 $c1        4    3    column normed scores

     **** Intrastructure ****

 data.frame nrow ncol content                     
 $Tli       474  3    row coordinates (each table)
 $Tco       24   3    col coordinates (each table)
 $TL        474  2    factors for Tli             
 $TC        24   2    factors for Tco             

> par(mfrow = c(2,2))
> scatter(fou1)
> s.traject(fou1$Tco, fou1$TC[,1])
> s.traject(fou1$Tco, fou1$TC[,2])
> s.label(fou1$Tco)
> s.label(fou1$co, add.p = TRUE, clab = 2)
> par(mfrow = c(1,1))
> kplot(fou1, clab.c = 2, clab.r = 0, csub = 3)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "bicenter.wt"
> 
> ### * bicenter.wt
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: bicenter.wt
> ### Title: Double Weighted Centring
> ### Aliases: bicenter.wt
> ### Keywords: utilities
> 
> ### ** Examples
> 
> w <- matrix(1:6, 3, 2)
> bicenter.wt(w, c(0.2,0.6,0.2), c(0.3,0.7))
     [,1] [,2]
[1,]    0    0
[2,]    0    0
[3,]    0    0
> 
> w <- matrix(1:20, 5, 4)
> sum(bicenter.wt(w, runif(5), runif(4))^2)
[1] 4.042912e-29
> 
> 
> 
> cleanEx(); ..nameEx <- "bordeaux"
> 
> ### * bordeaux
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: bordeaux
> ### Title: Wine Tasting
> ### Aliases: bordeaux
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(bordeaux)
> bordeaux
                    excellent good mediocre boring
Cru_Bourgeois              45  126       24      5
Grand_Cru_class\351        87   93       19      1
Vin_de_table                0    0       52    148
Bordeaux_d_origine         36   68       74     22
Vin_de_marque               0   30      111     59
> score(dudi.coa(bordeaux, scan = FALSE))
> 
> 
> 
> cleanEx(); ..nameEx <- "bsetal97"
> 
> ### * bsetal97
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: bsetal97
> ### Title: Ecological and Biological Traits
> ### Aliases: bsetal97
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(bsetal97)
> X <- prep.fuzzy.var(bsetal97$biol, bsetal97$biol.blo)
17 missing data found in block 1 
14 missing data found in block 2 
28 missing data found in block 3 
8 missing data found in block 4 
5 missing data found in block 5 
19 missing data found in block 6 
10 missing data found in block 7 
5 missing data found in block 8 
2 missing data found in block 9 
12 missing data found in block 10 
> Y <- prep.fuzzy.var(bsetal97$ecol, bsetal97$ecol.blo)
6 missing data found in block 1 
16 missing data found in block 2 
5 missing data found in block 3 
9 missing data found in block 4 
15 missing data found in block 5 
47 missing data found in block 6 
6 missing data found in block 7 
> plot(coinertia(dudi.fca(X, scan = FALSE), 
+     dudi.fca(Y, scan = FALSE), scan = FALSE))
> 
> 
> 
> cleanEx(); ..nameEx <- "buech"
> 
> ### * buech
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: buech
> ### Title: Buech basin
> ### Aliases: buech
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(buech)
> par(mfrow = c(1,2))
> 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)
> par(mfrow = c(1,1))
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "butterfly"
> 
> ### * butterfly
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: butterfly
> ### Title: Genetics-Ecology-Environment Triple
> ### Aliases: butterfly
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(butterfly)
> par(mfrow = c(2,2))
> s.label(butterfly$xy, contour = butterfly$contour, inc = FALSE)
> table.dist(dist(butterfly$xy), labels = row.names(butterfly$xy)) # depends of mva
> s.value(butterfly$xy, dudi.pca(butterfly$envir, scan = FALSE)$li[,1], 
+     contour = butterfly$contour, inc = FALSE, csi = 3)
> plot(mantel.randtest(dist(butterfly$xy), dist(butterfly$gen), 99),
+     main = "genetic/spatial")
> par(mfrow = c(1,1))
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "cailliez"
> 
> ### * cailliez
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: cailliez
> ### Title: Transformation to make Euclidean a distance matrix
> ### Aliases: cailliez
> ### Keywords: array
> 
> ### ** Examples
> 
> data(capitales)
> d0 <- as.dist(capitales$df)
> is.euclid(d0) # FALSE
[1] FALSE
> d1 <- cailliez(d0, TRUE)
Cailliez constant = 2429.87867 
> # Cailliez constant = 2429.87867 
> is.euclid(d1) # TRUE
[1] TRUE
> plot(d0, d1)
> abline(lm(unclass(d1)~unclass(d0)))
> print(coefficients(lm(unclass(d1)~unclass(d0))), dig = 8) # d1 = d + Cte
(Intercept) unclass(d0) 
  2429.8787      1.0000 
> is.euclid(d0 + 2428) # FALSE
[1] FALSE
> is.euclid(d0 + 2430) # TRUE the smallest constant
[1] TRUE
> 
> 
> 
> cleanEx(); ..nameEx <- "capitales"
> 
> ### * capitales
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: capitales
> ### Title: Road Distances
> ### Aliases: capitales
> ### Keywords: datasets
> 
> ### ** Examples
> 
> if (require(pixmap, quiet = TRUE)) {
+     data(capitales)
+     names(capitales$df)
+     # [1] "Madrid"     "Paris"      "Londres"    "Dublin"     "Rome"      
+     # [6] "Bruxelles"  "Amsterdam"  "Berlin"     "Copenhague" "Stokholm"  
+     #[11] "Luxembourg" "Helsinki"   "Vienne"     "Athenes"    "Lisbonne"
+ 
+     capitales.pnm <- read.pnm(system.file("pictures/capitales.pnm", package = "ade4"))
+     # plot(capitales.pnm) # depends of pixmap
+     # xy <- locator(15) # funny
+     data(capitales)
+     par(mfrow = c(2,2))
+     s.label(capitales$xy, lab = names(capitales$df)) 
+     s.label(capitales$xy, lab = names(capitales$df), pixmap = capitales.pnm, inc = FALSE)
+     table.dist(as.dist(capitales$df), lab = names(capitales$df)) # depends of mva
+     s.label(pcoscaled(lingoes(as.dist(capitales$df))))
+     par(mfrow = c(1,1))
+ }
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "carni19"
> 
> ### * carni19
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: carni19
> ### Title: Phylogeny and quantative trait of carnivora
> ### Aliases: carni19
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(carni19)
> carni19.phy <- newick2phylog(carni19$tre)
> par(mfrow = c(1,2))
> symbols.phylog(carni19.phy,carni19$bm-mean(carni19$bm))
> dotchart.phylog(carni19.phy, carni19$bm, clabel.l=0.75)
> par(mfrow = c(1,1))
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "carni70"
> 
> ### * carni70
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: carni70
> ### Title: Phylogeny and quantitative traits of carnivora
> ### Aliases: carni70
> ### Keywords: datasets
> 
> ### ** Examples
> 
> ## Not run: 
> ##D data(carni70)
> ##D carni70.phy <- newick2phylog(carni70$tre)
> ##D plot.phylog(carni70.phy)
> ##D 
> ##D size <- scalewt(log(carni70$tab))[,1]
> ##D names(size) <- row.names(carni70$tab)
> ##D symbols.phylog(carni70.phy,size)
> ##D orthogram(size, phylog = carni70.phy)
> ##D 
> ##D yrange <- scalewt(carni70$tab[,2])
> ##D names(yrange) <- row.names(carni70$tab)
> ##D symbols.phylog(carni70.phy,yrange)
> ##D orthogram(yrange, phylog = carni70.phy)
> ##D 
> ##D s.hist(cbind.data.frame(size, yrange), clabel = 0)
> ## End(Not run)
> 
> 
> cleanEx(); ..nameEx <- "carniherbi49"
> 
> ### * carniherbi49
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: carniherbi49
> ### Title: Taxonomy, phylogenies and quantitative traits of carnivora and
> ###   herbivora
> ### Aliases: carniherbi49
> ### Keywords: datasets
> 
> ### ** Examples
> 
> ## Not run: 
> ##D data(carniherbi49)
> ##D par(mfrow=c(1,3))
> ##D plot(newick2phylog(carniherbi49$tre1), clabel.leaves = 0,
> ##D  f.phylog = 2, sub ="article 1")
> ##D plot(newick2phylog(carniherbi49$tre2), clabel.leaves = 0,
> ##D  f.phylog = 2, sub = "article 2")
> ##D taxo <- as.taxo(carniherbi49$taxo)
> ##D plot(taxo2phylog(taxo), clabel.nodes = 1.2, clabel.leaves = 1.2)
> ##D par(mfrow = c(1,1))
> ## End(Not run)
> 
> 
> cleanEx(); ..nameEx <- "casitas"
> 
> ### * casitas
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: casitas
> ### Title: Enzymatic polymorphism in Mus musculus
> ### Aliases: casitas
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(casitas)
> casitas.pop <- as.factor(rep(c("dome", "cast", "musc", "casi"), c(24,11,9,30)))
> table(casitas.pop,casitas[,1]) 
           
casitas.pop 080080 080100 100100
       casi      2     11     17
       cast      2      3      6
       dome      0      0     24
       musc      0      0      9
> casi.genet <- char2genet(casitas, casitas.pop)
> names(casi.genet)
[1] "tab"        "center"     "pop.names"  "all.names"  "loc.blocks"
[6] "loc.fac"    "loc.names"  "pop.loc"    "all.pop"   
> 
> 
> 
> cleanEx(); ..nameEx <- "cca"
> 
> ### * cca
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: cca
> ### Title: Canonical Correspondence Analysis
> ### Aliases: cca
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(rpjdl)
> millog <- log(rpjdl$mil + 1)
> iv1 <- cca(rpjdl$fau, millog, scan = FALSE)
> plot(iv1)
> 
> # analysis with c1 - as - li -ls
> # projections of inertia axes on PCAIV axes
> s.corcircle(iv1$as)
> 
> # Species positions
> s.label(iv1$c1, 2, 1, clab = 0.5, xlim = c(-4,4))
> # Sites positions at the weighted mean of present species
> s.label(iv1$ls, 2, 1, clab = 0, cpoi = 1, add.p = TRUE)
> 
> # Prediction of the positions by regression on environmental variables
> s.match(iv1$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$co, 2, 1, clab = 0.75, add.plot = TRUE)
> 
> # coherence between weights and correlations
> par(mfrow = c(1,2))
> s.corcircle(iv1$cor, 2, 1)
> s.arrow(iv1$fa, 2, 1)
> par(mfrow = c(1,1))
>  
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "chatcat"
> 
> ### * chatcat
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: chatcat
> ### Title: Qualitative Weighted Variables
> ### Aliases: chatcat
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(chatcat)
> summary(chatcat$tab)
  age       feco    nport 
 1  :5   1-2  : 3   1: 9  
 2-3:6   3-6  :10   2:17  
 4-5:5   7-8  : 6         
 6-7:5   9-12 : 5         
 >=8:5   13-14: 2         
> w <- acm.disjonctif(chatcat$tab) #  Disjonctive table
> names(w) <- c(paste("A", 1:5, sep = ""), paste("B", 1:5, sep = ""), 
+     paste("C", 1:2, sep = ""))
> w <- t(w*chatcat$num)
> w <- data.frame(w)
> w # BURT table
   X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20 X21
A1  7  0  0 18  0  0  0  0  5   0   0   0   0   0   1   0   0   0   0   1   0
A2  0  3  0  0 14  0  0  0  0  10   0   0   0   0   0  14   0   0   0   0   4
A3  0  0  0  0  0  3  0  0  0   0   1   0   0   1   0   0   7   0   0   0   0
A4  0  0  0  0  0  0  3  0  0   0   0   5   0   0   0   0   0   5   0   0   0
A5  0  0  2  0  0  0  0  3  0   0   0   0   3   0   0   0   0   0   5   0   0
B1  7  3  2  0  0  0  0  0  0   0   0   0   0   0   0   0   0   0   0   0   0
B2  0  0  0 18 14  3  3  3  5  10   1   5   3   0   0   0   0   0   0   0   0
B3  0  0  0  0  0  0  0  0  0   0   0   0   0   1   1  14   7   5   5   0   0
B4  0  0  0  0  0  0  0  0  0   0   0   0   0   0   0   0   0   0   0   1   4
B5  0  0  0  0  0  0  0  0  0   0   0   0   0   0   0   0   0   0   0   0   0
C1  7  3  2 18 14  3  3  3  0   0   0   0   0   1   0   0   0   0   0   0   0
C2  0  0  0  0  0  0  0  0  5  10   1   5   3   0   1  14   7   5   5   1   4
   X22 X23 X24 X25 X26
A1   0   0   0   0   0
A2   0   0   0   2   0
A3   6   0   0   0   0
A4   0   4   0   0   2
A5   0   0   5   0   0
B1   0   0   0   0   0
B2   0   0   0   0   0
B3   0   0   0   0   0
B4   6   4   5   0   0
B5   0   0   0   2   2
C1   0   0   0   0   0
C2   6   4   5   2   2
> 
> 
> 
> cleanEx(); ..nameEx <- "chats"
> 
> ### * chats
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: chats
> ### Title: Pair of Variables
> ### Aliases: chats
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(chats)
> chatsw <- data.frame(t(chats))
> chatscoa <- dudi.coa(chatsw, scann = FALSE)
> par(mfrow = c(2,2))
> table.cont(chatsw, abmean.x = TRUE, csi = 2, abline.x = TRUE, 
+     clabel.r = 1.5, clabel.c = 1.5)
> table.cont(chatsw, abmean.y = TRUE, csi = 2, abline.y = TRUE, 
+     clabel.r = 1.5, clabel.c = 1.5)
> table.cont(chatsw, x = chatscoa$c1[,1], y = chatscoa$l1[,1],
+     abmean.x = TRUE, csi = 2, abline.x = TRUE, clabel.r = 1.5, 
+     clabel.c = 1.5)
> table.cont(chatsw,, x = chatscoa$c1[,1], y = chatscoa$l1[,1],
+     abmean.y = TRUE, csi = 2, abline.y = TRUE, clabel.r = 1.5, 
+     clabel.c = 1.5)
> par(mfrow = c(1,1))
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "chazeb"
> 
> ### * chazeb
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: chazeb
> ### Title: Charolais-Zebus
> ### Aliases: chazeb
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(chazeb)
> plot(discrimin(dudi.pca(chazeb$tab, scan = FALSE), 
+     chazeb$cla, scan = FALSE))
> 
> 
> 
> cleanEx(); ..nameEx <- "chevaine"
> 
> ### * chevaine
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: chevaine
> ### Title: Enzymatic polymorphism in Leuciscus cephalus
> ### Aliases: chevaine
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(chevaine)
> 'fun.chevaine' <- function(label=TRUE) {
+     opar <- par(mar = par("mar"))
+         on.exit(par(opar))
+         par(mar = c(0.1, 0.1, 0.1, 0.1))
+     s.label(chevaine$coo$poi, xlim = c(-20,400), clab = 0, cpoi = 0)
+     invisible(lapply(chevaine$coo$lac, polygon,col = "blue", type = "l", lwd = 2)) 
+     invisible(lapply(chevaine$coo$riv, points, col = "blue", type = "l", lwd = 2))
+     if (label){
+         s.label(chevaine$coo$poi, clab = 0.75, add.p = TRUE)
+         s.label(chevaine$coo$sta, add.p = TRUE, clab = 0.5)
+     }
+     arrows(200,100,300,100, code = 3, angle = 15, length = 0.2)
+     text(250,125,"50 Km")
+ }
> 
> fun.chevaine()
> 
> che.genet <- freq2genet(chevaine$tab)
> che.pca <- dudi.pca(che.genet$tab, center = che.genet$center, scannf = FALSE, nf = 3)
> 
> par(mfrow = c(1,2))
> fun.chevaine(FALSE)
> s.value(chevaine$coo$sta, che.pca$li[,1], csi = 2, add.p = TRUE)
> fun.chevaine(FALSE)
> s.value(chevaine$coo$sta, che.pca$li[,2], csi = 2, add.p = TRUE)
> 
> w = prep.fuzzy.var (che.genet$tab, che.genet$loc.blocks)
> che.fca <- dudi.fca(w, scannf = FALSE, nf = 3)
> 
> fun.chevaine(FALSE)
> s.value(chevaine$coo$sta, che.fca$li[,1], csi = 1.5, add.p = TRUE)
> fun.chevaine(FALSE)
> s.value(chevaine$coo$sta, che.fca$li[,2], csi = 1.5, add.p = TRUE)
> 
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "clementines"
> 
> ### * clementines
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: clementines
> ### Title: Fruit Production
> ### Aliases: clementines
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(clementines)
> op <- par(no.readonly = TRUE) 
> par(mfrow = c(5,4)) ; par(mar = c(2,2,1,1))
> for (i in 1:20) {
+     w0 <- 1:15 ; plot(w0, clementines[,i], type = "b")
+     abline(lm(clementines[,i] ~ w0))
+ }
> par(op)
> 
> pca1 <- dudi.pca(clementines, scan = FALSE)
> s.corcircle(pca1$co, clab = 0.75)
> 
> barplot(pca1$li[,1])
> 
> op <- par(no.readonly = TRUE) 
> par(mfrow = c(5,4)) ; par(mar = c(2,2,1,1))
> clem0 <- pca1$tab
> croi <- 1:15
> alter <- c(rep(c(1,-1),7),1)
> for (i in 1:20) {
+     y <- clem0[,i]
+     plot(w0, y, type = "b", ylim = c(-2,2))
+     z <- predict(lm(clem0[,i] ~ croi*alter))
+     points(w0, z, pch = 20, cex = 2)
+     for(j in 1:15) segments (j,y[j],j,z[j])
+ }
> par(op)
> par(mfrow = c(1,1))
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "cnc2003"
> 
> ### * cnc2003
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: cnc2003
> ### Title: Frequenting movie theaters in France in 2003
> ### Aliases: cnc2003
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(cnc2003)
> sco.quant(cnc2003$popu, cnc2003[,2:10], abline = TRUE, csub = 3)
> 
> 
> 
> cleanEx(); ..nameEx <- "coinertia"
> 
> ### * coinertia
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: coinertia
> ### Title: Coinertia Analysis
> ### Aliases: coinertia print.coinertia plot.coinertia summary.coinertia
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(doubs)
> dudi1 <- dudi.pca(doubs$mil, scale = TRUE, scan = FALSE, nf = 3)
> dudi2 <- dudi.pca(doubs$poi, scale = FALSE, scan = FALSE, nf = 2)
> coin1 <- coinertia(dudi1,dudi2, scan = FALSE, nf = 2)
> s.arrow(coin1$l1, clab = 0.7)
> 
> s.arrow(coin1$c1, clab = 0.7)
> 
> par(mfrow = c(1,2))
> s.corcircle(coin1$aX)
> s.corcircle(coin1$aY)
> par(mfrow = c(1,1))
>  
> coin1
Coinertia analysis
call: coinertia(dudiX = dudi1, dudiY = dudi2, scannf = FALSE, nf = 2)
class: coinertia dudi 

$rank (rank)     : 11
$nf (axis saved) : 2
$RV (RV coeff)   : 0.4505569

eigen values: 119 13.87 0.7566 0.5278 0.2709 ...

  vector length mode    content                    
1 $eig   11     numeric eigen values               
2 $lw    27     numeric row weigths (crossed array)
3 $cw    11     numeric col weigths (crossed array)

   data.frame nrow ncol content                         
1  $tab       27   11   crossed array (CA)              
2  $li        27   2    Y col = CA row: coordinates     
3  $l1        27   2    Y col = CA row: normed scores   
4  $co        11   2    X col = CA column: coordinates  
5  $c1        11   2    X col = CA column: normed scores
6  $lX        30   2    row coordinates (X)             
7  $mX        30   2    normed row scores (X)           
8  $lY        30   2    row coordinates (Y)             
9  $mY        30   2    normed row scores (Y)           
10 $aX        3    2    axis onto co-inertia axis (X)   
11 $aY        2    2    axis onto co-inertia axis (Y)   

> summary(coin1)

Eigenvalues decomposition:
        eig     covar      sdX      sdY      corr
1 119.01942 10.909602 2.326324 6.422570 0.7301798
2  13.87137  3.724429 1.685078 2.863743 0.7718017

Inertia & coinertia X:
    inertia      max     ratio
1  5.411785 6.321624 0.8560752
12 8.251272 8.553220 0.9646978

Inertia & coinertia Y:
    inertia      max     ratio
1  41.24940 42.74627 0.9649824
12 49.45042 50.90461 0.9714331

RV:
 0.4505569 
> plot(coin1)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "coleo"
> 
> ### * coleo
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: coleo
> ### Title: Table of Fuzzy Biological Traits
> ### Aliases: coleo
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(coleo)
> op <- par(no.readonly = TRUE) 
> coleo.fuzzy <- prep.fuzzy.var(coleo$tab, coleo$col.blocks)
2 missing data found in block 1 
1 missing data found in block 3 
2 missing data found in block 4 
> fca1 <- dudi.fca(coleo.fuzzy, sca = FALSE, nf = 3)
> par(mfrow = c(3,3))
> indica <- factor(rep(names(coleo$col), coleo$col))
> for (j in levels(indica)) s.distri (fca1$l1,
+     coleo$tab[,which(indica==j)], clab = 1.5, sub = as.character(j), 
+     cell = 0, csta = 0.5, csub = 3, 
+     label = coleo$moda.names[which(indica == j)])
> par(op)
> par(mfrow = c(1,1))
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "corkdist"
> 
> ### * corkdist
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: corkdist
> ### Title: Tests of randomization between distances applied to 'kdist'
> ###   objetcs
> ### Aliases: corkdist mantelkdist RVkdist print.corkdist summary.corkdist
> ###   plot.corkdist
> ### Keywords: nonparametric
> 
> ### ** Examples
> 
> data(friday87)
> fri.w <- ktab.data.frame(friday87$fau, friday87$fau.blo, 
+     tabnames = friday87$tab.names)
> fri.kc <- lapply(1:10, function(x) dist.binary(fri.w[[x]],10))
> names(fri.kc) <-  substr(friday87$tab.names,1,4)
> fri.kd <- kdist(fri.kc)
>  fri.mantel = mantelkdist(kd = fri.kd, nrepet = 999)
>  plot(fri.mantel,1:5,1:5)
>  plot(fri.mantel,1:5,6:10)
>  plot(fri.mantel,6:10,1:5)
>  plot(fri.mantel,6:10,6:10)
> s.corcircle (dudi.pca(as.data.frame(fri.kd), scan = FALSE)$co)
> plot(RVkdist(fri.kd),1:5,1:5)
> 
> data(yanomama)
> m1 <- mantelkdist(kdist(yanomama),999)
> m1
Mantel's tests for 'kdist' object
class: corkdist list 
Call: mantelkdist(kd = kdist(yanomama), nrepet = 999)

gen-geo 
Monte-Carlo test
Observation: 0.5098684 
Call: mantelkdist(kd = kdist(yanomama), nrepet = 999)
Based on 999 replicates
Simulated p-value: 0.003 

ant-geo 
Monte-Carlo test
Observation: 0.8428053 
Call: mantelkdist(kd = kdist(yanomama), nrepet = 999)
Based on 999 replicates
Simulated p-value: 0.001 

ant-gen 
Monte-Carlo test
Observation: 0.2995506 
Call: mantelkdist(kd = kdist(yanomama), nrepet = 999)
Based on 999 replicates
Simulated p-value: 0.052 
list of 3 'randtest' objects
> summary(m1)
Mantel's tests for 'kdist' object
Call: mantelkdist(kd = kdist(yanomama), nrepet = 999)
Simulated p-values:
        1     2 3
geo     -     - -
gen 0.003     - -
ant 0.001 0.052 -
> plot(m1)
> 
> 
> 
> cleanEx(); ..nameEx <- "corvus"
> 
> ### * corvus
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: corvus
> ### Title: Corvus morphology
> ### Aliases: corvus
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(corvus)
> plot(corvus[,1:2])
> s.class(corvus[,1:2], corvus[,4]:corvus[,3], add.p = TRUE)
> 
> 
> 
> cleanEx(); ..nameEx <- "deug"
> 
> ### * deug
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: deug
> ### Title: Exam marks for some students
> ### Aliases: deug
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(deug)
> # decentred PCA
> pca1 <- dudi.pca(deug$tab, scal = FALSE, center = deug$cent, 
+     scan = FALSE)
> s.class(pca1$li, deug$result)
> s.arrow(40 * pca1$c1, add.plot = TRUE)
> 
> 
> 
> cleanEx(); ..nameEx <- "disc"
> 
> ### * disc
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: disc
> ### Title: Rao's dissimilarity coefficient
> ### Aliases: disc
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(humDNAm)
> humDNA.dist <- disc(humDNAm$samples, sqrt(humDNAm$distances), humDNAm$structures)
> humDNA.dist
$samples
          oriental     tharu     wolof      peul      pima      maya   finnish
tharu    0.2520200                                                            
wolof    0.7821356 0.7965116                                                  
peul     0.8506600 0.8632233 0.1251814                                        
pima     0.1994968 0.2755218 0.7582823 0.8338317                              
maya     0.2559697 0.3023221 0.7177691 0.7857762 0.1406173                    
finnish  0.2455583 0.3043545 0.7615230 0.8367302 0.1416780 0.1899101          
sicilian 0.2966927 0.3553515 0.7194281 0.7871977 0.2731589 0.2783955 0.2593187
israelij 0.3578283 0.4564520 0.7963554 0.8604647 0.3906841 0.4075339 0.3601095
israelia 0.4102655 0.4363589 0.5822519 0.6437361 0.3588124 0.3027937 0.2984287
          sicilian  israelij
tharu                       
wolof                       
peul                        
pima                        
maya                        
finnish                     
sicilian                    
israelij 0.3690240          
israelia 0.3541134 0.3947386

$regions
              africa   america      asia    europe
america    0.7609083                              
asia       0.8016371 0.2280650                    
europe     0.7519837 0.1646208 0.2553667          
middleeast 0.6866876 0.3045622 0.3592600 0.2518122

> is.euclid(humDNA.dist$samples)
[1] TRUE
> is.euclid(humDNA.dist$regions)
[1] TRUE
> 
> ## Not run: 
> ##D data(ecomor)
> ##D ecomor.phylog <- taxo2phylog(ecomor$taxo)
> ##D ecomor.dist <- disc(ecomor$habitat, ecomor.phylog$Wdist)
> ##D ecomor.dist
> ##D is.euclid(ecomor.dist)
> ## End(Not run)
> 
> 
> 
> cleanEx(); ..nameEx <- "discrimin"
> 
> ### * discrimin
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: discrimin
> ### Title: Linear Discriminant Analysis (descriptive statistic)
> ### Aliases: discrimin plot.discrimin print.discrimin
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(chazeb)
> dis1 <- discrimin(dudi.pca(chazeb$tab, scan = FALSE), chazeb$cla, 
+     scan = FALSE)
> dis1
Discriminant analysis
call: discrimin(dudi = dudi.pca(chazeb$tab, scan = FALSE), fac = chazeb$cla, 
    scannf = FALSE)
class: discrimin 

$nf (axis saved) : 1

eigen values: 0.8451

  data.frame nrow ncol content                          
1 $fa        6    1    loadings / canonical weights     
2 $li        23   1    canonical scores                 
3 $va        6    1    cos(variables, canonical scores) 
4 $cp        6    1    cos(components, canonical scores)
5 $gc        2    1    class scores                     

> plot(dis1)
> 
> data(skulls)
> plot(discrimin(dudi.pca(skulls, scan = FALSE), gl(5,30), 
+     scan = FALSE))
> 
> 
> 
> cleanEx(); ..nameEx <- "discrimin.coa"
> 
> ### * discrimin.coa
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: discrimin.coa
> ### Title: Discriminant Correspondence Analysis
> ### Aliases: discrimin.coa
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(perthi02)
> plot(discrimin.coa(perthi02$tab, perthi02$cla, scan = FALSE))
> 
> 
> 
> cleanEx(); ..nameEx <- "dist.binary"
> 
> ### * dist.binary
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: dist.binary
> ### Title: Computation of Distance Matrices for Binary Data
> ### Aliases: dist.binary
> ### Keywords: array multivariate
> 
> ### ** Examples
> 
> data(aviurba)
> for (i in 1:10) {
+     d <- dist.binary(aviurba$fau, method = i)
+     cat(attr(d, "method"), is.euclid(d), "\n")}
JACCARD S3 TRUE 
SOCKAL & MICHENER S4 TRUE 
SOCKAL & SNEATH S5 TRUE 
ROGERS & TANIMOTO S6 TRUE 
CZEKANOWSKI S7 TRUE 
GOWER & LEGENDRE S9 TRUE 
OCHIAI S12 TRUE 
SOKAL & SNEATH S13 TRUE 
Phi of PEARSON S14 TRUE 
GOWER & LEGENDRE S2 TRUE 
> 
> 
> 
> cleanEx(); ..nameEx <- "dist.dudi"
> 
> ### * dist.dudi
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: dist.dudi
> ### Title: Computation of the Distance Matrix of a Statistical Triplet
> ### Aliases: dist.dudi
> ### Keywords: array multivariate
> 
> ### ** Examples
> 
> data (meaudret)
> pca1 <- dudi.pca(meaudret$mil, scan = FALSE)
> sum((dist(scalewt(meaudret$mil)) - dist.dudi(pca1))^2)
[1] 1.386546e-28
> #[1] 4.045e-29 the same thing
> 
> 
> 
> cleanEx(); ..nameEx <- "dist.genet"
> 
> ### * dist.genet
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: dist.genet
> ### Title: Genetic distances from gene frequencies
> ### Aliases: dist.genet
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(casitas)
> casi.genet <- char2genet(casitas,
+     as.factor(rep(c("dome", "cast", "musc", "casi"), c(24,11,9,30))))
> ldist <- lapply(1:5, function(method) dist.genet(casi.genet,method))
> ldist
[[1]]
          casi      cast      dome
cast 0.2863338                    
dome 0.1003991 0.3450556          
musc 1.2185291 0.5636602 1.2472719

[[2]]
          casi      cast      dome
cast 0.4378511                    
dome 0.3257588 0.5135286          
musc 0.7565759 0.6318505 0.7946422

[[3]]
          casi      cast      dome
cast 0.6596192                    
dome 0.6035993 0.7872298          
musc 0.8571692 0.7891692 0.9215769

[[4]]
          casi      cast      dome
cast 0.3413694                    
dome 0.1760324 0.3827603          
musc 0.7016184 0.4739970 0.7222744

[[5]]
          casi      cast      dome
cast 0.3547475                    
dome 0.1805556 0.3888889          
musc 0.7347222 0.5104798 0.7416667

> unlist(lapply(ldist, is.euclid))
[1] FALSE  TRUE  TRUE  TRUE  TRUE
> kdist(ldist)
List of distances matrices
call: kdist(ldist)
class: kdist
number of distances: 5
size: 4
labels:
    P1     P2     P3     P4 
"casi" "cast" "dome" "musc" 
X1: non euclidean distance
X2: euclidean distance
X3: euclidean distance
X4: euclidean distance
X5: euclidean distance
> 
> 
> 
> cleanEx(); ..nameEx <- "dist.neig"
> 
> ### * dist.neig
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: dist.neig
> ### Title: Computation of the Distance Matrix associated to a Neighbouring
> ###   Graph
> ### Aliases: dist.neig
> ### Keywords: array multivariate
> 
> ### ** Examples
> 
>     data(elec88)
>     d0 <- dist.neig(elec88$neig)
>     plot(dist(elec88$xy),d0)
> 
> 
> 
> cleanEx(); ..nameEx <- "dist.prop"
> 
> ### * dist.prop
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: dist.prop
> ### Title: Computation of Distance Matrices of Percentage Data
> ### Aliases: dist.prop
> ### Keywords: array multivariate
> 
> ### ** Examples
> 
> data(microsatt)
> w <- microsatt$tab[1:microsatt$loci.eff[1]]
> par(mfrow = c(2,2))
> scatter(dudi.pco(lingoes(dist.prop(w,1)), scann = FALSE))
> scatter(dudi.pco(lingoes(dist.prop(w,2)), scann = FALSE))
> scatter(dudi.pco(dist.prop(w,3), scann = FALSE))
> scatter(dudi.pco(lingoes(dist.prop(w,4)), scann = FALSE))
> par(mfrow = c(1,1))
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "dist.quant"
> 
> ### * dist.quant
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: dist.quant
> ### Title: Computation of Distance Matrices on Quantitative Variables
> ### Aliases: dist.quant
> ### Keywords: array multivariate
> 
> ### ** Examples
> 
> data(ecomor)
> par(mfrow = c(2,2))
> scatter(dudi.pco(dist.quant(ecomor$morpho,3), scan = FALSE))
> scatter(dudi.pco(dist.quant(ecomor$morpho,2), scan = FALSE))
> scatter(dudi.pco(dist(scalewt(ecomor$morpho)), scan = FALSE))
> scatter(dudi.pco(dist.quant(ecomor$morpho,1), scan = FALSE))
> par(mfrow = c(1,1))
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "divc"
> 
> ### * divc
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: divc
> ### Title: Rao's diversity coefficient also called quadratic entropy
> ### Aliases: divc
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(ecomor)
> ecomor.phylog <- taxo2phylog(ecomor$taxo)
> divc(ecomor$habitat, ecomor.phylog$Wdist)
    diversity
Bu1  2.754458
Bu2  2.967895
Bu3  3.107200
Bu4  3.137755
Ca1  2.500000
Ca2  3.230769
Ca3  3.272727
Ca4  3.283203
Ch1  2.333333
Ch2  2.595041
Ch3  3.246528
Ch4  3.135734
Pr1  2.840000
Pr2  2.816327
Pr3  3.055556
Pr4  3.028355
> 
> data(humDNAm)
> divc(humDNAm$samples, sqrt(humDNAm$distances))
          diversity
oriental 0.43100189
tharu    0.48255042
wolof    0.65884298
peul     0.55952920
pima     0.06198035
maya     0.17092768
finnish  0.33280992
sicilian 0.61913580
israelij 0.67061144
israelia 0.64036818
> 
> 
> 
> cleanEx(); ..nameEx <- "divcmax"
> 
> ### * divcmax
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: divcmax
> ### Title: Maximal value of Rao's diversity coefficient also called
> ###   quadratic entropy
> ### Aliases: divcmax
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> par.safe <- par()$mar
> data(elec88)
> par(mar = c(0.1, 0.1, 0.1, 0.1))
> # Departments of France.
> area.plot(elec88$area)
> 
> # Dissimilarity matrix.
> d0 <- dist(elec88$xy)
> 
> # Frequency distribution maximizing spatial diversity in France
> # according to Rao's quadratic entropy.
> France.m <- divcmax(d0)
> w0 <- France.m$vectors$num
> v0 <- France.m$value
> (1:94) [w0 > 0]
[1]  6 28 66
> 
> # Smallest circle including all the 94 departments.
> # The squared radius of that circle is the maximal value of the
> # spatial diversity.
> w1 = elec88$xy[c(6, 28, 66), ]
> w.c = apply(w1 * w0[c(6, 28, 66)], 2, sum)
> symbols(w.c[1], w.c[2], circles = sqrt(v0), inc = FALSE, add = TRUE)
> s.value(elec88$xy, w0, add.plot = TRUE)
> par(mar = par.safe)
> 
> ## Not run: 
> ##D # Maximisation of Rao's diversity coefficient
> ##D # with ultrametric dissimilarities.
> ##D data(microsatt)
> ##D mic.genet <- count2genet(microsatt$tab)
> ##D mic.dist <- dist.genet(mic.genet, 1)
> ##D mic.phylog <- hclust2phylog(hclust(mic.dist))
> ##D plot.phylog(mic.phylog)
> ##D mic.maxpond <- divcmax(mic.phylog$Wdist)$vectors$num
> ##D dotchart.phylog(mic.phylog, mic.maxpond)
> ## End(Not run)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "dotcircle"
> 
> ### * dotcircle
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: dotcircle
> ### Title: Representation of n values on a circle
> ### Aliases: dotcircle
> ### Keywords: hplot
> 
> ### ** Examples
> 
> w <- scores.neig(neig(n.cir = 24))
> par(mfrow = c(4,4))
> for (k in 1:16) dotcircle(w[,k],labels = 1:24)
> par(mfrow = c(1,1))
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "doubs"
> 
> ### * doubs
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: doubs
> ### Title: Pair of Ecological Tables
> ### Aliases: doubs
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(doubs)
> pca1 <- dudi.pca(doubs$mil, scan = FALSE)
> pca2 <- dudi.pca(doubs$poi, scale = FALSE, scan = FALSE)
> coiner1 <- coinertia(pca1, pca2, scan = FALSE)
> par(mfrow = c(3,3))
> s.corcircle(coiner1$aX)
> s.value(doubs$xy, coiner1$lX[,1])
> s.value(doubs$xy, coiner1$lX[,2])
> s.arrow(coiner1$c1)
> s.match(coiner1$mX, coiner1$mY)
> s.corcircle(coiner1$aY)
> s.arrow(coiner1$l1)
> s.value(doubs$xy, coiner1$lY[,1])
> s.value(doubs$xy, coiner1$lY[,2])
> par(mfrow = c(1,1))
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "dpcoa"
> 
> ### * dpcoa
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: dpcoa
> ### Title: Double principal coordinate analysis
> ### Aliases: dpcoa plot.dpcoa print.dpcoa
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(humDNAm)
> dpcoahum <- dpcoa(humDNAm$samples, sqrt(humDNAm$distances), scan = FALSE, nf = 2)
> dpcoahum
double principal coordinate analysis
class: dpcoa
$call: dpcoa(df = humDNAm$samples, dis = sqrt(humDNAm$distances), scannf = FALSE, 
    nf = 2)

$nf: 2 axis-components saved
eigen values: 0.1018 0.01035 0.006281 0.005602 0.003179 ...
  vector  length mode    content                                  
1 $w1     56     numeric weights of species                       
2 $w2     10     numeric weights of communities                   
3 $eig    9      numeric eigen values                             
4 $RaoDiv 10     numeric diversity coefficients within communities

  dist    Size content                          
1 $RaoDis 10   dissimilarities among communities

  data.frame  nrow ncol content                                    
1 $RaoDecodiv 3    1    decomposition of diversity                 
2 $l1         56   2    coordinates of the species                 
3 $l2         10   2    coordinates of the species                 
4 $c1         34   2    scores of the principal axes of the species
> plot(dpcoahum, csize = 1.5)
> ## Not run: 
> ##D data(ecomor)
> ##D ecomor.phylog <- taxo2phylog(ecomor$taxo)
> ##D dpcoaeco <- dpcoa(ecomor$habitat, ecomor.phylog$Wdist, scan = FALSE, nf = 2)
> ##D dpcoaeco
> ##D plot(dpcoaeco, csize = 1.5)
> ## End(Not run)
> 
> 
> 
> cleanEx(); ..nameEx <- "dudi"
> 
> ### * dudi
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: dudi
> ### Title: Duality Diagram
> ### Aliases: dudi as.dudi print.dudi t.dudi is.dudi redo.dudi
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(deug)
> dd1 <- dudi.pca(deug$tab, scannf = FALSE)
> dd1
Duality diagramm
class: pca dudi
$call: dudi.pca(df = deug$tab, scannf = FALSE)

$nf: 2 axis-components saved
$rank: 9
eigen values: 3.101 1.363 1.032 0.9341 0.7398 ...
  vector length mode    content       
1 $cw    9      numeric column weights
2 $lw    104    numeric row weights   
3 $eig   9      numeric eigen values  

  data.frame nrow ncol content             
1 $tab       104  9    modified array      
2 $li        104  2    row coordinates     
3 $l1        104  2    row normed scores   
4 $co        9    2    column coordinates  
5 $c1        9    2    column normed scores
other elements: cent norm 
> t(dd1)
Duality diagramm
class: transpo dudi
$call: t.dudi(x = dd1)

$nf: 2 axis-components saved
$rank: 9
eigen values: 3.101 1.363 1.032 0.9341 0.7398 ...
  vector length mode    content       
1 $cw    104    numeric column weights
2 $lw    9      numeric row weights   
3 $eig   9      numeric eigen values  

  data.frame nrow ncol content             
1 $tab       9    104  modified array      
2 $li        9    2    row coordinates     
3 $l1        9    2    row normed scores   
4 $co        104  2    column coordinates  
5 $c1        104  2    column normed scores
other elements: NULL
> is.dudi(dd1)
[1] TRUE
> redo.dudi(dd1,3)
Duality diagramm
class: pca dudi
$call: dudi.pca(df = deug$tab, scannf = FALSE, nf = 3)

$nf: 3 axis-components saved
$rank: 9
eigen values: 3.101 1.363 1.032 0.9341 0.7398 ...
  vector length mode    content       
1 $cw    9      numeric column weights
2 $lw    104    numeric row weights   
3 $eig   9      numeric eigen values  

  data.frame nrow ncol content             
1 $tab       104  9    modified array      
2 $li        104  3    row coordinates     
3 $l1        104  3    row normed scores   
4 $co        9    3    column coordinates  
5 $c1        9    3    column normed scores
other elements: cent norm 
> 
> 
> 
> cleanEx(); ..nameEx <- "dudi.acm"
> 
> ### * dudi.acm
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: dudi.acm
> ### Title: Multiple Correspondence Analysis
> ### Aliases: dudi.acm acm.burt acm.disjonctif boxplot.acm
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(ours)
> summary(ours)
 altit  deniv  cloiso domain boise  hetra  favor  inexp  citat  depart 
 1: 8   1:13   1:12   1: 9   1:10   1:19   1:15   1:20   1:22   AHP:5  
 2:17   2:14   2: 4   2:13   2:15   2: 5   2:12   2:10   2: 7   AM :4  
 3:13   3:11   3:22   3:16   3:13   3:14   3:11   3: 8   3: 4   D  :5  
                                                         4: 5   HP :8  
                                                                HS :4  
                                                                I  :5  
                                                                S  :7  
> boxplot(dudi.acm(ours, scan = FALSE))
> ## Not run: 
> ##D data(banque)
> ##D banque.acm <- dudi.acm(banque, scann = FALSE, nf = 3)
> ##D scatter.dudi(banque.acm)
> ##D 
> ##D apply(banque.acm$cr, 2, mean)
> ##D banque.acm$eig[1:banque.acm$nf] # the same thing
> ##D boxplot.acm(banque.acm)
> ##D 
> ##D scatter(banque.acm)
> ##D 
> ##D s.value(banque.acm$li, banque.acm$li[,3])
> ##D 
> ##D bb <- acm.burt(banque, banque)
> ##D bbcoa <- dudi.coa(bb, scann = FALSE)
> ##D plot(banque.acm$c1[,1], bbcoa$c1[,1])
> ##D # mca and coa of Burt table. Lebart & coll. section 1.4
> ##D 
> ##D bd <- acm.disjonctif(banque)
> ##D bdcoa <- dudi.coa(bd, scann = FALSE)
> ##D plot(banque.acm$li[,1], bdcoa$li[,1]) 
> ##D # mca and coa of disjonctive table. Lebart & coll. section 1.4
> ##D plot(banque.acm$co[,1], dudi.coa(bd, scann = FALSE)$co[,1]) 
> ## End(Not run)
> 
> 
> cleanEx(); ..nameEx <- "dudi.coa"
> 
> ### * dudi.coa
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: dudi.coa
> ### Title: Correspondence Analysis
> ### Aliases: dudi.coa
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(rpjdl)
> chisq.test(rpjdl$fau)$statistic
Warning in chisq.test(rpjdl$fau) : Chi-squared approximation may be incorrect
X-squared 
 7323.597 
> rpjdl.coa <- dudi.coa(rpjdl$fau, scannf = FALSE, nf = 4)
> sum(rpjdl.coa$eig)*rpjdl.coa$N # the same
[1] 7323.597
> 
> par(mfrow = c(1,2))
> s.label(rpjdl.coa$co, clab = 0.6, lab = rpjdl$frlab)
> s.label(rpjdl.coa$li, clab = 0.6)
> par(mfrow = c(1,1))
> 
> data(bordeaux)
> db <- dudi.coa(bordeaux, scan = FALSE)
> db
Duality diagramm
class: coa dudi
$call: dudi.coa(df = bordeaux, scannf = FALSE)

$nf: 2 axis-components saved
$rank: 3
eigen values: 0.5906 0.1102 0.03109
  vector length mode    content       
1 $cw    4      numeric column weights
2 $lw    5      numeric row weights   
3 $eig   3      numeric eigen values  

  data.frame nrow ncol content             
1 $tab       5    4    modified array      
2 $li        5    2    row coordinates     
3 $l1        5    2    row normed scores   
4 $co        4    2    column coordinates  
5 $c1        4    2    column normed scores
other elements: N 
> score(db)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "dudi.dec"
> 
> ### * dudi.dec
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: dudi.dec
> ### Title: Decentred Correspondence Analysis
> ### Aliases: dudi.dec
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(ichtyo)
> dudi1 <- dudi.dec(ichtyo$tab, ichtyo$eff, scan = FALSE)
> sum(apply(ichtyo$tab, 2, function(x)
+     chisq.test(x, p = ichtyo$eff/sum(ichtyo$eff))$statistic))
Warning in chisq.test(x, p = ichtyo$eff/sum(ichtyo$eff)) : 
	 Chi-squared approximation may be incorrect
Warning in chisq.test(x, p = ichtyo$eff/sum(ichtyo$eff)) : 
	 Chi-squared approximation may be incorrect
Warning in chisq.test(x, p = ichtyo$eff/sum(ichtyo$eff)) : 
	 Chi-squared approximation may be incorrect
Warning in chisq.test(x, p = ichtyo$eff/sum(ichtyo$eff)) : 
	 Chi-squared approximation may be incorrect
[1] 2851.051
> sum(dudi1$eig) * sum(ichtyo$eff) # the same
[1] 2851.051
> 
> s.class(dudi1$li, ichtyo$dat, wt = ichtyo$eff/sum(ichtyo$eff))
> 
> 
> 
> cleanEx(); ..nameEx <- "dudi.fca"
> 
> ### * dudi.fca
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: dudi.fca
> ### Title: Fuzzy Correspondence Analysis
> ### Aliases: dudi.fca prep.fuzzy.var
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> w1 <- matrix(c(1,0,0,2,1,1,0,2,2,0,1,0,1,1,1,0,1,3,1,0), 4, 5)
> w1 <- data.frame(w1)
> w2 <- prep.fuzzy.var(w1, c(2,3))
1 missing data found in block 1 
1 missing data found in block 2 
> w1
  X1 X2 X3 X4 X5
1  1  1  2  1  1
2  0  1  0  1  3
3  0  0  1  1  1
4  2  2  0  0  0
> w2
         X1        X2        X3        X4        X5
1 0.5000000 0.5000000 0.5000000 0.2500000 0.2500000
2 0.0000000 1.0000000 0.0000000 0.2500000 0.7500000
3 0.3333333 0.6666667 0.3333333 0.3333333 0.3333333
4 0.5000000 0.5000000 0.2777778 0.2777778 0.4444444
> attributes(w2)
$names
[1] "X1" "X2" "X3" "X4" "X5"

$row.names
[1] "1" "2" "3" "4"

$class
[1] "data.frame"

$col.blocks
FV1 FV2 
  2   3 

$row.w
[1] 0.25 0.25 0.25 0.25

$col.freq
[1] 0.3333333 0.6666667 0.2777778 0.2777778 0.4444444

$col.num
[1] 1 1 2 2 2
Levels: 1 2

> 
> data(bsetal97)
> w <- prep.fuzzy.var(bsetal97$biol, bsetal97$biol.blo)
17 missing data found in block 1 
14 missing data found in block 2 
28 missing data found in block 3 
8 missing data found in block 4 
5 missing data found in block 5 
19 missing data found in block 6 
10 missing data found in block 7 
5 missing data found in block 8 
2 missing data found in block 9 
12 missing data found in block 10 
> scatter(dudi.fca(w, scann = FALSE, nf = 3), csub = 3, clab.moda = 1.5)
> 
> w1 <- prep.fuzzy.var(bsetal97$biol, bsetal97$biol.blo)
17 missing data found in block 1 
14 missing data found in block 2 
28 missing data found in block 3 
8 missing data found in block 4 
5 missing data found in block 5 
19 missing data found in block 6 
10 missing data found in block 7 
5 missing data found in block 8 
2 missing data found in block 9 
12 missing data found in block 10 
> w2 <- prep.fuzzy.var(bsetal97$ecol, bsetal97$ecol.blo)
6 missing data found in block 1 
16 missing data found in block 2 
5 missing data found in block 3 
9 missing data found in block 4 
15 missing data found in block 5 
47 missing data found in block 6 
6 missing data found in block 7 
> d1 <- dudi.fca(w1, scann = FALSE, nf = 3)
> d2 <- dudi.fca(w2, scann = FALSE, nf = 3)
> plot(coinertia(d1, d2, scann = FALSE))
> 
> 
> 
> cleanEx(); ..nameEx <- "dudi.hillsmith"
> 
> ### * dudi.hillsmith
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: dudi.hillsmith
> ### Title: Ordination of Tables mixing quantitative variables and factors
> ### Aliases: dudi.hillsmith
> ### Keywords: multivariate
> 
> ### ** Examples
> 
>    data(dunedata)
>    attributes(dunedata$envir$use)$class <- "factor"   # use dudi.mix for ordered data
>    dd1 <- dudi.hillsmith(dunedata$envir, scann = FALSE)
>    scatter.dudi(dd1, clab.r = 1, clab.c = 1.5)
>   
> 
> 
> cleanEx(); ..nameEx <- "dudi.mix"
> 
> ### * dudi.mix
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: dudi.mix
> ### Title: Ordination of Tables mixing quantitative variables and factors
> ### Aliases: dudi.mix
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(dunedata)
> dd1 <- dudi.mix(dunedata$envir, scann = FALSE)
> scatter.dudi(dd1, clab.r = 1, clab.c = 1.5)
> 
> dd2 <- dudi.mix(dunedata$envir, scann = FALSE, add = TRUE)
> scatter.dudi(dd2, clab.r = 1, clab.c = 1.5)
> 
> 
> 
> cleanEx(); ..nameEx <- "dudi.nsc"
> 
> ### * dudi.nsc
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: dudi.nsc
> ### Title: Non symmetric correspondence analysis
> ### Aliases: dudi.nsc
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(housetasks)
> nsc1 <- dudi.nsc(housetasks, scan = FALSE)
> s.label(nsc1$c1, clab = 1.25)
> s.arrow(nsc1$li, add.pl = TRUE, clab = 0.75) # see ref p.383
> 
> 
> 
> cleanEx(); ..nameEx <- "dudi.pca"
> 
> ### * dudi.pca
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: dudi.pca
> ### Title: Principal Component Analysis
> ### Aliases: dudi.pca
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(deug)
> deug.dudi <- dudi.pca(deug$tab, center = deug$cent, 
+     scale = FALSE, scan = FALSE)
> par(mfrow = c(2,2))
> s.class(deug.dudi$li, deug$result, cpoint = 1)
> s.arrow(deug.dudi$c1, lab = names(deug$tab))
> deug.dudi1 <- dudi.pca(deug$tab, center = TRUE, 
+     scale = TRUE, scan = FALSE)
> s.class(deug.dudi1$li, deug$result, cpoint = 1)
> s.corcircle(deug.dudi1$co, lab = names(deug$tab), 
+     full = FALSE, box = TRUE)
> par(mfrow = c(1,1))
> 
> # for interpretations
> par(mfrow = c(3,3))
> par(mar = c(2.1,2.1,2.1,1.1))
> for(i in 1:9) { 
+ hist(deug.dudi$tab[,i], xlim = c(-40,40), breaks = seq(-45, 35, by = 5), 
+     prob = TRUE, right = FALSE, main = names(deug$tab)[i], xlab = "", 
+     ylim = c(0,0.10))
+ abline(v = 0, lwd = 3)
+ }
> par(mfrow = c(1,1))
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "dudi.pco"
> 
> ### * dudi.pco
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: dudi.pco
> ### Title: Principal Coordinates Analysis
> ### Aliases: dudi.pco scatter.pco
> ### Keywords: array multivariate
> 
> ### ** Examples
> 
> data(yanomama)
> gen <- quasieuclid(as.dist(yanomama$gen))
> geo <- quasieuclid(as.dist(yanomama$geo))
> ant <- quasieuclid(as.dist(yanomama$ant))
> geo1 <- dudi.pco(geo, scann = FALSE, nf = 3)
> gen1 <- dudi.pco(gen, scann = FALSE, nf = 3)
> ant1 <- dudi.pco(ant, scann = FALSE, nf = 3)
> plot(coinertia(ant1, gen1, scann = FALSE))
> 
> 
> 
> cleanEx(); ..nameEx <- "dunedata"
> 
> ### * dunedata
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: dunedata
> ### Title: Dune Meadow Data
> ### Aliases: dunedata
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(dunedata)
> summary(dunedata$envir)
       A1            moisture       manure           use    management
 Min.   : 2.800   Min.   :1.0   Min.   :0.00   hayfield:7   BF:3      
 1st Qu.: 3.500   1st Qu.:1.0   1st Qu.:0.00   both    :8   HF:5      
 Median : 4.200   Median :2.0   Median :2.00   grazing :5   NM:6      
 Mean   : 4.850   Mean   :2.9   Mean   :1.75                SF:6      
 3rd Qu.: 5.725   3rd Qu.:5.0   3rd Qu.:3.00                          
 Max.   :11.500   Max.   :5.0   Max.   :4.00                          
> is.ordered(dunedata$envir$use)
[1] TRUE
> score(dudi.mix(dunedata$envir, scan = FALSE))
> 
> 
> 
> cleanEx(); ..nameEx <- "ecg"
> 
> ### * ecg
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ecg
> ### Title: Electrocardiogram data
> ### Aliases: ecg
> ### Keywords: datasets
> 
> ### ** Examples
> 
> ## Not run: 
> ##D # figure 130 in Percival and Walden (2000)
> ##D if (require(waveslim) == TRUE) { 
> ##D data(ecg)
> ##D ecg.level <- haar2level(ecg)
> ##D ecg.haar <- orthobasis.haar(length(ecg))
> ##D ecg.mld <- mld(ecg, ecg.haar, ecg.level, plot = FALSE)
> ##D res <- cbind.data.frame(apply(ecg.mld[,1:5],1,sum), ecg.mld[,6:11])
> ##D par(mfrow = c(8,1))
> ##D par(mar = c(2, 5, 1.5, 0.6))
> ##D plot(as.ts(ecg), ylab = "ECG")
> ##D apply(res, 2, function(x) plot(as.ts(x), ylim = range(res),
> ##D  ylab = ""))
> ##D par(mfrow = c(1,1))
> ##D }
> ## End(Not run)
> 
> 
> 
> cleanEx(); ..nameEx <- "ecomor"
> 
> ### * ecomor
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ecomor
> ### Title: Ecomorphological Convergence
> ### Aliases: ecomor
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(ecomor)
> ric <- apply(ecomor$habitat, 2, sum)
> s.corcircle(dudi.pca(log(ecomor$morpho), scan = FALSE)$co)
> 
> forsub <- data.frame(t(apply(ecomor$forsub, 1,
+      function (x) x/sum(x))))
> pca1 <- dudi.pca(forsub, scan = FALSE, scale = FALSE)
> s.arrow(pca1$c1)
> w <- as.matrix(forsub)
> s.label(w, clab = 0, add.p = TRUE, cpoi = 2)
> 
> diet <- data.frame(t(apply(ecomor$diet, 1,
+      function (x) x/sum(x))))
> pca2 <- dudi.pca(diet, scan = FALSE, scale = FALSE)
> s.arrow(pca2$c1)
> w <- as.matrix(diet)
> s.label(w, clab = 0, add.p = TRUE, cpoi = 2)
> ## Not run: 
> ##D dmorpho <- dist.quant(log(ecomor$morpho), 3)
> ##D dhabitat <- dist.binary(ecomor$habitat, 1)
> ##D 
> ##D mantel.randtest(dmorpho, dhabitat)
> ##D RV.rtest(pcoscaled(dmorpho), pcoscaled(dhabitat), 999)
> ##D procuste.randtest(pcoscaled(dmorpho), pcoscaled(dhabitat))
> ##D 
> ##D ecophy <- taxo2phylog(ecomor$taxo)
> ##D table.phylog(ecomor$habitat, ecophy, clabel.n = 0.5, f = 0.6,
> ##D      clabel.c = 0.75, clabel.r = 0.5, csi = 0.75, cleg = 0)
> ##D plot.phylog(ecophy, clabel.n = 0.75, clabel.l = 0.75,
> ##D      labels.l = ecomor$labels[,"latin"])
> ##D dtaxo <- ecophy$Wdist
> ##D mantel.randtest(dmorpho, dtaxo)
> ##D mantel.randtest(dhabitat, dtaxo)
> ## End(Not run)
> 
> 
> cleanEx(); ..nameEx <- "elec88"
> 
> ### * elec88
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: elec88
> ### Title: Electoral Data
> ### Aliases: elec88
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(elec88)
> apply(elec88$tab, 2, mean)
 Mitterand     Chirac      Barre     Le.Pen   Lajoinie   Waechter     Juquin 
34.3063830 20.2670213 16.4680851 13.7606383  6.7106383  3.8638298  2.1670213 
Laguillier    Boussel 
 2.0574468  0.3989362 
> summary(elec88$res)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   0.40    2.10    6.80   11.11   16.50   34.10 
> 
> par(mfrow = c(2,2))
> plot(elec88$area[,2:3], type = "n", asp = 1)
> lpoly <- split(elec88$area[,2:3], elec88$area[,1])
> lapply(lpoly, function(x) {points (x,type = "l");invisible()})
$D1
NULL

$D10
NULL

$D11
NULL

$D12
NULL

$D13
NULL

$D14
NULL

$D15
NULL

$D16
NULL

$D17
NULL

$D18
NULL

$D19
NULL

$D2
NULL

$D21
NULL

$D22
NULL

$D23
NULL

$D24
NULL

$D25
NULL

$D26
NULL

$D27
NULL

$D28
NULL

$D29
NULL

$D3
NULL

$D30
NULL

$D31
NULL

$D32
NULL

$D33
NULL

$D34
NULL

$D35
NULL

$D36
NULL

$D37
NULL

$D38
NULL

$D39
NULL

$D4
NULL

$D40
NULL

$D41
NULL

$D42
NULL

$D43
NULL

$D44
NULL

$D45
NULL

$D46
NULL

$D47
NULL

$D48
NULL

$D49
NULL

$D5
NULL

$D50
NULL

$D51
NULL

$D52
NULL

$D53
NULL

$D54
NULL

$D55
NULL

$D56
NULL

$D57
NULL

$D58
NULL

$D59
NULL

$D6
NULL

$D60
NULL

$D61
NULL

$D62
NULL

$D63
NULL

$D64
NULL

$D65
NULL

$D66
NULL

$D67
NULL

$D68
NULL

$D69
NULL

$D7
NULL

$D70
NULL

$D71
NULL

$D72
NULL

$D73
NULL

$D74
NULL

$D75
NULL

$D76
NULL

$D77
NULL

$D78
NULL

$D79
NULL

$D8
NULL

$D80
NULL

$D81
NULL

$D82
NULL

$D83
NULL

$D84
NULL

$D85
NULL

$D86
NULL

$D87
NULL

$D88
NULL

$D89
NULL

$D9
NULL

$D90
NULL

$D91
NULL

$D92
NULL

$D93
NULL

$D94
NULL

$D95
NULL

> polygon(elec88$area[elec88$area$V1=="D25", 2:3], col = 1)
> area.plot(elec88$area, graph = elec88$neig, lwdg = 1)
> polygon(elec88$area[elec88$area$V1=="D25", 2:3], col = 1)
> pca1 <- dudi.pca(elec88$tab, scal = FALSE, scan = FALSE)
> area.plot(elec88$area, val = elec88$xy[,1] + elec88$xy[,2])
> area.plot(elec88$area, val = pca1$li[,1], sub = "F1 PCA", 
+     csub = 2, cleg = 1.5)
> par(mfrow = c(1,1))
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "escopage"
> 
> ### * escopage
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: escopage
> ### Title: K-tables of wine-tasting
> ### Aliases: escopage
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(escopage)
> w <- data.frame(scale(escopage$tab))
> w <- ktab.data.frame(w, escopage$blo)
> names(w)[1:4] <- escopage$tab.names
> plot(mfa(w, scan = FALSE))
> 
> 
> 
> cleanEx(); ..nameEx <- "euro123"
> 
> ### * euro123
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: euro123
> ### Title: Triangular Data
> ### Aliases: euro123
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(euro123)
> par(mfrow = c(2,2))
> triangle.plot(euro123$in78, addaxes = TRUE)
> triangle.plot(euro123$in86, addaxes = TRUE)
> triangle.plot(euro123$in97, addaxes = TRUE)
> triangle.biplot(euro123$in78, euro123$in97)
> par(mfrow = c(1,1))
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "fission"
> 
> ### * fission
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: fission
> ### Title: Fission pattern and heritable morphological traits
> ### Aliases: fission
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(fission)
> fis.phy <- newick2phylog(fission$tre)
> table.phylog(fission$tab[names(fis.phy$leaves),], fis.phy, csi = 2)
> gearymoran(fis.phy$Amat, fission$tab)
class: krandtest 
test number:   5 
permutation number:   999 
  test obs    P(X<=obs) P(X>=obs)
1 GM1  -0.228 0.047     0.955    
2 GM2  -0.127 0.233     0.771    
3 GM3  -0.054 0.554     0.448    
4 GN1  -0.045 0.502     0.501    
5 GN2  -0.203 0.089     0.913    

> 
> 
> 
> cleanEx(); ..nameEx <- "foucart"
> 
> ### * foucart
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: foucart
> ### Title: K-tables Correspondence Analysis with the same rows and the same
> ###   columns
> ### Aliases: foucart plot.foucart print.foucart
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(bf88)
> fou1 <- foucart(bf88, scann = FALSE, nf = 3)
> fou1
Foucart's  COA
class: foucart coa dudi
$call: foucart(X = bf88, scannf = FALSE, nf = 3)
table  number: 6 

$nf: 3 axis-components saved
$rank: 3
eigen values: 0.5278 0.3591 0.3235
blo    vector       6      blocks
 vector length mode    content       
 $cw    4      numeric column weights
 $lw    79     numeric row weights   
 $eig   3      numeric eigen values  

 data.frame nrow ncol content             
 $tab       79   4    modified array      
 $li        79   3    row coordinates     
 $l1        79   3    row normed scores   
 $co        4    3    column coordinates  
 $c1        4    3    column normed scores

     **** Intrastructure ****

 data.frame nrow ncol content                     
 $Tli       474  3    row coordinates (each table)
 $Tco       24   3    col coordinates (each table)
 $TL        474  2    factors for Tli             
 $TC        24   2    factors for Tco             

> plot(fou1)
> 
> data(meaudret)
> l1 <- split(meaudret$fau, meaudret$plan$dat)
> l1 <- lapply(l1, function(x) 
+     {row.names(x) <- paste("Sta",1:5,sep="");x})
> fou2 <- foucart(l1, scan = FALSE)
> kplot(fou2, clab.r = 2)
> 
> 
> 
> cleanEx(); ..nameEx <- "friday87"
> 
> ### * friday87
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: friday87
> ### Title: Faunistic K-tables
> ### Aliases: friday87
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(friday87)
> wfri <- data.frame(scale(friday87$fau, scal = FALSE))
> wfri <- ktab.data.frame(wfri, friday87$fau.blo, 
+     tabnames = friday87$tab.names)
> kplot(sepan(wfri), clab.r = 2, clab.c = 1)
> 
> 
> 
> cleanEx(); ..nameEx <- "fruits"
> 
> ### * fruits
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: fruits
> ### Title: Pair of Tables
> ### Aliases: fruits
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(fruits)
> par(mfrow = c(2,2))
> pcajug <- dudi.pca(fruits$jug, scann = FALSE)
> s.corcircle(pcajug$co)
> s.class(pcajug$li, fac = fruits$type)
> 
> pcavar <- dudi.pca(fruits$var, scann = FALSE)
> s.corcircle(pcavar$co)
> s.class(pcavar$li, fac = fruits$type)
> 
> par(mfrow = c(1,1))
> plot(coinertia(pcajug, pcavar, scan = FALSE))
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "fuzzygenet"
> 
> ### * fuzzygenet
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: fuzzygenet
> ### Title: Reading a table of genetic data (diploid individuals)
> ### Aliases: fuzzygenet
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(casitas)
> casitas[1:5, ]
     Aat    Amy    Es1    Es2   Es10    Hbb   Gpd1   Idh1   Mod1   Mod2    Mpi
1 100100 080080 094094 100100 100100 120120 100100 100100 110110 100100 100100
2 100100 080100 094094 100100 100100 120120 100100 100125 110110 100100 100100
3 100100 080080 094094 100100 100100 120120 100100 100100 110110 100100 100100
4 100100 080080 094094 100100 100100 120120 100100 100125 100100 100100 100100
5 100100 080080 094094 100100 100100 120120 100100 100100 110110 100100 100100
      Np   Pgm1   Pgm2    Sod
1 100100 100100 100100 100100
2 100100 100100 100100 100100
3 100100 100100 100100 100100
4 100100 100100 100100 100100
5 100100 100100 100100 100100
> casitas <- fuzzygenet(casitas)
> attributes(casitas)
$names
 [1] "L01.1" "L01.2" "L02.1" "L02.2" "L03.1" "L03.2" "L04.1" "L04.2" "L04.3"
[10] "L05.1" "L05.2" "L06.1" "L06.2" "L07.1" "L07.2" "L07.3" "L08.1" "L08.2"
[19] "L08.3" "L08.4" "L09.1" "L09.2" "L09.3" "L10.1" "L10.2" "L11.1" "L11.2"
[28] "L12.1" "L12.2" "L12.3" "L12.4" "L13.1" "L13.2" "L13.3" "L14.1" "L14.2"
[37] "L15.1" "L15.2"

$row.names
 [1] "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10" "11" "12" "13" "14" "15"
[16] "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30"
[31] "31" "32" "33" "34" "35" "36" "37" "38" "39" "40" "41" "42" "43" "44" "45"
[46] "46" "47" "48" "49" "50" "51" "52" "53" "54" "55" "56" "57" "58" "59" "60"
[61] "61" "62" "63" "64" "65" "66" "67" "68" "69" "70" "71" "72" "73" "74"

$class
[1] "data.frame"

$col.blocks
L01 L02 L03 L04 L05 L06 L07 L08 L09 L10 L11 L12 L13 L14 L15 
  2   2   2   3   2   2   3   4   3   2   2   4   3   2   2 

$all.names
     L01.1      L01.2      L02.1      L02.2      L03.1      L03.2      L04.1 
 "Aat.080"  "Aat.100"  "Amy.080"  "Amy.100"  "Es1.094"  "Es1.100"  "Es2.095" 
     L04.2      L04.3      L05.1      L05.2      L06.1      L06.2      L07.1 
 "Es2.098"  "Es2.100" "Es10.060" "Es10.100"  "Hbb.110"  "Hbb.120" "Gpd1.095" 
     L07.2      L07.3      L08.1      L08.2      L08.3      L08.4      L09.1 
"Gpd1.100" "Gpd1.105" "Idh1.050" "Idh1.080" "Idh1.100" "Idh1.125" "Mod1.100" 
     L09.2      L09.3      L10.1      L10.2      L11.1      L11.2      L12.1 
"Mod1.110" "Mod1.120" "Mod2.100" "Mod2.120"  "Mpi.100"  "Mpi.120"   "Np.080" 
     L12.2      L12.3      L12.4      L13.1      L13.2      L13.3      L14.1 
  "Np.085"   "Np.090"   "Np.100" "Pgm1.060" "Pgm1.080" "Pgm1.100" "Pgm2.080" 
     L14.2      L15.1      L15.2 
"Pgm2.100"  "Sod.080"  "Sod.100" 

$loc.names
   L01    L02    L03    L04    L05    L06    L07    L08    L09    L10    L11 
 "Aat"  "Amy"  "Es1"  "Es2" "Es10"  "Hbb" "Gpd1" "Idh1" "Mod1" "Mod2"  "Mpi" 
   L12    L13    L14    L15 
  "Np" "Pgm1" "Pgm2"  "Sod" 

$row.w
 [1] 0.01351351 0.01351351 0.01351351 0.01351351 0.01351351 0.01351351
 [7] 0.01351351 0.01351351 0.01351351 0.01351351 0.01351351 0.01351351
[13] 0.01351351 0.01351351 0.01351351 0.01351351 0.01351351 0.01351351
[19] 0.01351351 0.01351351 0.01351351 0.01351351 0.01351351 0.01351351
[25] 0.01351351 0.01351351 0.01351351 0.01351351 0.01351351 0.01351351
[31] 0.01351351 0.01351351 0.01351351 0.01351351 0.01351351 0.01351351
[37] 0.01351351 0.01351351 0.01351351 0.01351351 0.01351351 0.01351351
[43] 0.01351351 0.01351351 0.01351351 0.01351351 0.01351351 0.01351351
[49] 0.01351351 0.01351351 0.01351351 0.01351351 0.01351351 0.01351351
[55] 0.01351351 0.01351351 0.01351351 0.01351351 0.01351351 0.01351351
[61] 0.01351351 0.01351351 0.01351351 0.01351351 0.01351351 0.01351351
[67] 0.01351351 0.01351351 0.01351351 0.01351351 0.01351351 0.01351351
[73] 0.01351351 0.01351351

$col.freq
      L01.1       L01.2       L02.1       L02.2       L03.1       L03.2 
0.148648649 0.851351351 0.750000000 0.250000000 0.702702703 0.297297297 
      L04.1       L04.2       L04.3       L05.1       L05.2       L06.1 
0.081081081 0.195945946 0.722972973 0.116438356 0.883561644 0.465753425 
      L06.2       L07.1       L07.2       L07.3       L08.1       L08.2 
0.534246575 0.375000000 0.534722222 0.090277778 0.101351351 0.006756757 
      L08.3       L08.4       L09.1       L09.2       L09.3       L10.1 
0.608108108 0.283783784 0.128378378 0.797297297 0.074324324 0.689189189 
      L10.2       L11.1       L11.2       L12.1       L12.2       L12.3 
0.310810811 0.794520548 0.205479452 0.061643836 0.089041096 0.061643836 
      L12.4       L13.1       L13.2       L13.3       L14.1       L14.2 
0.787671233 0.027397260 0.130136986 0.842465753 0.082191781 0.917808219 
      L15.1       L15.2 
0.121621622 0.878378378 

$col.num
 [1] Aat  Aat  Amy  Amy  Es1  Es1  Es2  Es2  Es2  Es10 Es10 Hbb  Hbb  Gpd1 Gpd1
[16] Gpd1 Idh1 Idh1 Idh1 Idh1 Mod1 Mod1 Mod1 Mod2 Mod2 Mpi  Mpi  Np   Np   Np  
[31] Np   Pgm1 Pgm1 Pgm1 Pgm2 Pgm2 Sod  Sod 
15 Levels: Aat Amy Es1 Es10 Es2 Gpd1 Hbb Idh1 Mod1 Mod2 Mpi Np Pgm1 ... Sod

> rm(casitas)
> 
> 
> 
> cleanEx(); ..nameEx <- "gearymoran"
> 
> ### * gearymoran
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: gearymoran
> ### Title: Moran's I and Geary'c randomization tests for spatial and
> ###   phylogenetic autocorrelation
> ### Aliases: gearymoran
> ### Keywords: spatial ts
> 
> ### ** Examples
> 
> # a spatial example
> data(mafragh)
> tab0 <- (as.data.frame(scalewt(mafragh$mil)))
> bilis0 <- neig2mat(mafragh$neig)
> gm0 <- gearymoran(bilis0, tab0, 999)
> gm0
class: krandtest 
test number:   11 
permutation number:   999 
   test       obs   P(X<=obs) P(X>=obs)
1  Argile     0.424 1         0.001    
2  Limon      0.338 1         0.001    
3  Sable      0.099 0.945     0.057    
4  K2O        0.273 1         0.001    
5  Mg++       0.186 0.998     0.004    
6  Na+/100g   0.267 1         0.001    
7  K+         0.661 1         0.001    
8  Conduc     0.3   1         0.001    
9  Capa_Reten 0.201 1         0.002    
10 Na+/l      0.243 1         0.001    
11 Altitude   0.595 1         0.001    

> plot(gm0, nclass = 20)
> 
> ## Not run: 
> ##D # a phylogenetic example
> ##D data(mjrochet)
> ##D mjr.phy <- newick2phylog(mjrochet$tre)
> ##D mjr.tab <- log(mjrochet$tab)
> ##D gearymoran(mjr.phy$Amat, mjr.tab)
> ##D gearymoran(mjr.phy$Wmat, mjr.tab)
> ##D par(mfrow = c(1,2))
> ##D table.value(mjr.phy$Wmat, csi = 0.25, clabel.r = 0)
> ##D table.value(mjr.phy$Amat, csi = 0.35, clabel.r = 0)
> ##D par(mfrow = c(1,1))
> ## End(Not run)
> 
> 
> cleanEx(); ..nameEx <- "genet"
> 
> ### * genet
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: genet
> ### Title: A class of data: tables of populations and alleles
> ### Aliases: genet char2genet count2genet freq2genet
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(casitas)
> casitas[24,]
      Aat    Amy    Es1    Es2   Es10    Hbb   Gpd1   Idh1   Mod1   Mod2    Mpi
24 100100 080100 094094 100100 000000 000000 000000 125125 110110 100100 100100
       Np   Pgm1   Pgm2    Sod
24 000000 100100 100100 100100
> casitas.pop <- as.factor(rep(c("dome", "cast", "musc", "casi"), c(24,11,9,30)))
> casi.genet <- char2genet(casitas, casitas.pop, complete=TRUE)
> names(casi.genet$tab) 
 [1] "L01.1" "L01.2" "L02.1" "L02.2" "L03.1" "L03.2" "L04.1" "L04.2" "L05.1"
[10] "L05.2" "L05.3" "L06.1" "L06.2" "L06.3" "L07.1" "L07.2" "L08.1" "L08.2"
[19] "L08.3" "L08.4" "L09.1" "L09.2" "L09.3" "L10.1" "L10.2" "L11.1" "L11.2"
[28] "L12.1" "L12.2" "L12.3" "L12.4" "L13.1" "L13.2" "L13.3" "L14.1" "L14.2"
[37] "L15.1" "L15.2"
> casi.genet$tab[,1:8] 
       L01.1     L01.2     L02.1     L02.2     L03.1     L03.2     L04.1
P1 0.2500000 0.7500000 0.8333333 0.1666667 0.8500000 0.1500000 0.0000000
P2 0.3181818 0.6818182 1.0000000 0.0000000 0.2272727 0.7727273 0.0000000
P3 0.0000000 1.0000000 0.8125000 0.1875000 1.0000000 0.0000000 0.0000000
P4 0.0000000 1.0000000 0.0000000 1.0000000 0.0000000 1.0000000 0.9444444
        L04.2
P1 1.00000000
P2 1.00000000
P3 1.00000000
P4 0.05555556
> casi.genet$pop.names
    P1     P2     P3     P4 
"casi" "cast" "dome" "musc" 
> casi.genet$loc.names
   L01    L02    L03    L04    L05    L06    L07    L08    L09    L10    L11 
 "Aat"  "Amy"  "Es1" "Es10"  "Es2" "Gpd1"  "Hbb" "Idh1" "Mod1" "Mod2"  "Mpi" 
   L12    L13    L14    L15 
  "Np" "Pgm1" "Pgm2"  "Sod" 
> casi.genet$all.names
     L01.1      L01.2      L02.1      L02.2      L03.1      L03.2      L04.1 
 "Aat.080"  "Aat.100"  "Amy.080"  "Amy.100"  "Es1.094"  "Es1.100" "Es10.060" 
     L04.2      L05.1      L05.2      L05.3      L06.1      L06.2      L06.3 
"Es10.100"  "Es2.095"  "Es2.098"  "Es2.100" "Gpd1.095" "Gpd1.100" "Gpd1.105" 
     L07.1      L07.2      L08.1      L08.2      L08.3      L08.4      L09.1 
 "Hbb.110"  "Hbb.120" "Idh1.050" "Idh1.080" "Idh1.100" "Idh1.125" "Mod1.100" 
     L09.2      L09.3      L10.1      L10.2      L11.1      L11.2      L12.1 
"Mod1.110" "Mod1.120" "Mod2.100" "Mod2.120"  "Mpi.100"  "Mpi.120"   "Np.080" 
     L12.2      L12.3      L12.4      L13.1      L13.2      L13.3      L14.1 
  "Np.085"   "Np.090"   "Np.100" "Pgm1.060" "Pgm1.080" "Pgm1.100" "Pgm2.080" 
     L14.2      L15.1      L15.2 
"Pgm2.100"  "Sod.080"  "Sod.100" 
> casi.genet$loc.blocks # number of allelic forms by loci
L01 L02 L03 L04 L05 L06 L07 L08 L09 L10 L11 L12 L13 L14 L15 
  2   2   2   2   3   3   2   4   3   2   2   4   3   2   2 
> casi.genet$loc.fac # factor classifying the allelic forms by locus
 [1] L01 L01 L02 L02 L03 L03 L04 L04 L05 L05 L05 L06 L06 L06 L07 L07 L08 L08 L08
[20] L08 L09 L09 L09 L10 L10 L11 L11 L12 L12 L12 L12 L13 L13 L13 L14 L14 L15 L15
Levels: L01 L02 L03 L04 L05 L06 L07 L08 L09 L10 L11 L12 L13 L14 L15
> casi.genet$pop.loc # table populations loci
   L01 L02 L03 L04 L05 L06 L07 L08 L09 L10 L11 L12 L13 L14 L15
P1  30  30  30  30  30  30  30  30  30  30  30  30  30  30  30
P2  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11
P3  24  24  24  23  24  23  23  24  24  24  24  23  24  24  24
P4   9   9   9   9   9   8   9   9   9   9   8   9   8   8   9
> names(casi.genet$comp)
 [1] "L01.1" "L01.2" "L02.1" "L02.2" "L03.1" "L03.2" "L04.1" "L04.2" "L05.1"
[10] "L05.2" "L05.3" "L06.1" "L06.2" "L06.3" "L07.1" "L07.2" "L08.1" "L08.2"
[19] "L08.3" "L08.4" "L09.1" "L09.2" "L09.3" "L10.1" "L10.2" "L11.1" "L11.2"
[28] "L12.1" "L12.2" "L12.3" "L12.4" "L13.1" "L13.2" "L13.3" "L14.1" "L14.2"
[37] "L15.1" "L15.2"
> casi.genet$comp[1:4,]
   L01.1 L01.2 L02.1 L02.2 L03.1 L03.2 L04.1 L04.2 L05.1 L05.2 L05.3 L06.1
01     0     2     2     0     1     1     0     2     2     0     0     0
02     1     1     2     0     2     0     0     2     0     0     2     0
03     1     1     1     1     2     0     0     2     2     0     0     0
04     0     2     1     1     2     0     0     2     0     0     2     2
   L06.2 L06.3 L07.1 L07.2 L08.1 L08.2 L08.3 L08.4 L09.1 L09.2 L09.3 L10.1
01     0     2     1     1     0     0     1     1     0     2     0     0
02     0     2     1     1     0     0     2     0     0     2     0     1
03     2     0     1     1     0     0     2     0     0     2     0     0
04     0     0     2     0     0     0     2     0     0     2     0     0
   L10.2 L11.1 L11.2 L12.1 L12.2 L12.3 L12.4 L13.1 L13.2 L13.3 L14.1 L14.2
01     2     2     0     0     0     0     2     0     0     2     0     2
02     1     2     0     0     0     0     2     0     0     2     0     2
03     2     2     0     0     0     0     2     0     0     2     0     2
04     2     2     0     0     0     0     2     0     0     2     0     2
   L15.1 L15.2
01     0     2
02     0     2
03     0     2
04     0     2
> casi.genet$comp.pop
 [1] P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1
[26] P1 P1 P1 P1 P1 P2 P2 P2 P2 P2 P2 P2 P2 P2 P2 P2 P3 P3 P3 P3 P3 P3 P3 P3 P3
[51] P3 P3 P3 P3 P3 P3 P3 P3 P3 P3 P3 P3 P3 P3 P4 P4 P4 P4 P4 P4 P4 P4
Levels: P1 P2 P3 P4
> casi.genet$center
      L01.1       L01.2       L02.1       L02.2       L03.1       L03.2 
0.148648649 0.851351351 0.750000000 0.250000000 0.702702703 0.297297297 
      L04.1       L04.2       L05.1       L05.2       L05.3       L06.1 
0.116438356 0.883561644 0.081081081 0.195945946 0.722972973 0.375000000 
      L06.2       L06.3       L07.1       L07.2       L08.1       L08.2 
0.534722222 0.090277778 0.465753425 0.534246575 0.101351351 0.006756757 
      L08.3       L08.4       L09.1       L09.2       L09.3       L10.1 
0.608108108 0.283783784 0.128378378 0.797297297 0.074324324 0.689189189 
      L10.2       L11.1       L11.2       L12.1       L12.2       L12.3 
0.310810811 0.794520548 0.205479452 0.061643836 0.089041096 0.061643836 
      L12.4       L13.1       L13.2       L13.3       L14.1       L14.2 
0.787671233 0.027397260 0.130136986 0.842465753 0.082191781 0.917808219 
      L15.1       L15.2 
0.121621622 0.878378378 
> apply(casi.genet$tab,2,mean)
     L01.1      L01.2      L02.1      L02.2      L03.1      L03.2      L04.1 
0.14204545 0.85795455 0.66145833 0.33854167 0.51931818 0.48068182 0.23611111 
     L04.2      L05.1      L05.2      L05.3      L06.1      L06.2      L06.3 
0.76388889 0.05719697 0.37247475 0.57032828 0.56666667 0.37916667 0.05416667 
     L07.1      L07.2      L08.1      L08.2      L08.3      L08.4      L09.1 
0.54431818 0.45568182 0.13446970 0.01136364 0.47935606 0.37481061 0.09365530 
     L09.2      L09.3      L10.1      L10.2      L11.1      L11.2      L12.1 
0.75356692 0.15277778 0.80833333 0.19166667 0.59943182 0.40056818 0.12500000 
     L12.2      L12.3      L12.4      L13.1      L13.2      L13.3      L14.1 
0.14772727 0.12500000 0.60227273 0.06250000 0.26704545 0.67045455 0.10729167 
     L14.2      L15.1      L15.2 
0.89270833 0.25000000 0.75000000 
> casi.genet$pop.loc[,"L15"]
[1] 30 11 24  9
> casi.genet$tab[, c("L15.1","L15.2")]
   L15.1 L15.2
P1     0     1
P2     0     1
P3     0     1
P4     1     0
> class(casi.genet)
[1] "genet" "list" 
> casitas.coa <- dudi.coa(casi.genet$comp, scannf = FALSE)
> s.class(casitas.coa$li,casi.genet$comp.pop)
> 
> 
> 
> cleanEx(); ..nameEx <- "granulo"
> 
> ### * granulo
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: granulo
> ### Title: Granulometric Curves
> ### Aliases: granulo
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(granulo)
> w <- t(apply(granulo$tab, 1, function (x) x / sum(x)))
> w <- data.frame(w)
> wtr <- data.frame(t(w))
> wmoy <- data.frame(matrix(apply(wtr, 1, mean), 1))
> d1 <- dudi.pca(w, scal = FALSE, scan = FALSE)
> wmoy <- suprow.default(d1, wmoy)$lisup
> s.arrow(d1$c1, clab = 1.5)
> s.distri(d1$c1, wtr, cstar = 0.33, cell = 0, 
+     axesell = FALSE, add.p = TRUE, clab = 0.75)
> s.label(wmoy, cpoi = 5, clab = 0, add.p = TRUE)
> 
> 
> 
> cleanEx(); ..nameEx <- "gridrowcol"
> 
> ### * gridrowcol
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: gridrowcol
> ### Title: Complete regular grid analysis
> ### Aliases: gridrowcol
> ### Keywords: spatial
> 
> ### ** Examples
> 
> w <- gridrowcol(8,5)
> par(mfrow = c(1,2))
> area.plot(w$area,center = w$xy, graph = w$neig, clab = 0.75)
> area.plot(w$area,center = w$xy, graph = w$neig, clab = 0.75,
+  label = as.character(1:40))
> par(mfrow = c(1,1))
> 
> par(mfrow = c(5,8))
> for(k in 1:39)
+     s.value(w$xy, w$orthobasis[,k], csi = 3, cleg = 0, csub = 2,
+      sub = as.character(signif(attr(w$orthobasis, "values")[k],3)),
+       incl = FALSE, addax = FALSE, cgr = 0, ylim = c(0,10))
> par(mfrow = c(1,1))
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "housetasks"
> 
> ### * housetasks
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: housetasks
> ### Title: Contingency Table
> ### Aliases: housetasks
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(housetasks)
> nsc1 <- dudi.nsc(housetasks, scan = FALSE)
> s.label(nsc1$c1, clab = 1.25)
> s.arrow(nsc1$li, add.pl = TRUE, clab = 0.75) 
> 
> 
> 
> cleanEx(); ..nameEx <- "humDNAm"
> 
> ### * humDNAm
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: humDNAm
> ### Title: human mitochondrial DNA restriction data
> ### Aliases: humDNAm
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(humDNAm)
> dpcoahum <- dpcoa(humDNAm$samples, sqrt(humDNAm$distances), scan = FALSE, nf = 2)
> plot(dpcoahum, csize = 1.5)
> 
> 
> 
> cleanEx(); ..nameEx <- "ichtyo"
> 
> ### * ichtyo
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ichtyo
> ### Title: Point sampling of fish community
> ### Aliases: ichtyo
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(ichtyo)
> dudi1 <- dudi.dec(ichtyo$tab, ichtyo$eff, scan = FALSE)
> s.class(dudi1$li, ichtyo$dat, wt = ichtyo$eff / sum(ichtyo$eff))
> 
> 
> 
> cleanEx(); ..nameEx <- "inertia.dudi"
> 
> ### * inertia.dudi
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: inertia.dudi
> ### Title: Statistics of inertia in a one-table analysis
> ### Aliases: inertia.dudi
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(housetasks)
> coa1 <- dudi.coa(housetasks, scann = FALSE)
> inertia.dudi(coa1, col = TRUE, row = FALSE)$col.rel
            Comp1 Comp2 con.tra
Wife        -8019 -1524    2700
Alternating   -48 -1051    1057
Husband      7720 -2075    3421
Jointly       207  9773    2823
> 
> 
> 
> cleanEx(); ..nameEx <- "irishdata"
> 
> ### * irishdata
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: irishdata
> ### Title: Geary's Irish Data
> ### Aliases: irishdata
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(irishdata)
> par(mfrow = c(2,2))
> area.plot(irishdata$area, lab = irishdata$county.names, clab = 0.75)
> area.plot(irishdata$area)
> apply(irishdata$contour, 1, function(x) segments(x[1],x[2],x[3],x[4], 
+     lwd = 3))
NULL
> s.corcircle(dudi.pca(irishdata$tab, scan = FALSE)$co)
> score <- dudi.pca(irishdata$tab, scan = FALSE, nf = 1)$li$Axis1
> names(score) <- row.names(irishdata$tab)
> area.plot(irishdata$area, score)
> par(mfrow = c(1,1))
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "is.euclid"
> 
> ### * is.euclid
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: is.euclid
> ### Title: Is a Distance Matrix Euclidean ?
> ### Aliases: is.euclid summary.dist mat2dist dist2mat
> ### Keywords: array
> 
> ### ** Examples
> 
> w <- matrix(runif(10000), 100, 100)
> w <- dist(w)
> summary(w)
Class: dist 
Distance matrix by lower triangle : d21, d22, ..., d2n, d32, ...
Size: 100 
Labels: 
call: dist(x = w)
method: euclidean 
Euclidean matrix (Gower 1966): TRUE 
> is.euclid (w) # TRUE
[1] TRUE
> w <- quasieuclid(w) # no correction need in: quasieuclid(w)
Warning in quasieuclid(w) : Euclidean distance found : no correction need
> w <- lingoes(w) # no correction need in: lingoes(w)
Warning in lingoes(w) : Euclidean distance found : no correction need
> w <- cailliez(w) # no correction need in: cailliez(w)
Warning in cailliez(w) : Euclidean distance found : no correction need
> rm(w)
> 
> 
> 
> cleanEx(); ..nameEx <- "julliot"
> 
> ### * julliot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: julliot
> ### Title: Seed dispersal
> ### Aliases: julliot
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(julliot)
> par(mfrow = c(3,3))
> ## Not run: 
> ##D for(k in 1:7)
> ##D     area.plot(julliot$area,val = log(julliot$tab[,k]+1),
> ##D      sub = names(julliot$tab)[k], csub = 2.5)
> ## End(Not run)
> 
> if (require(splancs, quiet = TRUE)){
+     par(mfrow = c(3,3))
+     for(k in 1:7)
+       s.image(julliot$xy, log(julliot$tab[,k]+1), kgrid = 3, span = 0.25,
+       sub = names(julliot$tab)[k], csub = 2.5)
+ }

Spatial Point Pattern Analysis Code in S-Plus

 Version 2 - Spatial and Space-Time analysis
> 
> ## Not run: 
> ##D par(mfrow = c(3,3))
> ##D for(k in 1:7) {
> ##D     area.plot(julliot$area)
> ##D     s.value(julliot$xy, scalewt(log(julliot$tab[,k]+1)),
> ##D      sub = names(julliot$tab)[k],csub = 2.5, add.p = TRUE)
> ##D }
> ## End(Not run)
> par(mfrow = c(3,3))
> for(k in 1:7)
+     s.value(julliot$xy,log(julliot$tab[,k]+1),
+      sub = names(julliot$tab)[k], csub = 2.5)
> 
> ## Not run: 
> ##D if (require(spdep, quiet = TRUE)){
> ##D par(mfrow = c(1,1))
> ##D neig0 <- nb2neig(dnearneigh(as.matrix(julliot$xy), 1, 1.8))
> ##D s.label(julliot$xy, neig = neig0, clab = 0.75, incl = FALSE,
> ##D  addax = FALSE, grid = FALSE)
> ##D 
> ##D gearymoran(neig.util.LtoG(neig0), log(julliot$tab+1))
> ##D orthogram(log(julliot$tab[,3]+1), ortho = scores.neig(neig0),
> ##D  nrepet = 9999)}
> ## End(Not run)
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "jv73"
> 
> ### * jv73
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: jv73
> ### Title: K-tables Multi-Regions
> ### Aliases: jv73
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(jv73)
> s.label(jv73$xy, contour = jv73$contour, incl = FALSE, 
+     clab = 0.75)
> s.class(jv73$xy, jv73$fac.riv, add.p = TRUE, cell = 0,
+     axese = FALSE, csta = 0, cpoi = 0, clab = 1.25)
> 
> w <- split(jv73$morpho, jv73$fac.riv)
> w <- lapply(w, function(x) t(dudi.pca(x, scann = FALSE)))
> w <- ktab.list.dudi(w)
> kplot(sepan(w), perm = TRUE, clab.r = 0, clab.c = 2, show = FALSE)
> 
> 
> 
> cleanEx(); ..nameEx <- "kcponds"
> 
> ### * kcponds
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: kcponds
> ### Title: Ponds in a nature reserve
> ### Aliases: kcponds
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(kcponds)
> 
> par(mfrow=c(3,1))
> area.plot(kcponds$area)
> s.label(kcponds$xy,add.p = TRUE, cpoi = 2, clab = 0)
> s.label(kcponds$xy,add.p = TRUE, cpoi = 3, clab = 0)
> 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 <- as.numeric(scalewt(kcponds$tab$N))
> s.value(kcponds$xy, w, cleg = 2, sub = "Nitrogen concentration",
+      csub = 4, possub = "topright", include = FALSE)
> par(mfrow = c(1,1))
> ## Not run: 
> ##D par(mfrow=c(3,1))
> ##D pca1 <- dudi.pca(kcponds$tab, scan = FALSE, nf = 4)
> ##D if (require(maptools, quiet = TRUE) & require(spdep, quiet = TRUE)) {
> ##D multi1 <- multispati(pca1, nb2listw(neig2nb(kcponds$neig)),
> ##D      scan = FALSE, nfposi = 2, nfnega = 1)
> ##D summary(multi1)}
> ##D par(mfrow = c(1,1))
> ## End(Not run)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "kdist"
> 
> ### * kdist
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: kdist
> ### Title: the class of objects 'kdist' (K distance matrices)
> ### Aliases: kdist c.kdist print.kdist [.kdist as.data.frame.kdist
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> # starting from a list of matrices 
> data(yanomama)
> lapply(yanomama,class)  
$geo
[1] "matrix"

$gen
[1] "matrix"

$ant
[1] "matrix"

> kd1 = kdist(yanomama)
> print(kd1)
List of distances matrices
call: kdist(yanomama)
class: kdist
number of distances: 3
size: 19
labels:
 [1] "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10" "11" "12" "13" "14" "15"
[16] "16" "17" "18" "19"
geo: non euclidean distance
gen: non euclidean distance
ant: non euclidean distance
> 
> # giving the correlations of Mantel's test
> cor(as.data.frame(kd1))
          geo       gen       ant
geo 1.0000000 0.5098684 0.8428053
gen 0.5098684 1.0000000 0.2995506
ant 0.8428053 0.2995506 1.0000000
> pairs(as.data.frame(kd1))
> 
> # starting from a list of objects 'dist'
> data(friday87)
> fri.w <- ktab.data.frame(friday87$fau, friday87$fau.blo, 
+     tabnames = friday87$tab.names)
> fri.kd = lapply(1:10, function(x) dist.binary(fri.w[[x]],2))
> names(fri.kd) = friday87$tab.names
> unlist(lapply(fri.kd,class)) # a list of distances
    Hemiptera       Odonata   Trichoptera Ephemeroptera    Coleoptera 
       "dist"        "dist"        "dist"        "dist"        "dist" 
      Diptera   Hydracarina  Malacostraca      Mollusca   Oligochaeta 
       "dist"        "dist"        "dist"        "dist"        "dist" 
> fri.kd = kdist(fri.kd)
> fri.kd
List of distances matrices
call: kdist(fri.kd)
class: kdist
number of distances: 10
size: 16
labels:
 [1] "Q" "P" "R" "J" "E" "C" "D" "K" "B" "A" "G" "M" "L" "F" "H" "N"
Hemiptera: euclidean distance
Odonata: euclidean distance
Trichoptera: euclidean distance
Ephemeroptera: euclidean distance
Coleoptera: euclidean distance
Diptera: euclidean distance
Hydracarina: euclidean distance
Malacostraca: euclidean distance
Mollusca: euclidean distance
Oligochaeta: euclidean distance
> s.corcircle(dudi.pca(as.data.frame(fri.kd), scan = FALSE)$co)
> 
> # starting from several distances
> data(ecomor)
> d1 <- dist.binary(ecomor$habitat, 1)
> d2 <- dist.prop(ecomor$forsub, 5)
> d3 <- dist.prop(ecomor$diet, 5)
> d4 <- dist.quant(ecomor$morpho, 3)
> d5 <- taxo2phylog(ecomor$taxo)$Wdist
> ecomor.kd <- kdist(d1, d2, d3, d4, d5)
> names(ecomor.kd) = c("habitat", "forsub", "diet", "morpho", "taxo")
> class(ecomor.kd)
[1] "kdist"
> s.corcircle(dudi.pca(as.data.frame(ecomor.kd), scan = FALSE)$co)
> 
> data(bsetal97)
> X <- prep.fuzzy.var(bsetal97$biol, bsetal97$biol.blo)
17 missing data found in block 1 
14 missing data found in block 2 
28 missing data found in block 3 
8 missing data found in block 4 
5 missing data found in block 5 
19 missing data found in block 6 
10 missing data found in block 7 
5 missing data found in block 8 
2 missing data found in block 9 
12 missing data found in block 10 
> w1 <- attr(X, "col.num")
> w2 <- levels(w1)
> w3 <- lapply(w2, function(x) dist.quant(X[,w1==x], method = 1))
> names(w3) <- names(attr(X, "col.blocks"))
> w3 <- kdist(list = w3)
> s.corcircle(dudi.pca(as.data.frame(w3), scan = FALSE)$co)
> 
> data(rpjdl)
> w1 = lapply(1:10, function(x) dist.binary(rpjdl$fau, method = x))
> w2 = c("JACCARD", "SOCKAL_MICHENER", "SOCKAL_SNEATH_S4", "ROGERS_TANIMOTO")
> w2 = c(w2, "CZEKANOWSKI", "S9_GOWER_LEGENDRE", "OCHIAI", "SOKAL_SNEATH_S13")
> w2 <- c(w2, "Phi_PEARSON", "S2_GOWER_LEGENDRE")
> names(w1) <- w2
> w3 = kdist(list = w1)
> w4 <- dudi.pca(as.data.frame(w3), scan = FALSE)$co
> w4
                      Comp1       Comp2
JACCARD           0.9791013  0.18151963
SOCKAL_MICHENER   0.8693893 -0.48952277
SOCKAL_SNEATH_S4  0.9673038  0.17757093
ROGERS_TANIMOTO   0.8760634 -0.48095455
CZEKANOWSKI       0.9811773  0.18245217
S9_GOWER_LEGENDRE 0.8693893 -0.48952277
OCHIAI            0.9801507  0.18304997
SOKAL_SNEATH_S13  0.9882272  0.12063315
Phi_PEARSON       0.9955160  0.03143543
S2_GOWER_LEGENDRE 0.8342624  0.49634908
> 
> 
> 
> cleanEx(); ..nameEx <- "kdist2ktab"
> 
> ### * kdist2ktab
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: kdist2ktab
> ### Title: Transformation of K distance matrices (object 'kdist') into K
> ###   Euclidean representations (object 'ktab')
> ### Aliases: kdist2ktab
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(friday87)
> fri.w <- ktab.data.frame(friday87$fau, friday87$fau.blo, 
+     tabnames = friday87$tab.names)
> fri.kd <- lapply(1:10, function(x) dist.binary(fri.w[[x]], 10))
> names(fri.kd) <-  substr(friday87$tab.names, 1, 4)
> fri.kd <- kdist(fri.kd)
> fri.ktab = kdist2ktab(kd = fri.kd)
> fri.sepan = sepan(fri.ktab)
> plot(fri.sepan, csub = 3)
> 
> tapply(fri.sepan$Eig, fri.sepan$TC[,1], sum)
 1  2  3  4  5  6  7  8  9 10 
 1  1  1  1  1  1  1  1  1  1 
> # the sum of the eigenvalues is constant and equal to 1, for each K tables
> 
> fri.statis <- statis(fri.ktab, scan = FALSE, nf = 2)
> round(fri.statis$RV, dig = 2)
     Hemi Odon Tric Ephe Cole Dipt Hydr Mala Moll Olig
Hemi 1.00 0.84 0.89 0.72 0.93 0.91 0.94 0.87 0.90 0.87
Odon 0.84 1.00 0.86 0.71 0.86 0.88 0.85 0.83 0.87 0.81
Tric 0.89 0.86 1.00 0.82 0.93 0.93 0.91 0.89 0.92 0.89
Ephe 0.72 0.71 0.82 1.00 0.73 0.83 0.74 0.81 0.83 0.79
Cole 0.93 0.86 0.93 0.73 1.00 0.94 0.94 0.89 0.92 0.87
Dipt 0.91 0.88 0.93 0.83 0.94 1.00 0.92 0.91 0.94 0.89
Hydr 0.94 0.85 0.91 0.74 0.94 0.92 1.00 0.88 0.93 0.86
Mala 0.87 0.83 0.89 0.81 0.89 0.91 0.88 1.00 0.92 0.86
Moll 0.90 0.87 0.92 0.83 0.92 0.94 0.93 0.92 1.00 0.86
Olig 0.87 0.81 0.89 0.79 0.87 0.89 0.86 0.86 0.86 1.00
> 
> fri.mfa <- mfa(fri.ktab, scan = FALSE, nf = 2)
> fri.mcoa <- mcoa(fri.ktab, scan = FALSE, nf = 2)
> 
> apply(fri.statis$RV, 1, mean) 
     Hemi      Odon      Tric      Ephe      Cole      Dipt      Hydr      Mala 
0.8870267 0.8491046 0.9042958 0.7980208 0.9019560 0.9153542 0.8969580 0.8862786 
     Moll      Olig 
0.9084652 0.8693471 
> fri.statis$RV.tabw 
 [1] 0.3182568 0.3042336 0.3241214 0.2849738 0.3236518 0.3281335 0.3218042
 [8] 0.3176219 0.3256389 0.3114247
> plot(apply(fri.statis$RV, 1, mean), fri.statis$RV.tabw)
> plot(fri.statis$RV.tabw, fri.statis$RV.tabw)
> 
> 
> 
> cleanEx(); ..nameEx <- "kdisteuclid"
> 
> ### * kdisteuclid
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: kdisteuclid
> ### Title: a way to obtain Euclidean distance matrices
> ### Aliases: kdisteuclid
> ### Keywords: multivariate utilities
> 
> ### ** Examples
> 
> w <- c(0.8,0.8,0.377350269,0.8,0.377350269,0.377350269) # see ref.
> w <- kdist(w)
> w1 <- c(kdisteuclid(kdist(w), "lingoes"), kdisteuclid(kdist(w), "cailliez"),
+     kdisteuclid(kdist(w), "quasi"))
[1] "Lingoes constant = 0.0532050808642208"
[1] "Cailliez constant = 0.200000000448660"
[1] "First ev = 0.32 Last ev = -0.0532050808642208"
> print(w, print = TRUE)
List of distances matrices
call: kdist(w)
class: kdist
number of distances: 1
size: 4
labels:
[1] "1" "2" "3" "4"
w: non euclidean distance
  1         2         3         4
1                                
2 0.8000000                      
3 0.8000000 0.8000000            
4 0.3773503 0.3773503 0.3773503  

> print(w1, print = TRUE)
List of distances matrices
call: c.kdist(kdisteuclid(kdist(w), "lingoes"), kdisteuclid(kdist(w), 
    "cailliez"), kdisteuclid(kdist(w), "quasi"))
class: kdist
number of distances: 3
size: 4
labels:
[1] "1" "2" "3" "4"
kdisteuclid(kdist(w), "lingoes").w: euclidean distance
  1         2         3         4
1                                
2 0.8639503                      
3 0.8639503 0.8639503            
4 0.4988020 0.4988020 0.4988020  

kdisteuclid(kdist(w), "cailliez").w: euclidean distance
  1         2         3         4
1                                
2 1.0000000                      
3 1.0000000 1.0000000            
4 0.5773503 0.5773503 0.5773503  

kdisteuclid(kdist(w), "quasi").w: euclidean distance
  1         2         3         4
1                                
2 0.8000000                      
3 0.8000000 0.8000000            
4 0.4618802 0.4618802 0.4618802  

> 
> data(eurodist)
> par(mfrow = c(1, 3))
> eu1 <- kdist(eurodist) # an object of class 'dist'
> plot(data.frame(unclass(c(eu1, kdisteuclid(eu1, "quasi")))), asp = 1)
[1] "First ev = 19538377.0895428 Last ev = -2251844.33173616"
> title(main = "Quasi") ; abline(0,1)
> plot(data.frame(unclass(c(eu1, kdisteuclid(eu1, "lingoes")))), asp = 1)
[1] "Lingoes constant = 2251844.33173616"
> title(main = "Lingoes") ; abline(0,1)
> plot(data.frame(unclass(c(eu1, kdisteuclid(eu1, "cailliez")))), asp = 1)
[1] "Cailliez constant = 2132.67849519794"
> title(main = "Cailliez") ; abline(0,1)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "kplot"
> 
> ### * kplot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: kplot
> ### Title: Generic Function for Multiple Graphs in a K-tables Analysis
> ### Aliases: kplot
> ### Keywords: multivariate hplot
> 
> ### ** Examples
> 
> methods(plot)
 [1] plot.Date*          plot.HoltWinters*   plot.POSIXct*      
 [4] plot.POSIXlt*       plot.TukeyHSD       plot.acf*          
 [7] plot.between        plot.coinertia      plot.corkdist      
[10] plot.data.frame*    plot.decomposed.ts* plot.default       
[13] plot.dendrogram*    plot.density        plot.discrimin     
[16] plot.dpcoa          plot.ecdf           plot.factor*       
[19] plot.formula*       plot.foucart        plot.hclust*       
[22] plot.histogram*     plot.isoreg*        plot.krandtest     
[25] plot.lm             plot.mcoa           plot.medpolish*    
[28] plot.mfa            plot.mlm            plot.multispati    
[31] plot.niche          plot.pcaiv          plot.phylog        
[34] plot.ppr*           plot.prcomp*        plot.princomp*     
[37] plot.procuste       plot.profile.nls*   plot.pta           
[40] plot.randtest       plot.rlq            plot.rtest         
[43] plot.sepan          plot.spec           plot.spec.coherency
[46] plot.spec.phase     plot.statis         plot.stepfun       
[49] plot.stl*           plot.table*         plot.ts            
[52] plot.tskernel*      plot.within        

   Non-visible functions are asterisked
> methods(scatter)
[1] scatter.acm    scatter.coa    scatter.dudi   scatter.fca    scatter.pco   
[6] scatter.smooth
> methods(kplot)
[1] kplot.foucart   kplot.mcoa      kplot.mfa       kplot.pta      
[5] kplot.sepan     kplot.sepan.coa kplot.statis   
> 
> 
> 
> cleanEx(); ..nameEx <- "kplot.foucart"
> 
> ### * kplot.foucart
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: kplot.foucart
> ### Title: Multiple Graphs for the Foucart's Correspondence Analysis
> ### Aliases: kplot.foucart
> ### Keywords: multivariate hplot
> 
> ### ** Examples
> 
> data(bf88)
> fou1 <- foucart(bf88, scann = FALSE, nf = 3)
> kplot(fou1, clab.r = 0, clab.c = 2, csub = 3)
> 
> 
> 
> cleanEx(); ..nameEx <- "kplot.mcoa"
> 
> ### * kplot.mcoa
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: kplot.mcoa
> ### Title: Multiple Graphs for a Multiple Co-inertia Analysis
> ### Aliases: kplot.mcoa
> ### Keywords: multivariate hplot
> 
> ### ** Examples
> 
> data(friday87)
> w1 <- data.frame(scale(friday87$fau, scal = FALSE))
> w2 <- ktab.data.frame(w1, friday87$fau.blo, 
+     tabnames = friday87$tab.names)
> mcoa1 <- mcoa(w2, "lambda1", scan = FALSE)
> kplot(mcoa1, clab = 2, csub = 2, cpoi = 3, opt = "axis")
> 
> kplot(mcoa1, mfrow = c(3,4), clab = 2, csub = 3, cpoi = 3)
> 
> kplot(mcoa1, clab = 2, csub = 3, cpoi = 3, mfrow = c(3,4), 
+     opt = "columns")
> 
> 
> 
> cleanEx(); ..nameEx <- "kplot.mfa"
> 
> ### * kplot.mfa
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: kplot.mfa
> ### Title: Multiple Graphs for a Multiple Factorial Analysis
> ### Aliases: kplot.mfa
> ### Keywords: multivariate hplot
> 
> ### ** Examples
> 
> data(friday87)
> w1 <- data.frame(scale(friday87$fau, scal = FALSE))
> w2 <- ktab.data.frame(w1, friday87$fau.blo, 
+     tabnames = friday87$tab.names)
> mfa1 <- mfa(w2, scann = FALSE)
> kplot(mfa1)
> 
> 
> 
> cleanEx(); ..nameEx <- "kplot.pta"
> 
> ### * kplot.pta
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: kplot.pta
> ### Title: Multiple Graphs for a Partial Triadic Analysis
> ### Aliases: kplot.pta
> ### Keywords: multivariate hplot
> 
> ### ** Examples
> 
> data(meaudret)
> wit1 <- within(dudi.pca(meaudret$fau, scan = FALSE, scal = FALSE), 
+     meaudret$plan$dat, scan = FALSE)
> kta1 <- ktab.within(wit1, colnames = rep(c("S1","S2","S3","S4","S5"), 4))
> kta2 <- t(kta1) ; pta1 <- pta(kta2, scann = FALSE)
> kplot(pta1, clab = 1.5, csub = 3)
> kplot(pta1, clab = 1.5, csub = 3, which.graph = 3, mfrow = c(2,2))
> 
> 
> 
> cleanEx(); ..nameEx <- "kplot.sepan"
> 
> ### * kplot.sepan
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: kplot.sepan
> ### Title: Multiple Graphs for Separated Analyses in a K-tables
> ### Aliases: kplot.sepan kplot.sepan.coa
> ### Keywords: multivariate hplot
> 
> ### ** Examples
> 
> data(escopage)
> w <- data.frame(scale(escopage$tab))
> w <- ktab.data.frame(w, escopage$blo, tabnames = escopage$tab.names)
> sep1 <- sepan(w)
> kplot(sep1, show = FALSE)
> 
> data(friday87)
> w <- data.frame(scale(friday87$fau, scal = FALSE))
> w <- ktab.data.frame(w, friday87$fau.blo, tabnames = friday87$tab.names)
> kplot(sepan(w), clab.r = 1.25, clab.c = 0, csub = 3)
> 
> data(microsatt)
> w <- dudi.coa(data.frame(t(microsatt$tab)), scann = FALSE)
> loci.fac <- factor(rep(microsatt$loci.names, microsatt$loci.eff))
> wit <- within(w, loci.fac, scann = FALSE)
> microsatt.ktab <- ktab.within(wit)
> kplot.sepan.coa(sepan(microsatt.ktab), show = FALSE, clab.c = 0, 
+     mfrow = c(3,3), clab.r = 1.5)
> 
> 
> 
> cleanEx(); ..nameEx <- "kplot.statis"
> 
> ### * kplot.statis
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: kplot.statis
> ### Title: Multiple Graphs of a STATIS Analysis
> ### Aliases: kplot.statis
> ### Keywords: multivariate hplot
> 
> ### ** Examples
> 
> data(jv73)
> dudi1 <- dudi.pca(jv73$poi, scann = FALSE, scal = FALSE)
> wit1 <- within(dudi1, jv73$fac.riv, scann = FALSE)
> kta3 <- ktab.within(wit1)
> data(jv73)
> statis3 <- statis(kta3, scann = FALSE)
> kplot(statis3, traj = TRUE, arrow = FALSE, unique = TRUE, 
+     clab = 0, csub = 3, cpoi = 3)
> 
> 
> 
> cleanEx(); ..nameEx <- "krandtest"
> 
> ### * krandtest
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: krandtest
> ### Title: Class of the Permutation Tests (in C).
> ### Aliases: krandtest plot.krandtest print.krandtest
> ### Keywords: methods
> 
> ### ** Examples
> 
> wkrandtest <- data.frame(eg1=c(0, rnorm(200)))
> for (x0 in c(1.2,2.4,3.4,5.4,20.4)) {
+     wkrandtest <- cbind.data.frame(wkrandtest, c(x0, rnorm(200)))
+ }
> names(wkrandtest) <- paste ("Test",1:6,sep="_")
> class(wkrandtest) <- "krandtest"
> wkrandtest
class: krandtest 
test number:   6 
permutation number:   200 
  test   obs  P(X<=obs) P(X>=obs)
1 Test_1 0    0.535     0.475    
2 Test_2 1.2  0.895     0.115    
3 Test_3 2.4  0.995     0.015    
4 Test_4 3.4  1         0.005    
5 Test_5 5.4  1         0.005    
6 Test_6 20.4 1         0.005    

> plot(wkrandtest)
> 
> 
> 
> cleanEx(); ..nameEx <- "ktab"
> 
> ### * ktab
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ktab
> ### Title: the class of objects 'ktab' (K-tables)
> ### Aliases: ktab is.ktab c.ktab [.ktab print.ktab t.ktab row.names.ktab
> ###   row.names<-.ktab col.names col.names.ktab col.names<-
> ###   col.names<-.ktab tab.names tab.names.ktab tab.names<-
> ###   tab.names<-.ktab ktab.util.names ktab.util.addfactor<-
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(friday87)
> wfri <- data.frame(scale(friday87$fau, scal = FALSE))
> wfri <- ktab.data.frame(wfri, friday87$fau.blo)
> wfri[2:4]
class: ktab 

tab number:   3 
  data.frame    nrow ncol
1 Odonata       16   7   
2 Trichoptera   16   13  
3 Ephemeroptera 16   4   

  vector length mode    content       
4 $lw    16     numeric row weigths   
5 $cw    24     numeric column weights
6 $blo   3      numeric column numbers
7 $tabw  0      NULL    array weights 

   data.frame nrow ncol content                         
8  $TL        48   2    Factors Table number Line number
9  $TC        24   2    Factors Table number Col number 
10 $T4        12   2    Factors Table number 1234       

11 $call: "[.ktab"(object = wfri, selection = 2:4)

names :
Odonata : B1 B2 B3 B4 B5 B6 B7 
Trichoptera : C1 C2 C3 C4 C5 C6 C7 C8 C9 Ca Cb Cc Cd 
Ephemeroptera : D1 D2 D3 D4 

Col weigths :
Odonata : 1 1 1 1 1 1 1 
Trichoptera : 1 1 1 1 1 1 1 1 1 1 1 1 1 
Ephemeroptera : 1 1 1 1 

Row weigths :
0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625
> c(wfri[2:4], wfri[5])
class: ktab 

tab number:   4 
  data.frame    nrow ncol
1 Odonata       16   7   
2 Trichoptera   16   13  
3 Ephemeroptera 16   4   
4 Coleoptera    16   13  

  vector length mode    content       
5 $lw    16     numeric row weigths   
6 $cw    37     numeric column weights
7 $blo   4      numeric column numbers
8 $tabw  0      NULL    array weights 

   data.frame nrow ncol content                         
9  $TL        64   2    Factors Table number Line number
10 $TC        37   2    Factors Table number Col number 
11 $T4        16   2    Factors Table number 1234       

12 $call: c.ktab(wfri[2:4], wfri[5])

names :
Odonata : B1 B2 B3 B4 B5 B6 B7 
Trichoptera : C1 C2 C3 C4 C5 C6 C7 C8 C9 Ca Cb Cc Cd 
Ephemeroptera : D1 D2 D3 D4 
Coleoptera : E1 E2 E3 E4 E5 E6 E7 E8 E9 Ea Eb Ec Ed 

Col weigths :
Odonata : 1 1 1 1 1 1 1 
Trichoptera : 1 1 1 1 1 1 1 1 1 1 1 1 1 
Ephemeroptera : 1 1 1 1 
Coleoptera : 1 1 1 1 1 1 1 1 1 1 1 1 1 

Row weigths :
0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625
> 
> data(meaudret)
> wit1 <- within.pca(meaudret$mil, meaudret$plan$dat, scan = FALSE, 
+     scal = "partial")
> kta1 <- ktab.within(wit1, colnames = rep(c("S1","S2","S3","S4","S5"), 4))
> kta2 <- t(kta1)
> kplot(sepan(kta2), clab.r = 1.5, clab.c = 0.75)
> 
> 
> 
> cleanEx(); ..nameEx <- "ktab.data.frame"
> 
> ### * ktab.data.frame
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ktab.data.frame
> ### Title: Creation of K-tables from a data frame
> ### Aliases: ktab.data.frame
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(escopage)
> wescopage <- data.frame(scalewt(escopage$tab))
> wescopage <- ktab.data.frame(wescopage, escopage$blo,
+         tabnames = escopage$tab.names)
> plot(sepan(wescopage))
> data(friday87)
> w <- data.frame(scale(friday87$fau, scal = FALSE))
> w <- ktab.data.frame(w, friday87$fau.blo, tabnames = friday87$tab.names)
> kplot(sepan(w))
> 
> 
> 
> cleanEx(); ..nameEx <- "ktab.list.df"
> 
> ### * ktab.list.df
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ktab.list.df
> ### Title: Creating a K-tables from a list of data frames.
> ### Aliases: ktab.list.df
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(jv73)
> l0 <- split(jv73$morpho, jv73$fac.riv)
> l0 <- lapply(l0, function(x) data.frame(t(scalewt(x))))
> kta <- ktab.list.df(l0)
> kplot(sepan(kta[c(2,5,7,10)]), perm = TRUE, clab.r = 1, clab.c = 1.5)
> 
> 
> 
> cleanEx(); ..nameEx <- "ktab.list.dudi"
> 
> ### * ktab.list.dudi
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ktab.list.dudi
> ### Title: Creation of a K-tables from a list of duality diagrams
> ### Aliases: ktab.list.dudi
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(euro123)
> pca1 <- dudi.pca(euro123$in78, scale = FALSE, scann = FALSE)
> pca2 <- dudi.pca(euro123$in86, scale = FALSE, scann = FALSE)
> pca3 <- dudi.pca(euro123$in97, scale = FALSE, scann = FALSE)
> ktabeuro <- ktab.list.dudi(list(pca1, pca2, pca3), 
+     tabnames = c("1978","1986","1997"))
> kplot(sepan(ktabeuro), mfr = c(2,2), clab.c = 1.5)
> 
> data(meaudret)
> w1 <- split(meaudret$mil,meaudret$plan$dat)
> ll <- lapply(w1, dudi.pca, scann = FALSE)
> kta <- ktab.list.dudi(ll, rownames <- paste("Station", 1:5, sep=""))
> kplot(sepan(kta), clab.r = 1.5, clab.c = 0.75)
> 
> data(jv73)
> w <- split(jv73$poi, jv73$fac.riv)
> wjv73poi <- lapply(w, dudi.pca, scal = FALSE, scan = FALSE)
> wjv73poi <- lapply(wjv73poi, t)
> wjv73poi <- ktab.list.dudi(wjv73poi)
> kplot(sepan(wjv73poi), permut = TRUE, traj = TRUE)
> 
> 
> 
> cleanEx(); ..nameEx <- "ktab.match2ktabs"
> 
> ### * ktab.match2ktabs
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ktab.match2ktabs
> ### Title: STATIS and Co-Inertia : Analysis of a series of paired
> ###   ecological tables
> ### Aliases: ktab.match2ktabs
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(meau)
> wit1 <- within.pca(meau$mil, meau$plan$dat, scan = FALSE, scal = "total")
> pcafau <- dudi.pca(meau$fau, scale = FALSE, scan = FALSE, nf = 2)
> wit2 <- within(pcafau, meau$plan$dat, scan = FALSE, nf = 2)
> kta1 <- ktab.within(wit1, colnames = rep(c("S1","S2","S3","S4","S5","S6"), 4))
> kta2 <- ktab.within(wit2, colnames = rep(c("S1","S2","S3","S4","S5","S6"), 4))
> kcoi <- ktab.match2ktabs(kta1, kta2)
> ptacoi <- pta(kcoi, scan = FALSE, nf = 2)
> plot(ptacoi)
> kplot(ptacoi)
> 
> 
> 
> cleanEx(); ..nameEx <- "ktab.within"
> 
> ### * ktab.within
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ktab.within
> ### Title: Process to go from a Within Analysis to a K-tables
> ### Aliases: ktab.within
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(bacteria)
> w1 <- data.frame(t(bacteria$espcodon))
> dudi1 <- dudi.coa(w1, scann = FALSE, nf = 4)
> wit1 <- within(dudi1, bacteria$code, scannf = FALSE)
> kta1 <- ktab.within(wit1)
> plot(statis(kta1, scann = FALSE))
> 
> kta2 <- kta1[kta1$blo>3]
> kplot(mfa(kta2, scann = FALSE))
> 
> 
> 
> cleanEx(); ..nameEx <- "lascaux"
> 
> ### * lascaux
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: lascaux
> ### Title: Genetic/Environment and types of variables
> ### Aliases: lascaux
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(lascaux)
> par(mfrow = c(2,2))
> 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")
> par(mfrow = c(1,1))
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "lingoes"
> 
> ### * lingoes
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: lingoes
> ### Title: Transformation of a Distance Matrix for becoming Euclidean
> ### Aliases: lingoes
> ### Keywords: array multivariate
> 
> ### ** Examples
> 
> data(capitales)
> d0 <- as.dist(capitales$df)
> is.euclid(d0) # FALSE
[1] FALSE
> d1 <- lingoes(d0, TRUE)
Lingoes constant = 2120982 
> # Lingoes constant = 2120982
> is.euclid(d1) # TRUE
[1] TRUE
> plot(d0, d1)
> x0 <- sort(unclass(d0))
> lines(x0, sqrt(x0^2 + 2 * 2120982), lwd = 3)
>  
> is.euclid(sqrt(d0^2 + 2 * 2120981), tol = 1e-10) # FALSE
[1] FALSE
> is.euclid(sqrt(d0^2 + 2 * 2120982), tol = 1e-10) # FALSE
[1] FALSE
> is.euclid(sqrt(d0^2 + 2 * 2120983), tol = 1e-10) 
[1] TRUE
>     # TRUE the smaller constant
> 
> 
> 
> cleanEx(); ..nameEx <- "lizards"
> 
> ### * lizards
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: lizards
> ### Title: Phylogeny and quantitative traits of lizards
> ### Aliases: lizards
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(lizards)
> w <- data.frame(scalewt(log(lizards$traits)))
> par(mfrow = c(1,2))
> wphy <- newick2phylog(lizards$hprA)
> table.phylog(w, wphy, csi = 3)
> wphy <- newick2phylog(lizards$hprB)
> table.phylog(w, wphy, csi = 3)
> par(mfrow = c(1,1))
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "macaca"
> 
> ### * macaca
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: macaca
> ### Title: Landmarks
> ### Aliases: macaca
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(macaca)
> par(mfrow = c(2,2))
> s.match(macaca$xy1, macaca$xy2, clab = 0)
> pro1 <- procuste(macaca$xy1, macaca$xy2, scal = FALSE)
> s.match(pro1$tab1, pro1$rot2, clab = 0.7)
> s.match(pro1$tab2, pro1$rot1, clab = 0.7)
> pro2 <- procuste(macaca$xy1, macaca$xy2)
> s.match(pro2$tab2, pro2$rot1, clab = 0.7)
> par(mfrow = c(1,1))
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "macon"
> 
> ### * macon
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: macon
> ### Title: Wine Tasting
> ### Aliases: macon
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(macon)
> s.corcircle(dudi.pca(macon, scan = FALSE)$co)
> 
> 
> 
> cleanEx(); ..nameEx <- "mafragh"
> 
> ### * mafragh
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mafragh
> ### Title: Phyto-Ecological Survey
> ### Aliases: mafragh
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(mafragh)
> par(mfrow = c(3,2))
> s.label(mafragh$xy, inc = FALSE, neig = mafragh$neig, 
+     sub = "Samples & Neighbourhood graph")
> coa1 <- dudi.coa(mafragh$flo, scan = FALSE)
> s.value(mafragh$xy, coa1$li[,1], sub = "Axis 1 - COA")
> pca1 <- dudi.pca(mafragh$xy, scan = FALSE)
> s.value(mafragh$xy, pca1$li[,1], sub = "Axis 1 - PCA")
> s.class(pca1$li, mafragh$partition, sub = "Plane 1-2 - PCA")
> s.class(coa1$li, mafragh$partition, sub = "Plane 1-2 - COA")
> s.chull(mafragh$xy, mafragh$partition, optchull = 1)
> par(mfrow=c(1,1))
> 
> ## Not run: 
> ##D link1 <- area2link(mafragh$area)
> ##D neig1 <- neig(mat01 = 1*(link1>0))
> ##D nb1 <- neig2nb(neig1)
> ##D par(mfrow = c(2,1))
> ##D area.plot(mafragh$area,center = mafragh$xy,clab=0.75)
> ##D area.plot(mafragh$area,center = mafragh$xy,graph=neig1)
> ##D if (require(maptools, quiet = TRUE) & require(spdep, quiet = TRUE)) {
> ##D     lw1 <- apply(link1,1,function(x) x[x>0])
> ##D     listw1 <- nb2listw(nb1,lw1)
> ##D     coa1 <- dudi.coa(mafragh$flo, scan = FALSE, nf = 4)
> ##D     ms1 <- multispati(coa1, listw1, scan = FALSE, nfp = 2, nfn = 0)
> ##D     summary(ms1)
> ##D     par(mfrow = c(2,2))
> ##D     barplot(coa1$eig)
> ##D     barplot(ms1$eig)
> ##D     s.corcircle(ms1$as)
> ##D     plot(coa1$li[,1], ms1$li[,1])
> ##D }
> ##D par(mfrow = c(1,1))
> ## End(Not run)
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "mantel.randtest"
> 
> ### * mantel.randtest
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mantel.randtest
> ### Title: Mantel test (correlation between two distance matrices (in C).)
> ### Aliases: mantel.randtest
> ### Keywords: array nonparametric
> 
> ### ** Examples
> 
> data(yanomama)
> gen <- quasieuclid(as.dist(yanomama$gen))
> geo <- quasieuclid(as.dist(yanomama$geo))
> plot(r1 <- mantel.randtest(geo,gen), main = "Mantel's test")
> r1
Monte-Carlo test
Observation: 0.5095199 
Call: mantel.randtest(m1 = geo, m2 = gen)
Based on 999 replicates
Simulated p-value: 0.002 
> 
> 
> 
> cleanEx(); ..nameEx <- "mantel.rtest"
> 
> ### * mantel.rtest
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mantel.rtest
> ### Title: Mantel test (correlation between two distance matrices (in R).)
> ### Aliases: mantel.rtest
> ### Keywords: array nonparametric
> 
> ### ** Examples
> 
>     data(yanomama)
>     gen <- quasieuclid(as.dist(yanomama$gen))
>     geo <- quasieuclid(as.dist(yanomama$geo))
>     plot(r1 <- mantel.rtest(geo,gen), main = "Mantel's test")
>     r1
Monte-Carlo test
Observation: 0.5095199 
Call: mantel.rtest(m1 = geo, m2 = gen)
Based on 99 replicates
Simulated p-value: 0.01 
> 
> 
> 
> cleanEx(); ..nameEx <- "maples"
> 
> ### * maples
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: maples
> ### Title: Phylogeny and quantitative traits of flowers
> ### Aliases: maples
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(maples)
> phy <- newick2phylog(maples$tre)
> dom <- maples$tab$Dom
> bif <- maples$tab$Bif
> orthogram(dom, phylog = phy)
class: krandtest 
test number:   4 
permutation number:   999 
  test  obs   P(X<=obs) P(X>=obs)
1 R2Max 0.664 1         0.001    
2 SkR2k 2.907 0.001     1        
3 Dmax  0.649 1         0.001    
4 SCE   2.676 1         0.001    

> orthogram(bif, phylog = phy)
class: krandtest 
test number:   4 
permutation number:   999 
  test  obs   P(X<=obs) P(X>=obs)
1 R2Max 0.606 1         0.002    
2 SkR2k 2.962 0.001     1        
3 Dmax  0.666 1         0.001    
4 SCE   2.538 1         0.001    

> par(mfrow = c(1,2))
> dotchart.phylog(phy, dom)
> dotchart.phylog(phy, bif, clabel.nodes = 0.7)
> par(mfrow = c(1,1))
> plot(bif,dom,pch = 20)
> abline(lm(dom~bif))
> summary(lm(dom~bif))

Call:
lm(formula = dom ~ bif)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.14842 -0.06725  0.00496  0.07620  0.11130 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.259967   0.105927  11.895 4.88e-09 ***
bif         -0.008654   0.001699  -5.093 0.000132 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Residual standard error: 0.08404 on 15 degrees of freedom
Multiple R-Squared: 0.6336,	Adjusted R-squared: 0.6091 
F-statistic: 25.94 on 1 and 15 DF,  p-value: 0.0001324 

> if (require(ape, quiet = TRUE)){
+ cor.test(bif,dom)
+ phylo <- read.tree(text = maples$tre)
+ pic.bif <- pic(bif, phylo)
+ pic.dom <- pic(dom, phylo)
+ cor.test(pic.bif, pic.dom)}
Loading required package: gee
Loading required package: nlme
Loading required package: lattice

	Pearson's product-moment correlation

data:  pic.bif and pic.dom 
t = -1.5823, df = 14, p-value = 0.1359
alternative hypothesis: true correlation is not equal to 0 
95 percent confidence interval:
 -0.7419510  0.1316186 
sample estimates:
       cor 
-0.3894993 

> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "mariages"
> 
> ### * mariages
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mariages
> ### Title: Correspondence Analysis Table
> ### Aliases: mariages
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(mariages)
> par(mfrow = c(2,2))
> w <- dudi.coa(mariages, scan = FALSE, nf = 3)
> scatter(w, met = 1, posi = "bottom")
> scatter(w, met = 2, posi = "bottom")
> scatter(w, met = 3, posi = "bottom")
> score(w, 3)
> par(mfrow = c(1,1))
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "mcoa"
> 
> ### * mcoa
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mcoa
> ### Title: Multiple CO-inertia Analysis
> ### Aliases: mcoa print.mcoa summary.mcoa plot.mcoa
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(friday87)
> w1 <- data.frame(scale(friday87$fau, scal = FALSE))
> w2 <- ktab.data.frame(w1, friday87$fau.blo, tabnames = friday87$tab.names)
> mcoa1 <- mcoa(w2, "lambda1", scan = FALSE)
> mcoa1
Multiple Co-inertia Analysis
list of class mcoa

$pseudoeig: 16 pseudo eigen values
6.459 4.07 1.914 1.644 0.98 ...

$call: mcoa(X = w2, option = "lambda1", scannf = FALSE)

$nf: 3 axis saved

   data.frame nrow ncol content                                 
1  $SynVar    16   3    synthetic scores                        
2  $axis      91   3    co-inertia axis                         
3  $Tli       160  3    co-inertia coordinates                  
4  $Tl1       160  3    co-inertia normed scores                
5  $Tax       40   3    inertia axes onto co-inertia axis       
6  $Tco       91   3    columns onto synthetic scores           
7  $TL        160  2    factors for Tli Tl1                     
8  $TC        91   2    factors for Tco                         
9  $T4        40   2    factors for Tax                         
10 $lambda    10   3    eigen values (separate analysis)        
11 $cov2      10   3    pseudo eigen values (synthetic analysis)
other elements: NULL
> summary(mcoa1)
Multiple Co-inertia Analysis
Array n° 1 Hemiptera Rows 16 Cols 11 
  Iner  Iner+ Var   Var+  cos2  cov2 
1 1     1     0.688 0.688 0.68  0.468
2 0.748 1.748 0.979 1.667 0.82  0.803
3 0.384 2.132 0.356 2.023 0.668 0.238

Array n° 2 Odonata Rows 16 Cols 7 
  Iner  Iner+ Var   Var+  cos2  cov2 
1 1     1     0.839 0.839 0.853 0.715
2 0.856 1.856 0.873 1.712 0.681 0.594
3 0.573 2.43  0.268 1.979 0.444 0.119

Array n° 3 Trichoptera Rows 16 Cols 13 
  Iner  Iner+ Var   Var+  cos2  cov2 
1 1     1     0.941 0.941 0.76  0.715
2 0.395 1.395 0.361 1.302 0.715 0.258
3 0.238 1.634 0.176 1.478 0.726 0.128

Array n° 4 Ephemeroptera Rows 16 Cols 4 
  Iner  Iner+ Var   Var+  cos2  cov2 
1 1     1     0.942 0.942 0.915 0.861
2 0.697 1.697 0.752 1.694 0.53  0.399
3 0.079 1.777 0.035 1.728 0.134 0.005

Array n° 5 Coleoptera Rows 16 Cols 13 
  Iner  Iner+ Var   Var+  cos2  cov2 
1 1     1     0.691 0.691 0.695 0.481
2 0.683 1.683 0.659 1.351 0.663 0.437
3 0.527 2.21  0.579 1.93  0.804 0.466

Array n° 6 Diptera Rows 16 Cols 22 
  Iner  Iner+ Var   Var+  cos2  cov2 
1 1     1     0.951 0.951 0.854 0.812
2 0.478 1.478 0.393 1.343 0.584 0.23 
3 0.369 1.847 0.239 1.582 0.665 0.159

Array n° 7 Hydracarina Rows 16 Cols 4 
  Iner  Iner+ Var   Var+  cos2  cov2 
1 1     1     0.915 0.915 0.693 0.634
2 0.87  1.87  0.851 1.766 0.548 0.466
3 0.591 2.461 0.667 2.434 0.86  0.574

Array n° 8 Malacostraca Rows 16 Cols 3 
  Iner  Iner+ Var   Var+  cos2  cov2 
1 1     1     0.876 0.876 0.751 0.657
2 0.529 1.529 0.653 1.528 0.672 0.438
3 0.154 1.683 0.155 1.683 0.075 0.012

Array n° 9 Mollusca Rows 16 Cols 8 
  Iner  Iner+ Var   Var+  cos2  cov2 
1 1     1     0.974 0.974 0.773 0.753
2 0.286 1.286 0.261 1.235 0.805 0.21 
3 0.207 1.493 0.173 1.408 0.481 0.083

Array n° 10 Oligochaeta Rows 16 Cols 6 
  Iner  Iner+ Var   Var+  cos2  cov2 
1 1     1     0.755 0.755 0.482 0.364
2 0.799 1.799 0.443 1.198 0.53  0.234
3 0.383 2.183 0.244 1.442 0.539 0.132

> plot(mcoa1)
> 
> 
> 
> cleanEx(); ..nameEx <- "meau"
> 
> ### * meau
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: meau
> ### Title: Ecological Data : sites-variables, sites-species, where and when
> ### Aliases: meau
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(meau)
> par(mfrow = c(2,2))
> pca1 <- dudi.pca(meau$mil, scan = FALSE, nf = 4)
> s.class(pca1$li, meau$plan$dat, 
+     sub = "Principal Component Analysis")
> pca2 <- between(pca1, meau$plan$dat, scan = FALSE, nf = 2)
> s.class(pca2$ls, meau$plan$dat, sub = "Between dates Principal Component Analysis")
> s.corcircle(pca1$co)
> s.corcircle(pca2$as)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "meaudret"
> 
> ### * meaudret
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: meaudret
> ### Title: Ecological Data : sites-variables, sites-species, where and when
> ### Aliases: meaudret
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(meaudret)
> par(mfrow = c(2,2))
> pca1 <- dudi.pca(meaudret$mil, scan = FALSE, nf = 4)
> s.class(pca1$li, meaudret$plan$dat, 
+     sub = "Principal Component Analysis")
> pca2 <- between(pca1, meaudret$plan$dat, scan = FALSE, nf = 2)
> s.class(pca2$ls, meaudret$plan$dat, sub = "Between dates Principal Component Analysis")
> s.corcircle(pca1$co)
> s.corcircle(pca2$as)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "mfa"
> 
> ### * mfa
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mfa
> ### Title: Multiple Factorial Analysis
> ### Aliases: mfa print.mfa plot.mfa summary.mfa
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(friday87)
> w1 <- data.frame(scale(friday87$fau, scal = FALSE))
> w2 <- ktab.data.frame(w1, friday87$fau.blo, 
+     tabnames = friday87$tab.names)
> mfa1 <- mfa(w2, scann = FALSE)
> mfa1
Multiple Factorial Analysis
list of class function
$call: mfa(X = w2, scannf = FALSE)
$nf: 3 axis-components saved

  vector     length mode      content      
1 $tab.names 10     character tab names    
2 $blo       10     numeric   column number
3 $rank      1      numeric   tab rank     
4 $eig       15     numeric   eigen values 
5 $lw        16     numeric   row weights  
6 $tabw      0      NULL      array weights

   data.frame nrow ncol content                        
1  $tab       16   91   modified array                 
2  $li        16   3    row coordinates                
3  $l1        16   3    row normed scores              
4  $co        91   3    column coordinates             
5  $c1        91   3    column normed scores           
6  $lisup     160  3    row coordinates from each table
7  $TL        160  2    factors for li l1              
8  $TC        91   2    factors for co c1              
9  $T4        40   2    factors for T4comp             
10 $T4comp    40   3    component projection           
11 $link      10   3    link array-total               
other elements: NULL
> plot(mfa1)
> 
> data(escopage)
> w <- data.frame(scale(escopage$tab))
> w <- ktab.data.frame(w, escopage$blo, tabnames = escopage$tab.names)
> plot(mfa(w, scann = FALSE))
> 
> 
> 
> cleanEx(); ..nameEx <- "microsatt"
> 
> ### * microsatt
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: microsatt
> ### Title: Genetic Relationships between cattle breeds with microsatellites
> ### Aliases: microsatt
> ### Keywords: datasets
> 
> ### ** Examples
> 
> ## Not run: 
> ##D data(microsatt)
> ##D fac <- factor(rep(microsatt$loci.names, microsatt$loci.eff))
> ##D w <- dudi.coa(data.frame(t(microsatt$tab)), scann = FALSE)
> ##D wit <- within(w, fac, scann = FALSE)
> ##D microsatt.ktab <- ktab.within(wit)
> ##D 
> ##D plot(sepan(microsatt.ktab)) # 9 separated correspondence analyses
> ##D plot(mcoa(microsatt.ktab, scan = FALSE))
> ##D plot(mfa(microsatt.ktab, scan = FALSE))
> ##D plot(statis(microsatt.ktab, scan = FALSE))
> ## End(Not run)
> 
> 
> cleanEx(); ..nameEx <- "mjrochet"
> 
> ### * mjrochet
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mjrochet
> ### Title: Phylogeny and quantitative traits of teleos fishes
> ### Aliases: mjrochet
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(mjrochet)
> mjrochet.phy <- newick2phylog(mjrochet$tre)
> tab <- log((mjrochet$tab))
> tab0 <- data.frame(scalewt(tab))
> table.phylog(tab0, mjrochet.phy, csi = 2, clabel.r = 0.75)
> orthogram(tab0[,1], ortho = mjrochet.phy$Bscores)
class: krandtest 
test number:   4 
permutation number:   999 
  test  obs   P(X<=obs) P(X>=obs)
1 R2Max 0.15  0.881     0.121    
2 SkR2k 14.71 0.001     1        
3 Dmax  0.352 1         0.001    
4 SCE   2.527 0.999     0.003    

> 
> 
> 
> cleanEx(); ..nameEx <- "mld"
> 
> ### * mld
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mld
> ### Title: Multi Level Decomposition of unidimensional data
> ### Aliases: mld haar2level
> ### Keywords: ts spatial
> 
> ### ** Examples
> 
> ## Not run: 
> ##D # decomposition of a time serie
> ##D data(co2)
> ##D x <- log(co2)
> ##D orthobas <- orthobasis.line(length(x))
> ##D level<-rep("D", 467)
> ##D level[1:3]<-rep("A", 3)
> ##D level[c(77,78,79,81)]<-rep("B", 4)
> ##D level[156]<-"C"
> ##D level<-as.factor(level)
> ##D res <- mld(x, orthobas, level)
> ##D sum(scale(x, scale = FALSE) - apply(res, 1, sum))
> ## End(Not run)
> # decomposition of a biological trait on a phylogeny
> data(palm)
> vfruit<-palm$traits$vfruit
> vfruit<-scalewt(vfruit)   
> palm.phy<-newick2phylog(palm$tre)
> level <- rep("F", 65)
> level[c(4, 21, 3, 6, 13)] <- LETTERS[1:5]
> level <- as.factor(level)
> res <- mld(as.vector(vfruit), palm.phy$Bscores, level,
+  phylog = palm.phy, clabel.nod = 0.7, f.phylog=0.8,
+   csize = 2, clabel.row = 0.7, clabel.col = 0.7)
> 
> 
> 
> cleanEx(); ..nameEx <- "mollusc"
> 
> ### * mollusc
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mollusc
> ### Title: Faunistic Communities and Sampling Experiment
> ### Aliases: mollusc
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(mollusc)
> coa1 <- dudi.coa(log(mollusc$fau + 1), scannf = FALSE, nf = 3)
> par(mfrow = c(2,2))
> s.chull(coa1$li, mollusc$plan$site, 2, 3, opt = 1, cpoi = 1)
> s.chull(coa1$li, mollusc$plan$season, 2, 3, opt = 1, cpoi = 1)
> s.chull(coa1$li, mollusc$plan$method, 2, 3, opt = 1, cpoi = 1)
> s.chull(coa1$li, mollusc$plan$duration, 2, 3, opt = 1, cpoi = 1)
> par(mfrow=c(1,1))
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "monde84"
> 
> ### * monde84
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: monde84
> ### Title: Global State of the World in 1984
> ### Aliases: monde84
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(monde84)
> X <- cbind.data.frame(lpib = log(monde84$pib), monde84$croipop)
> Y <- cbind.data.frame(lmorta = log(monde84$morta), 
+     lanal = log(monde84$anal + 1), rscol = sqrt(100 - monde84$scol))
> pcaY <- dudi.pca(Y, scan = FALSE)
> pcaiv1 <- pcaiv(pcaY, X0 <- scale(X), scan = FALSE)
> sum(cor(pcaiv1$l1[,1], Y0 <- scale(Y))^2)
[1] 2.227037
> pcaiv1$eig[1] #the same
[1] 2.227037
> 
> 
> 
> cleanEx(); ..nameEx <- "morphosport"
> 
> ### * morphosport
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: morphosport
> ### Title: Athletes' Morphology
> ### Aliases: morphosport
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(morphosport)
> plot(discrimin(dudi.pca(morphosport$tab, scan = FALSE), morphosport$sport, scan = FALSE))
> 
> 
> 
> cleanEx(); ..nameEx <- "mstree"
> 
> ### * mstree
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mstree
> ### Title: Minimal Spanning Tree
> ### Aliases: mstree
> ### Keywords: utilities
> 
> ### ** 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))
> par(mfrow = c(2,2))
> 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)
+  }
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "multispati"
> 
> ### * multispati
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: multispati
> ### Title: Multivariate spatial analysis
> ### Aliases: multispati plot.multispati summary.multispati print.multispati
> ### Keywords: multivariate spatial
> 
> ### ** Examples
> 
> ## Not run: 
> ##D if (require(maptools, quiet = TRUE) & require(spdep, quiet = TRUE)) {
> ##D     data(mafragh)
> ##D     maf.xy <- mafragh$xy
> ##D     maf.flo <- mafragh$flo
> ##D     maf.listw <- nb2listw(neig2nb(mafragh$neig))
> ##D     s.label(maf.xy, neig = mafragh$neig, clab = 0.75)
> ##D     maf.coa <- dudi.coa(maf.flo,scannf = FALSE)
> ##D     multispati.randtest(maf.coa, maf.listw)
> ##D     maf.coa.ms <- multispati(maf.coa, maf.listw, scannf = FALSE, nfposi = 2, nfnega = 2)
> ##D     summary(maf.coa.ms)
> ##D     par(mfrow = c(1,3))
> ##D     barplot(maf.coa$eig)
> ##D     barplot(maf.coa.ms$eig) 
> ##D     s.corcircle(maf.coa.ms$as)
> ##D  
> ##D     par(mfrow = c(2,2))
> ##D     s.value(maf.xy, -maf.coa$li[,1])
> ##D     s.value(maf.xy, -maf.coa$li[,2])
> ##D     s.value(maf.xy, maf.coa.ms$li[,1])
> ##D     s.value(maf.xy, maf.coa.ms$li[,2])
> ##D     par(mfrow = c(1,1))
> ##D 
> ##D     par(mfrow = c(1,2))
> ##D     w1 <- -maf.coa$li[,1:2]
> ##D     w1m <- apply(w1, 2, lag.listw, x = maf.listw)
> ##D     s.match(w1, w1m, clab = 0.75)
> ##D     w1.ms <- maf.coa.ms$li[,1:2]
> ##D     w1.msm <- apply(w1.ms, 2, lag.listw, x = maf.listw)
> ##D     s.match(w1.ms, w1.msm, clab = 0.75)
> ##D     par(mfrow = c(1,1))
> ##D 
> ##D     maf.pca <- dudi.pca(mafragh$mil, scannf = FALSE)
> ##D     multispati.randtest(maf.pca, maf.listw)
> ##D     maf.pca.ms <- multispati(maf.pca, maf.listw, scannf=FALSE)
> ##D     plot(maf.pca.ms)
> ##D }
> ## End(Not run)
> 
> 
> cleanEx(); ..nameEx <- "multispati.randtest"
> 
> ### * multispati.randtest
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: multispati.randtest
> ### Title: Multivariate spatial autocorrelation test (in C)
> ### Aliases: multispati.randtest
> ### Keywords: multivariate spatial nonparametric
> 
> ### ** Examples
> 
> if (require(maptools, quiet = TRUE) & require(spdep, quiet = TRUE)) {
+     data(mafragh)
+     maf.xy <- mafragh$xy
+     maf.flo <- mafragh$flo
+     maf.listw <- nb2listw(neig2nb(mafragh$neig))
+     s.label(maf.xy, neig = mafragh$neig, clab = 0.75)
+     maf.coa <- dudi.coa(maf.flo,scannf = FALSE)
+     multispati.randtest(maf.coa, maf.listw)
+ }
Loading required package: foreign
Loading required package: tripack
Loading required package: SparseM
[1] "SparseM library loaded"
Monte-Carlo test
Observation: 0.2075949 
Call: multispati.randtest(dudi = maf.coa, listw = maf.listw)
Based on 999 replicates
Simulated p-value: 0.001 
> 
> 
> 
> cleanEx(); ..nameEx <- "multispati.rtest"
> 
> ### * multispati.rtest
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: multispati.rtest
> ### Title: Multivariate spatial autocorrelation test
> ### Aliases: multispati.rtest
> ### Keywords: multivariate spatial nonparametric
> 
> ### ** Examples
> 
> if(require(spdep, quiet = TRUE)){
+     data(mafragh)
+     maf.xy <- mafragh$xy
+     maf.flo <- mafragh$flo
+     maf.listw <- nb2listw(neig2nb(mafragh$neig))
+     s.label(maf.xy, neig = mafragh$neig, clab = 0.75)
+     maf.coa <- dudi.coa(maf.flo, scannf = FALSE)
+     multispati.rtest(maf.coa, maf.listw)
+ }
Loading required package: tripack
Loading required package: maptools
Loading required package: foreign
Loading required package: SparseM
Monte-Carlo test
Observation: 0.2075949 
Call: multispati.rtest(dudi = maf.coa, listw = maf.listw)
Based on 99 replicates
Simulated p-value: 0.01 
> 
> 
> 
> cleanEx(); ..nameEx <- "neig"
> 
> ### * neig
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: neig
> ### Title: Neighbourhood Graphs
> ### Aliases: neig neig.util.GtoL neig.util.LtoG print.neig summary.neig
> ###   scores.neig nb2neig neig2nb neig2mat
> ### Keywords: utilities
> 
> ### ** Examples
> 
> data(mafragh)
> if (require(tripack, quietly=TRUE)) {
+     par(mfrow = c(2,1))
+     provi <- neighbours(tri.mesh(mafragh$xy))
+     provi.neig <- neig(list = provi)
+     
+     s.label(mafragh$xy, neig = provi.neig, inc = FALSE, 
+         addax = FALSE, clab = 0, cnei = 2)
+     dist <- apply(provi.neig, 1, function(x) 
+         sqrt(sum((mafragh$xy[x[1],] - mafragh$xy[x[2],])^2)))
+     #hist(dist, nclass = 50)
+     mafragh.neig <- neig(edges = provi.neig[dist<50,])
+     s.label(mafragh$xy, neig = mafragh.neig, inc = FALSE, 
+         addax = FALSE, clab = 0, cnei = 2)
+     par(mfrow = c(1,1))
+     
+     data(irishdata)
+     irish.neig <- neig(area = irishdata$area)
+       summary(irish.neig)
+       print(irish.neig)
+     s.label(irishdata$xy, neig = irish.neig, cneig = 3,
+         area = irishdata$area, clab = 0.8, inc = FALSE)
+     
+     irish.scores <- scores.neig(irish.neig)
+     par(mfrow = c(2,3))
+     for (i in 1:6) s.value(irishdata$xy, irish.scores[,i],
+         inc = FALSE, grid = FALSE, addax = FALSE,
+         neig = irish.neig,
+         csi = 2, cleg = 0, sub = paste("Eigenvector n°",i), csub = 2)
+     par(mfrow = c(1,1))
+     
+     a.neig <- neig(n.circle = 16)
+     a.scores <- scores.neig(a.neig)
+     xy <- cbind.data.frame(cos((1:16) * pi / 8), sin((1:16) * pi / 8))
+     par(mfrow = c(4,4))
+     for (i in 1:15) s.value(xy, a.scores[,i], neig = a.neig, 
+         csi = 3, cleg = 0)
+     par(mfrow = c(1,1))
+     
+     a.neig <- neig(n.line = 28)
+     a.scores <- scores.neig(a.neig)
+     par(mfrow = c(7,4))
+     par(mar = c(1.1,2.1,0.1,0.1))
+     for (i in 1:27) barplot(a.scores[,i], col = grey(0.8))
+ }
Neigbourhood undirected graph
Vertices: 25 
Degrees: 5 5 4 4 1 5 3 6 5 5 5 4 4 2 3 7 3 7 7 3 8 4 6 4 4 
Edges (pairs of vertices): 57 
S01 .
S02 ..
S03 ...
S04 ....
S05 .....
S06 ..1...
S07 ..11...
S08 1.......
S09 1........
S10 1......11.
S11 .1..1......
S12 ..11..1.....
S13 .1........1..
S14 ..............
S15 .....1.........
S16 .1.....1.....1..
S17 .1...........1.1.
S18 .....1.1.1.....1..
S19 .....1....1.1.1..1.
S20 ..........1...1...1.
S21 ..11.1..11.1.....1...
S22 ...1....1...........1.
S23 .1.....1....1..1.11....
S24 1.......1............1..
S25 1......1.......1.......1.
> par(mfrow = c(1,1))
> 
> if (require(maptools, quiet = TRUE) & require(spdep, quiet = TRUE)) {
+     data(columbus)
+     par(mfrow = c(2,1))
+     par(mar = c(0.1,0.1,0.1,0.1))
+     plot(col.gal.nb, coords)
+     s.label(data.frame(coords), neig = neig(list = col.gal.nb),
+         inc = FALSE, clab = 0.6, cneig = 1)
+     par(mfrow = c(1,1))
+     
+     data(mafragh)
+     maf.rel <- relativeneigh(as.matrix(mafragh$xy))
+     maf.rel <- graph2nb(maf.rel)
+     s.label(mafragh$xy, neig = neig(list = maf.rel), inc = FALSE,
+         clab = 0, addax = FALSE, cne = 1, cpo = 2)
+     
+     par(mfrow = c(2,2))
+     w <- matrix(runif(100), 50, 2)
+     x.gab <- gabrielneigh(w)
+     x.gab <- graph2nb(x.gab)
+     s.label(data.frame(w), neig = neig(list = x.gab), inc = FALSE,
+         clab = 0, addax = FALSE, cne = 1, cpo = 2, sub = "relative")
+     x.rel <- relativeneigh(w)
+     x.rel <- graph2nb(x.rel)
+     s.label(data.frame(w), neig = neig(list = x.rel), inc = FALSE,
+         clab = 0, addax = FALSE, cne = 1, cpo = 2, sub = "Gabriel")
+     k1 <- knn2nb(knearneigh(w))
+     s.label(data.frame(w), neig = neig(list = k1), inc = FALSE,
+         clab = 0, addax = FALSE, cne = 1, cpo = 2, sub = "k nearest neighbours")
+     
+     all.linked <- max(unlist(nbdists(k1, w)))
+     z <- dnearneigh(w, 0, all.linked)
+     s.label(data.frame(w), neig = neig(list = z), inc = FALSE,
+         clab = 0, addax = FALSE, cne = 1, cpo = 2, 
+         sub = "Neighbourhood contiguity by distance")
+ }
Loading required package: foreign
Loading required package: SparseM
> par(mfrow = c(1,1))
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "newick.eg"
> 
> ### * newick.eg
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: newick.eg
> ### Title: Phylogenetic trees in Newick format
> ### Aliases: newick.eg
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(newick.eg)
> newick2phylog(newick.eg[[11]])
Phylogenetic tree with 18 leaves and 17 nodes
$class: phylog
$call: newick2phylog(x.tre = newick.eg[[11]])
$tre: ((Sa,Sh)I1,((((Tl,(Mc,My)...Ls,Lv)I13)I14)I15)I16)Root; 

        class   length content                                     
$leaves numeric 18     length of the first preceeding adjacent edge
$nodes  numeric 17     length of the first preceeding adjacent edge
$parts  list    17     subsets of descendant nodes                 
$paths  list    35     path from root to node or leave             
$droot  numeric 35     distance to root                            

         class      dim   content                                   
$Wmat    matrix     18-18 W matrix : root to the closest ancestor   
$Wdist   dist       153   Nodal distances                           
$Wvalues numeric    17    Eigen values of QWQ/sum(Q)                
$Wscores data.frame 18-17 Eigen vectors of QWQ '1/n' normed         
$Amat    matrix     18-18 Topological proximity matrix A            
$Avalues numeric    17    Eigen values of QAQ matrix                
$Adim    integer    1     number of positive eigen values of QAQ    
$Ascores data.frame 18-17 Eigen vectors of QAQ '1/n' normed         
$Aparam  data.frame 17-3  Topological indices for nodes             
$Bindica data.frame 18-17 class indicator from nodes                
$Bscores data.frame 18-17 Topological orthonormal basis '1/n' normed
$Blabels character  17    Nodes labelling from orthonormal basis    
> radial.phylog(newick2phylog(newick.eg[[7]]), circ = 1,
+  clabel.l = 0.75)
> 
> 
> 
> cleanEx(); ..nameEx <- "newick2phylog"
> 
> ### * newick2phylog
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: newick2phylog
> ### Title: Create phylogeny
> ### Aliases: newick2phylog hclust2phylog taxo2phylog newick2phylog.addtools
> ### Keywords: manip
> 
> ### ** Examples
> 
> 
>  w <- "((((,,),,(,)),),(,));"
>  w.phy <- newick2phylog(w)
>  print(w.phy)
Phylogenetic tree with 9 leaves and 6 nodes
$class: phylog
$call: newick2phylog(x.tre = w)
$tre: ((((Ext1,Ext2,Ext3)I1,Ext...Ext7)I4,(Ext8,Ext9)I5)Root; 

        class   length content                                     
$leaves numeric 9      length of the first preceeding adjacent edge
$nodes  numeric 6      length of the first preceeding adjacent edge
$parts  list    6      subsets of descendant nodes                 
$paths  list    15     path from root to node or leave             
$droot  numeric 15     distance to root                            

         class      dim content                                   
$Wmat    matrix     9-9 W matrix : root to the closest ancestor   
$Wdist   dist       36  Nodal distances                           
$Wvalues numeric    8   Eigen values of QWQ/sum(Q)                
$Wscores data.frame 9-8 Eigen vectors of QWQ '1/n' normed         
$Amat    matrix     9-9 Topological proximity matrix A            
$Avalues numeric    8   Eigen values of QAQ matrix                
$Adim    integer    1   number of positive eigen values of QAQ    
$Ascores data.frame 9-8 Eigen vectors of QAQ '1/n' normed         
$Aparam  data.frame 6-3 Topological indices for nodes             
$Bindica data.frame 9-8 class indicator from nodes                
$Bscores data.frame 9-8 Topological orthonormal basis '1/n' normed
$Blabels character  6   Nodes labelling from orthonormal basis    
>  plot(w.phy)
> 
> ## Not run: 
> ##D # newick2phylog
> ##D data(newick.eg)
> ##D radial.phylog(newick2phylog(newick.eg[[8]], FALSE), cnode = 1,
> ##D  clabel.l = 0.8)
> ##D 
> ##D w <- NULL
> ##D w[1] <- "(,((((((((((((((((,,(,(,))),),(((,(,)),(,)),),(,(,)),(,)),((((("
> ##D w[2] <- ",(,)),),),(,)),((((,((,),((,(,)),))),(,)),(,(,),,((,),(,)),)),("
> ##D w[3] <- "(((((,),),(,(,))),),(,)),(((,),),)))),((,,((,),)),(,)),((,),(,)"
> ##D w[4] <- ")),(((((((((,,),),,),),((,),)),(,),((,),)),),(((((,),),),((,),)"
> ##D w[5] <- "),(((,(,(,(,)))),(,)),(((,),(((((((,),),),,),(,)),(,)),)),((,)"
> ##D w[6] <- ",))))),(,((,),(,)),((,(,)),)))),((((,(,(,))),((,(,)),,((,(,)),)"
> ##D w[7] <- ",)),(((,),),(((,),),))),((,),))),((((((((((,,,,(,)),),((,),)),("
> ##D w[8] <- ",(,))),(((((((((,(,)),(,)),((((,((,),(,(,(,))))),((,),(,(,)))),"
> ##D w[9] <- "),((,),))),(((((((((,(,)),((,),(,))),),),),(((,((,),)),),((,((,"
> ##D w[10] <- "),)),)),(,)),(,(,(,)))),((((,(,)),(,)),(((,),(,)),(,),,(,))),(,"
> ##D w[11] <- "))),(,,,))),((((,),),),(((,(,(,))),((,),)),(,)))),(,)),),(,((,("
> ##D w[12] <- ",)),),(((,),),))),),(((,),),(,),(,(,))),(((,),(,)),((,),(,)))),"
> ##D w[13] <- "(((,),((,),)),(((((,,,,,),(,)),(,)),(,((,),))),))),(,(((((,(((("
> ##D w[14] <- ",(,)),),),)),),((,((,),((,((,),(,))),))),)),((((,),(((,),(,(,))"
> ##D w[15] <- "),)),),)),((,),)))),(((,((,,((,),)),)),),((,),))),((,),(,))),(("
> ##D w[16] <- ",),)),(((((,),((,(,)),(((,(,)),(,(((,),),))),))),(,),,),),),,(,"
> ##D w[17] <- ")),((((,),,),),((,,,),((,),((,),))))),((((((,(,)),,(,)),,(,),(,"
> ##D w[18] <- "),),(((((,(,(,),)),(((,),,),(,))),),),),,,((,),)),),)),(((((,),"
> ##D w[19] <- "(,(,)),),((,((,),),,),)),(((((((,),((((,,,),(,(,))),(((,(,)),),"
> ##D w[20] <- "(,))),)),),),),(,)),),),((,),))),((,),)),(((((((((((,),),(((((("
> ##D w[21] <- ",),),((,),)),(,)),),)),(,)),),((((((,),),(((,),),)),(,)),),(,))"
> ##D w[22] <- ",),),),),(,)),),((,),(,),,,)),(,(,(,)))),),(,)),),);"
> ##D phy1 <- newick2phylog(w,FALSE)
> ##D phy1
> ##D radial.phylog(phy1, clabel.l = 0, circle = 2.2, clea = 0.5,
> ##D  cnod = 0.5)
> ##D data(newick.eg)
> ##D radial.phylog(newick2phylog(newick.eg[[8]], FALSE), cnode = 1,
> ##D  clabel.l = 0.8)
> ##D 
> ##D # hclust2phylog
> ##D data(USArrests)
> ##D hc <- hclust(dist(USArrests), "ave")
> ##D par(mfrow = c(1,2))
> ##D plot(hc, hang = -1)
> ##D phy <- hclust2phylog(hc)
> ##D plot.phylog(phy, clabel.l = 0.75, clabel.n = 0.6, f = 0.75)
> ##D 
> ##D par(mfrow = c(1,1))
> ##D row.names(USArrests) 
> ##D names(phy$leaves) #WARNING not the same for two reasons
> ##D row.names(USArrests) <- gsub(" ","_",row.names(USArrests))
> ##D row.names(USArrests) 
> ##D names(phy$leaves) #WARNING not the same for one reason
> ##D USArrests <- USArrests[names(phy$leaves),]
> ##D row.names(USArrests) 
> ##D names(phy$leaves) #the same
> ##D table.phylog(data.frame(scalewt(USArrests)), phy, csi = 2.5,
> ##D  clabel.r = 0.75, f = 0.7)
> ##D 
> ##D #taxo2phylog
> ##D data(taxo.eg)
> ##D tax <- as.taxo(taxo.eg[[1]])
> ##D tax.phy <- taxo2phylog(as.taxo(taxo.eg[[1]]))
> ##D par(mfrow = c(1,2))
> ##D plot.phylog(tax.phy, clabel.l = 1.25, clabel.n = 1.25, f = 0.75)
> ##D plot.phylog(taxo2phylog(as.taxo(taxo.eg[[1]][sample(15),])),
> ##D  clabel.l = 1.25, clabel.n = 1.25, f = 0.75)
> ##D 
> ##D par(mfrow=c(1,1))
> ##D plot.phylog(taxo2phylog(as.taxo(taxo.eg[[2]])), clabel.l = 1,
> ##D  clabel.n = 0.75, f = 0.65)
> ## End(Not run)
> 
> 
> cleanEx(); ..nameEx <- "niche"
> 
> ### * niche
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: niche
> ### Title: Method to Analyse a pair of tables : Environmental and Faunistic
> ###   Data
> ### Aliases: niche plot.niche print.niche
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(doubs)
> dudi1 <- dudi.pca(doubs$mil, scale = TRUE, scan = FALSE, nf = 3)
> nic1 <- niche(dudi1, doubs$poi, scann = FALSE)
> 
> par(mfrow = c(2,2))
> s.traject(dudi1$li, clab = 0)
> s.traject(nic1$ls, clab = 0)
> s.corcircle(nic1$as)
> s.arrow(nic1$c1)
> 
> par(mfrow = c(5,6))
> for (i in 1:27) s.distri(nic1$ls, as.data.frame(doubs$poi[,i]),
+     csub = 2, sub = names(doubs$poi)[i])
> 
> par(mfrow = c(1,1))
> s.arrow(nic1$li, clab = 0.7)
> 
> par(mfrow = c(1,1))
> data(trichometeo)
> pca1 <- dudi.pca(trichometeo$meteo, scan = FALSE)
> nic1 <- niche(pca1, log(trichometeo$fau + 1), scan = FALSE)
> plot(nic1)
> 
> data(rpjdl)
> plot(niche(dudi.pca(rpjdl$mil, scan = FALSE), rpjdl$fau, scan = FALSE))
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "njplot"
> 
> ### * njplot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: njplot
> ### Title: Phylogeny and trait of bacteria
> ### Aliases: njplot
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(njplot)
> njplot.phy <- newick2phylog(njplot$tre)
> par(mfrow = c(2,1))
> tauxcg0 <- njplot$tauxcg - mean(njplot$tauxcg)
> symbols.phylog(njplot.phy, squares = tauxcg0)
> symbols.phylog(njplot.phy, circles = tauxcg0)
> par(mfrow = c(1,1))
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "olympic"
> 
> ### * olympic
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: olympic
> ### Title: Olympic Decathlon
> ### Aliases: olympic
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(olympic)
> pca1 <- dudi.pca(olympic$tab, scan = FALSE)
> par(mfrow = c(2,2))
> barplot(pca1$eig)
> s.corcircle(pca1$co)
> plot(olympic$score, pca1$l1[,1])
> abline(lm(pca1$l1[,1]~olympic$score))
> s.label(pca1$l1, clab = 0.5)
> s.arrow(2 * pca1$co, add.p = TRUE)
> par(mfrow = c(1,1))
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "oribatid"
> 
> ### * oribatid
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: oribatid
> ### Title: Oribatid mite
> ### Aliases: oribatid
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(oribatid)
> ori.xy <- oribatid$xy[,c(2,1)]
> names(ori.xy) <- c("x","y")
> plot(ori.xy,pch = 20, cex = 2, asp = 1)
> 
> if (require(tripack, quiet = TRUE)) {
+   if (require(spdep, quiet = TRUE)) {
+     plot(voronoi.mosaic(ori.xy), add = TRUE)
+     s.label(ori.xy, add.p = TRUE,
+      neig = nb2neig(knn2nb(knearneigh(as.matrix(ori.xy), 3))),
+      clab = 0)
+     }
+   }
Loading required package: maptools
Loading required package: foreign
Loading required package: SparseM
> 
> 
> 
> cleanEx(); ..nameEx <- "orthobasis"
> 
> ### * orthobasis
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: orthobasis
> ### Title: Orthonormal basis for orthonormal transform
> ### Aliases: orthobasis orthobasis.neig orthobasis.line orthobasis.circ
> ###   orthobasis.mat orthobasis.listw orthobasis.haar print.orthobasis
> ### Keywords: spatial ts
> 
> ### ** Examples
> 
> 
> # a 2D spatial orthobasis
> par(mfrow = c(4,4))
> w <- gridrowcol(8,8)
>  for (k in 1:16)
+     s.value(w$xy, w$orthobasis[,k], cleg = 0, csi = 2, incl = FALSE,
+      addax = FALSE, sub = k, csub = 4, ylim = c(0,10), cgri = 0)
> par(mfrow = c(1,1))
> barplot(attr(w$orthobasis, "values"))
> 
> # Haar 1D orthobasis
> w <- orthobasis.haar(32)
> par(mfrow = c(8,4))
> par(mar = c(0.1,0.1,0.1,0.1))
>  for (k in 1:31) {
+     plot(w[,k], type="S",xlab = "", ylab = "", xaxt = "n",
+      yaxt = "n", xaxs = "i", yaxs = "i",ylim=c(-4.5,4.5))
+     points(w[,k], type = "p", pch = 20, cex = 1.5)
+ }
> 
> # a 1D orthobasis
> w <- orthobasis.line(n = 33)
> par(mfrow = c(8,4))
> par(mar = c(0.1,0.1,0.1,0.1))
>  for (k in 1:32) {
+     plot(w[,k], type="l",xlab = "", ylab = "", xaxt = "n",
+      yaxt = "n", xaxs = "i", yaxs = "i",ylim=c(-1.5,1.5))
+     points(w[,k], type = "p", pch = 20, cex = 1.5)
+ }
> 
> par(mfrow = c(1,1))
> barplot(attr(w, "values"))
> 
> w <- orthobasis.circ(n = 26)
> #par(mfrow = c(5,5))
> #par(mar = c(0.1,0.1,0.1,0.1))
> # for (k in 1:25) 
> #    dotcircle(w[,k], xlim = c(-1.5,1.5), cleg = 0)
> 
> par(mfrow = c(1,1))
> #barplot(attr(w, "values"))
> 
> ## Not run: 
> ##D # a spatial orthobasis
> ##D data(mafragh)
> ##D w <- orthobasis.neig(mafragh$neig)
> ##D par(mfrow = c(4,2))
> ##D for (k in 1:8)
> ##D     s.value(mafragh$xy, w[,k],cleg = 0, sub = as.character(k),
> ##D      csub = 3)
> ##D 
> ##D par(mfrow = c(1,1))
> ##D barplot(attr(w, "values"))
> ##D 
> ##D # a phylogenetic orthobasis
> ##D data(njplot)
> ##D phy <- newick2phylog(njplot$tre)
> ##D wA <- phy$Ascores
> ##D wW <- phy$Wscores
> ##D table.phylog(phylog = phy, wA, clabel.row = 0, clabel.col  = 0.5)
> ##D table.phylog(phylog = phy, wW, clabel.row = 0, clabel.col  = 0.5)
> ##D 
> ## End(Not run)
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "orthogram"
> 
> ### * orthogram
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: orthogram
> ### Title: Orthonormal decomposition of variance
> ### Aliases: orthogram
> ### Keywords: spatial ts
> 
> ### ** Examples
> 
> # a phylogenetic example
> data(ungulates)
> ung.phy <- newick2phylog(ungulates$tre)
> FemBodyMass <- log(ungulates$tab[,1])
> NeonatBodyMass <- log((ungulates$tab[,2]+ungulates$tab[,3])/2)
> plot(FemBodyMass,NeonatBodyMass, pch = 20, cex = 2)
> abline(lm(NeonatBodyMass~FemBodyMass))
> z <- residuals(lm(NeonatBodyMass~FemBodyMass))
> dotchart.phylog(ung.phy,val = z, clabel.n = 1,
+      labels.n = ung.phy$Blabels, cle = 1.5, cdot = 2)
> table.phylog(ung.phy$Bscores, ung.phy,clabel.n = 1,
+      labels.n = ung.phy$Blabels)
> orthogram(z, ung.phy$Bscores)
class: krandtest 
test number:   4 
permutation number:   999 
  test  obs   P(X<=obs) P(X>=obs)
1 R2Max 0.357 0.734     0.268    
2 SkR2k 5.32  0.012     0.99     
3 Dmax  0.427 0.985     0.017    
4 SCE   1.047 0.972     0.03     

> orthogram(z, phyl=ung.phy) # the same thing
class: krandtest 
test number:   4 
permutation number:   999 
  test  obs   P(X<=obs) P(X>=obs)
1 R2Max 0.357 0.734     0.268    
2 SkR2k 5.32  0.012     0.99     
3 Dmax  0.427 0.985     0.017    
4 SCE   1.047 0.972     0.03     

> 
> # a spatial example
> data(irishdata)
> neig1 <- neig(mat01 = 1*(irishdata$link > 0))
> sco1 <- scores.neig(neig1)
> z <- scalewt(irishdata$tab$cow)
> orthogram(z, sco1)
class: krandtest 
test number:   4 
permutation number:   999 
  test  obs   P(X<=obs) P(X>=obs)
1 R2Max 0.322 0.911     0.091    
2 SkR2k 4.055 0.001     1        
3 Dmax  0.629 1         0.001    
4 SCE   3.834 1         0.001    

> 
> # a temporal example
> data(arrival)
> w <- orthobasis.circ(24)
> orthogram(arrival$hours, w)
class: krandtest 
test number:   4 
permutation number:   999 
  test  obs   P(X<=obs) P(X>=obs)
1 R2Max 0.738 1         0.001    
2 SkR2k 4.426 0.001     1        
3 Dmax  0.651 1         0.001    
4 SCE   3.53  1         0.001    

> par(mfrow = c(1,2))
> dotcircle(arrival$hours)
> dotcircle(w[,2])
> par(mfrow = c(1,1))
> 
> data(lynx)
> ortho <- orthobasis.line(114)
> orthogram(lynx,ortho)
class: krandtest 
test number:   4 
permutation number:   999 
  test  obs    P(X<=obs) P(X>=obs)
1 R2Max 0.328  1         0.001    
2 SkR2k 25.524 0.001     1        
3 Dmax  0.58   1         0.001    
4 SCE   13.274 1         0.001    

> attributes(lynx)$tsp
[1] 1821 1934    1
> par(mfrow = c(2,1))
> par(mar = c(4,4,2,2))
> plot.ts(lynx)
> plot(ts(ortho[,23], start = 1821, end = 1934, freq = 1), ylab = "score 23")
> par(mfrow = c(1,1))
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "ours"
> 
> ### * ours
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ours
> ### Title: A table of Qualitative Variables
> ### Aliases: ours
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(ours)
> boxplot(dudi.acm(ours, scan = FALSE))
> 
> 
> 
> cleanEx(); ..nameEx <- "palm"
> 
> ### * palm
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: palm
> ### Title: Phylogenetic and quantitative traits of amazonian palm trees
> ### Aliases: palm
> ### Keywords: datasets
> 
> ### ** Examples
> 
> ## Not run: 
> ##D data(palm)
> ##D palm.phy <- newick2phylog(palm$tre)
> ##D radial.phylog(palm.phy,clabel.l=1.25)
> ##D 
> ##D orthogram(palm$traits[,4],palm.phy$Bscores)
> ##D dotchart.phylog(palm.phy,palm$traits[,4], clabel.l = 1,
> ##D  labels.n = palm.phy$Blabels, clabel.n = 0.75)
> ##D w <- cbind.data.frame(palm.phy$Bscores[,c(3,4,6,13,21)],
> ##D  scalewt((palm$traits[,4])))
> ##D names(w)[6] <- names(palm$traits[4])
> ##D table.phylog(w, palm.phy, clabel.r = 0.75, f = 0.5)
> ##D 
> ##D gearymoran(palm.phy$Amat, palm$traits[,-c(1,3)])
> ## End(Not run)
> 
> 
> cleanEx(); ..nameEx <- "pap"
> 
> ### * pap
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: pap
> ### Title: Taxonomy and quantitative traits of carnivora
> ### Aliases: pap
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(pap)
> taxo <- taxo2phylog(as.taxo(pap$taxo))
> table.phylog(as.data.frame(scalewt(pap$tab)), taxo, csi = 2, clabel.nod = 0.6,
+  f.phylog = 0.6)
> 
> 
> 
> cleanEx(); ..nameEx <- "pcaiv"
> 
> ### * pcaiv
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: pcaiv
> ### Title: Principal component analysis with respect to instrumental
> ###   variables
> ### Aliases: pcaiv plot.pcaiv print.pcaiv
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(rhone)
> pca1 <- dudi.pca(rhone$tab, scan = FALSE, nf = 3)
> iv1 <- pcaiv(pca1, rhone$disch, scan = FALSE)
> iv1
Principal Component Analysis with Instrumental Variables
call: pcaiv(dudi = pca1, df = rhone$disch, scannf = FALSE)
class: pcaiv dudi 

$rank (rank)     : 3
$nf (axis saved) : 2

eigen values: 3.703 3.538 0.3015

 vector length mode    content                
 $eig   3      numeric eigen values           
 $lw    39     numeric row weigths (from dudi)
 $cw    15     numeric col weigths (from dudi)

 data.frame nrow ncol content                             
 $Y         39   15   Dependant variables                 
 $X         39   3    Explanatory variables               
 $tab       39   15   modified array (projected variables)

 data.frame nrow ncol content                               
 $c1        15   2    PPA Pseudo Principal Axes             
 $as        3    2    Principal axis of dudi$tab on PAP     
 $ls        39   2    projection of lines of dudi$tab on PPA
 $li        39   2    $ls predicted by X                    

 data.frame nrow ncol content                                  
 $fa        4    2    Loadings (CPC as linear combinations of X
 $l1        39   2    CPC Constraint Principal Components      
 $co        15   2    inner product CPC - Y                    
 $cor       4    2    correlation CPC - X                      

 iner inercum inerC inercumC ratio R2    lambda
 6.27 6.27    5.52  5.52     0.879 0.671 3.7   
 4.14 10.4    4.74  10.3     0.984 0.747 3.54  

> # iner inercum inerC inercumC ratio R2    lambda
> # 6.27 6.27    5.52  5.52     0.879 0.671 3.7   
> # 4.14 10.4    4.74  10.3     0.984 0.747 3.54  
> plot(iv1)
> 
> 
> 
> cleanEx(); ..nameEx <- "pcaivortho"
> 
> ### * pcaivortho
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: pcaivortho
> ### Title: Principal Component Analysis with respect to orthogonal
> ###   instrumental variables
> ### Aliases: pcaivortho
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> ## Not run: 
> ##D par(mfrow = c(2,2))
> ##D data(avimedi)
> ##D cla <- avimedi$plan$reg:avimedi$plan$str
> ##D 
> ##D # simple ordination
> ##D coa1 <- dudi.coa(avimedi$fau, scan = FALSE, nf = 3)
> ##D s.class(coa1$li, cla, sub = "Sans contrainte")
> ##D 
> ##D # within region
> ##D w1 <- within(coa1, avimedi$plan$reg, scan = FALSE)
> ##D s.match(w1$li, w1$ls, clab = 0, sub = "Intra Région")
> ##D s.class(w1$li, cla, add.plot = TRUE)
> ##D 
> ##D # no region the same result
> ##D pcaivnonA <- pcaivortho(coa1, avimedi$plan$reg, scan = FALSE)
> ##D s.match(pcaivnonA$li, pcaivnonA$ls, clab = 0, 
> ##D     sub = "Contrainte Non A")
> ##D s.class(pcaivnonA$li, cla, add.plot = TRUE)
> ##D 
> ##D # region + strate
> ##D interAplusB <- pcaiv(coa1, avimedi$plan, scan = FALSE)
> ##D s.match(interAplusB$li, interAplusB$ls, clab = 0, 
> ##D     sub = "Contrainte A + B")
> ##D s.class(interAplusB$li, cla, add.plot = TRUE)
> ##D 
> ##D par(mfrow = c(1,1))
> ## End(Not run)
> 
> 
> cleanEx(); ..nameEx <- "pcoscaled"
> 
> ### * pcoscaled
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: pcoscaled
> ### Title: Simplified Analysis in Principal Coordinates
> ### Aliases: pcoscaled
> ### Keywords: array
> 
> ### ** Examples
> 
>     a <- 1 / sqrt(3) - 0.2
>     w <- matrix(c(0,0.8,0.8,a,0.8,0,0.8,a,
+         0.8,0.8,,0,a,a,a,a,0),4,4)
>     w <- as.dist(w)
>     w <- cailliez(w)
>     w
          1         2         3
2 1.0000000                    
3 1.0000000 1.0000000          
4 0.5773503 0.5773503 0.5773503
>     pcoscaled(w)
          C1 C2
1  1.1547005  0
2 -0.5773503 -1
3 -0.5773503  1
4  0.0000000  0
>     dist(pcoscaled(w)) # w
         1        2        3
2 2.000000                  
3 2.000000 2.000000         
4 1.154701 1.154701 1.154701
>     dist(pcoscaled(2 * w)) # the same
         1        2        3
2 2.000000                  
3 2.000000 2.000000         
4 1.154701 1.154701 1.154701
>     sum(pcoscaled(w)^2) # unity
[1] 4
> 
> 
> 
> cleanEx(); ..nameEx <- "perthi02"
> 
> ### * perthi02
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: perthi02
> ### Title: Contingency Table with a partition in Molecular Biology
> ### Aliases: perthi02
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(perthi02)
> plot(discrimin.coa(perthi02$tab, perthi02$cla, scan = FALSE))
> 
> 
> 
> cleanEx(); ..nameEx <- "phylog"
> 
> ### * phylog
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: phylog
> ### Title: Phylogeny
> ### Aliases: phylog print.phylog phylog.extract phylog.permut
> ### Keywords: manip
> 
> ### ** Examples
> 
> marthans.tre <- NULL
> marthans.tre[1] <-"((((1:4,2:4)a:5,(3:7,4:7)b:2)c:2,5:11)d:2,"
> marthans.tre[2] <- "((6:5,7:5)e:4,(8:4,9:4)f:5)g:4);"
> marthans.phylog <- newick2phylog(marthans.tre)
> marthans.phylog
Phylogenetic tree with 9 leaves and 8 nodes
$class: phylog
$call: newick2phylog(x.tre = marthans.tre)
$tre: ((((X1,X2)a,(X3,X4)b)c,X5...,((X6,X7)e,(X8,X9)f)g)Root; 

        class   length content                                     
$leaves numeric 9      length of the first preceeding adjacent edge
$nodes  numeric 8      length of the first preceeding adjacent edge
$parts  list    8      subsets of descendant nodes                 
$paths  list    17     path from root to node or leave             
$droot  numeric 17     distance to root                            

         class      dim content                                   
$Wmat    matrix     9-9 W matrix : root to the closest ancestor   
$Wdist   dist       36  Nodal distances                           
$Wvalues numeric    8   Eigen values of QWQ/sum(Q)                
$Wscores data.frame 9-8 Eigen vectors of QWQ '1/n' normed         
$Amat    matrix     9-9 Topological proximity matrix A            
$Avalues numeric    8   Eigen values of QAQ matrix                
$Adim    integer    1   number of positive eigen values of QAQ    
$Ascores data.frame 9-8 Eigen vectors of QAQ '1/n' normed         
$Aparam  data.frame 8-3 Topological indices for nodes             
$Bindica data.frame 9-8 class indicator from nodes                
$Bscores data.frame 9-8 Topological orthonormal basis '1/n' normed
$Blabels character  8   Nodes labelling from orthonormal basis    
> 
> if (require(ape, quietly=TRUE)) {
+ marthans.phylo <- read.tree(text = marthans.tre)
+ marthans.phylo
+ 
+ par(mfrow =c (1,2))
+ plot.phylog(marthans.phylog, cnode = 3, f = 0.8, cle = 3)
+ plot.phylo(marthans.phylo)
+ par(mfrow = c(1,1))
+ }
Loading required package: gee
Loading required package: nlme
Loading required package: lattice
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "plot.phylog"
> 
> ### * plot.phylog
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.phylog
> ### Title: Plot phylogenies
> ### Aliases: plot.phylog radial.phylog enum.phylog
> ### Keywords: hplot
> 
> ### ** Examples
> 
> data(newick.eg)
> par(mfrow = c(3,2))
> for(i in 1:6) plot.phylog(newick2phylog(newick.eg[[i]], FALSE),
+  clea = 2, clabel.l = 3, cnod = 2.5)
> par(mfrow = c(1,1))
> 
> ## Not run: 
> ##D par(mfrow = c(1,2))
> ##D plot.phylog(newick2phylog(newick.eg[[11]], FALSE), clea = 1.5,
> ##D  clabel.l = 1.5, clabel.nod = 0.75, f = 0.8)
> ##D plot.phylog(newick2phylog(newick.eg[[10]], FALSE), clabel.l = 0,
> ##D  clea = 0, cn = 0, f = 1)
> ##D par(mfrow = c(1,1))
> ## End(Not run)
> 
> par(mfrow = c(2,2))
> w7 <- newick2phylog("(((((1,2,3)b),(6)c),(4,5)d,7)f);")
> plot.phylog(w7,clabel.l = 1.5, clabel.n = 1.5, f = 0.8, cle = 2,
+  cnod = 3, sub = "(((((1,2,3)b),(6)c),(4,5)d,7)f);", csub = 2)
> w <- NULL
> w[1] <- "((((e1:4,e2:4)a:5,(e3:7,e4:7)b:2)c:2,e5:11)d:2,"
> w[2] <- "((e6:5,e7:5)e:4,(e8:4,e9:4)f:5)g:4);"
> plot(newick2phylog(w), f = 0.8, cnod = 2, cleav = 2, clabel.l = 2)
> 
> data(taxo.eg)
> w <- taxo2phylog(as.taxo(taxo.eg[[1]]))
> plot(w, clabel.lea = 1.25, clabel.n = 1.25, sub = "Taxonomy",
+  csub = 3, f = 0.8, possub = "topleft")
> 
> provi.tre <- "(((a,b,c,d,e)A,(f,g,h)B)C)D;"
> provi.phy <- newick2phylog(provi.tre)
> plot.phylog(provi.phy, clabel.l = 2, clabel.n = 2, f = 0.8)
> par(mfrow = c(1,1))
> 
> ## Not run: 
> ##D par(mfrow = c(3,3))
> ##D for (j in 1:6) radial.phylog(newick2phylog(newick.eg[[j]],
> ##D  FALSE), clabel.l = 2, cnodes = 2)
> ##D radial.phylog(newick2phylog(newick.eg[[7]],FALSE), clabel.l = 2)
> ##D radial.phylog(newick2phylog(newick.eg[[8]],FALSE), clabel.l = 0,
> ##D  circle = 1.8)
> ##D radial.phylog(newick2phylog(newick.eg[[9]],FALSE), clabel.l = 1,
> ##D  clabel.n = 1, cle = 0, cnode = 1)
> ##D par(mfrow = c(1,1))
> ##D 
> ##D data(bsetal97)
> ##D bsetal.phy = taxo2phylog(as.taxo(bsetal97$taxo[,1:3]), FALSE)
> ##D radial.phylog(bsetal.phy, cnod = 1, clea = 1, clabel.l = 0.75,
> ##D  draw.box = TRUE, cir = 1.1)
> ##D par(mfrow = c(1,1))
> ## End(Not run)
> 
> ## Not run: 
> ##D # plot all the possible representations of a phylogenetic tree
> ##D a <- "((a,b)A,(c,d,(e,f)B)C)D;"
> ##D wa <- newick2phylog(a)
> ##D wx <- enum.phylog(wa)
> ##D dim(wx)
> ##D 
> ##D par(mfrow = c(6,8))
> ##D fun <- function(x) {
> ##D     w  <-NULL
> ##D     lapply(x, function(y) w<<-paste(w,as.character(y),sep=""))
> ##D     plot(wa, x, clabel.n = 1.25, f = 0.75, clabel.l = 2,
> ##D      box = FALSE, cle = 1.5, sub = w, csub = 2)
> ##D     invisible()}
> ##D apply(wx,1,fun)
> ##D par(mfrow = c(1,1))
> ## End(Not run)
> 
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "presid2002"
> 
> ### * presid2002
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: presid2002
> ### Title: Results of the French presidential elections of 2002
> ### Aliases: presid2002
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(presid2002)
> all((presid2002$tour2$Chirac + presid2002$tour2$Le_Pen) == presid2002$tour2$exprimes)
[1] TRUE
> ## Not run: 
> ##D data(elec88)
> ##D data(cnc2003)
> ##D w1 = area.util.class(elec88$area, cnc2003$reg)
> ##D 
> ##D par(mfrow = c(2,2))
> ##D par(mar = c(0.1,0.1,0.1,0.1))
> ##D 
> ##D area.plot(w1)
> ##D w = scale(elec88$tab$Chirac)
> ##D s.value(elec88$xy, w, add.plot = TRUE)
> ##D scatterutil.sub("Chirac 1988 T1", csub = 2, "topleft")
> ##D 
> ##D area.plot(w1)
> ##D w = scale(presid2002$tour1$Chirac/ presid2002$tour1$exprimes)
> ##D s.value(elec88$xy, w, add.plot = TRUE)
> ##D scatterutil.sub("Chirac 2002 T1", csub = 2, "topleft")
> ##D 
> ##D area.plot(w1)
> ##D w = scale(elec88$tab$Mitterand)
> ##D s.value(elec88$xy, w, add.plot = TRUE)
> ##D scatterutil.sub("Mitterand 1988 T1", csub = 2, "topleft")
> ##D 
> ##D area.plot(w1)
> ##D w = scale(presid2002$tour2$Chirac/ presid2002$tour2$exprimes)
> ##D s.value(elec88$xy, w, add.plot = TRUE)
> ##D scatterutil.sub("Chirac 2002 T2", csub = 2, "topleft")
> ## End(Not run)
> 
> 
> 
> cleanEx(); ..nameEx <- "procella"
> 
> ### * procella
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: procella
> ### Title: Phylogeny and quantitative traits of birds
> ### Aliases: procella
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(procella)
> pro.phy <- newick2phylog(procella$tre)
> plot(pro.phy,clabel.n = 1, clabel.l = 1)
> wt <- procella$traits
> wt$site.fid[is.na(wt$site.fid)] <- mean(wt$site.fid[!is.na(wt$site.fid)])
> wt$site.fid <- asin(sqrt(wt$site.fid/100))
> wt$ALE[is.na(wt$ALE)] <- mean(wt$ALE[!is.na(wt$ALE)])
> wt$ALE <- sqrt(wt$ALE)
> wt$BF[is.na(wt$BF)] <- mean(wt$BF[!is.na(wt$BF)])
> wt$mass <- log(wt$mass)
> wt <- wt[, -6]
> table.phylog(scalewt(wt), pro.phy, csi = 2)
> gearymoran(pro.phy$Amat,wt,9999)
class: krandtest 
test number:   5 
permutation number:   9999 
  test     obs    P(X<=obs) P(X>=obs)
1 site.fid 0.0999 0.7687    0.2315   
2 mate.fid 0.1776 0.8864    0.1138   
3 mass     0.5447 0.9994    8e-04    
4 ALE      9e-04  0.5342    0.466    
5 BF       0.1972 0.9076    0.0926   

> 
> 
> 
> cleanEx(); ..nameEx <- "procuste"
> 
> ### * procuste
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: procuste
> ### Title: Simple Procruste Rotation between two sets of points
> ### Aliases: procuste plot.procuste print.procuste
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(macaca)
> par(mfrow = c(2,2))
> pro1 <- procuste(macaca$xy1, macaca$xy2, scal = FALSE)
> s.match(pro1$tab1, pro1$rot2, clab = 0.7)
> s.match(pro1$tab2, pro1$rot1, clab = 0.7)
> pro2 <- procuste(macaca$xy1, macaca$xy2)
> s.match(pro2$tab1, pro2$rot2, clab = 0.7)
> s.match(pro2$tab2, pro2$rot1, clab = 0.7)
> par(mfrow = c(1,1))
> 
> data(doubs)
> pca1 <- dudi.pca(doubs$mil, scal = TRUE, scann = FALSE)
> pca2 <- dudi.pca(doubs$poi, scal = FALSE, scann = FALSE)
> pro3 <- procuste(pca1$tab, pca2$tab, nf = 2)
> par(mfrow = c(2,2))
> s.traject(pro3$scor1, clab = 0)
> s.label(pro3$scor1, clab = 0.8, add.p = TRUE)
> s.traject(pro3$scor2, clab = 0)
> s.label(pro3$scor2, clab = 0.8, add.p = TRUE)
> s.arrow(pro3$load1, clab = 0.75)
> s.arrow(pro3$load2, clab = 0.75)
> plot(pro3)
> par(mfrow = c(1,1))
> 
> data(fruits)
> plot(procuste(scalewt(fruits$jug), scalewt(fruits$var)))
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "procuste.randtest"
> 
> ### * procuste.randtest
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: procuste.randtest
> ### Title: Monte-Carlo Test on the sum of the singular values of a
> ###   procustean rotation (in C).
> ### Aliases: procuste.randtest
> ### Keywords: multivariate nonparametric
> 
> ### ** Examples
> 
> data(doubs)
> pca1 <- dudi.pca(doubs$mil, scal = TRUE, scann = FALSE)
> pca2 <- dudi.pca(doubs$poi, scal = FALSE, scann = FALSE)
> protest1 <- procuste.randtest(pca1$tab, pca2$tab, 999)
> protest1
Monte-Carlo test
Observation: 0.6562 
Call: procuste.randtest(df1 = pca1$tab, df2 = pca2$tab, nrepet = 999)
Based on 999 replicates
Simulated p-value: 0.001 
> plot(protest1,main="PROTEST")
> 
> 
> 
> cleanEx(); ..nameEx <- "procuste.rtest"
> 
> ### * procuste.rtest
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: procuste.rtest
> ### Title: Monte-Carlo Test on the sum of the singular values of a
> ###   procustean rotation (in R).
> ### Aliases: procuste.rtest
> ### Keywords: multivariate nonparametric
> 
> ### ** Examples
> 
> data(doubs)
> pca1 <- dudi.pca(doubs$mil, scal = TRUE, scann = FALSE)
> pca2 <- dudi.pca(doubs$poi, scal = FALSE, scann = FALSE)
> proc1 <- procuste(pca1$tab, pca2$tab)
> protest1 <- procuste.rtest(pca1$tab, pca2$tab, 999)
> protest1
Monte-Carlo test
Observation: 0.6562 
Call: procuste.rtest(df1 = pca1$tab, df2 = pca2$tab, nrepet = 999)
Based on 999 replicates
Simulated p-value: 0.001 
> plot(protest1)
> 
> 
> 
> cleanEx(); ..nameEx <- "pta"
> 
> ### * pta
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: pta
> ### Title: Partial Triadic Analysis of a K-tables
> ### Aliases: pta print.pta plot.pta
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(meaudret)
> wit1 <- within.pca(meaudret$mil, meaudret$plan$dat, scan = FALSE, 
+     scal = "partial")
> kta1 <- ktab.within(wit1, colnames = rep(c("S1","S2","S3","S4","S5"), 4))
> kta2 <- t(kta1)
> pta1 <- pta(kta2, scann = FALSE)
> pta1
Partial Triadic Analysis
class:pta dudi 
table number: 4 
row number: 5   column number: 9 

     **** Interstructure ****

eigen values: 2.812 0.7541 0.2537 0.18
 $RV       matrix       4      4     RV coefficients
 $RV.eig   vector       4       eigenvalues
 $RV.coo   data.frame   4      4    array scores
 $tab.names    vector       4        array names

      **** Compromise ****

eigen values: 17.2 7.298 0.6099 0.2008

 $nf: 2 axis-components saved
 $rank: 4 

 vector length mode    content                               
 $tabw  4      numeric array weights                         
 $cw    9      numeric column weights                        
 $lw    5      numeric row weights                           
 $eig   4      numeric eigen values                          
 $cos2  4      numeric cosine^2 between compromise and arrays

 data.frame nrow ncol content             
 $tab       5    9    modified array      
 $li        5    2    row coordinates     
 $l1        5    2    row normed scores   
 $co        9    2    column coordinates  
 $c1        9    2    column normed scores

     **** Intrastructure ****

 data.frame nrow ncol content                          
 $Tli       20   2    row coordinates (each table)     
 $Tco       36   2    col coordinates (each table)     
 $Tcomp     16   2    principal components (each table)
 $Tax       16   2    principal axis (each table)      
 $TL        20   2    factors for Tli                  
 $TC        36   2    factors for Tco                  
 $T4        16   2    factors for Tax Tcomp            

> plot(pta1)
> 
> 
> 
> cleanEx(); ..nameEx <- "quasieuclid"
> 
> ### * quasieuclid
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: quasieuclid
> ### Title: Transformation of a distance matrice to a Euclidean one
> ### Aliases: quasieuclid
> ### Keywords: array
> 
> ### ** Examples
> 
> data(yanomama)
> geo <- as.dist(yanomama$geo)
> is.euclid(geo) # FALSE
[1] FALSE
> geo1 <- quasieuclid(geo)
> is.euclid(geo1) # TRUE
[1] TRUE
> par(mfrow = c(2,2))
> lapply(yanomama, function(x) plot(as.dist(x), quasieuclid(as.dist(x))))
$geo
NULL

$gen
NULL

$ant
NULL

> 
> par(mfrow = c(1,1))
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "randtest"
> 
> ### * randtest
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: randtest
> ### Title: Class of the Permutation Tests (in C).
> ### Aliases: randtest as.randtest plot.randtest print.randtest
> ### Keywords: methods
> 
> ### ** Examples
> 
> par(mfrow = c(2,2))
> for (x0 in c(2.4,3.4,5.4,20.4)) {
+     l0 <- as.randtest(sim = rnorm(200), obs = x0)
+     print(l0)
+     plot(l0,main=paste("p.value = ", round(l0$pvalue, dig = 5)))
+ }
Monte-Carlo test
Observation: 2.4 
Call: as.randtest(sim = rnorm(200), obs = x0)
Based on 200 replicates
Simulated p-value: 0.009950249 
Monte-Carlo test
Observation: 3.4 
Call: as.randtest(sim = rnorm(200), obs = x0)
Based on 200 replicates
Simulated p-value: 0.004975124 
Monte-Carlo test
Observation: 5.4 
Call: as.randtest(sim = rnorm(200), obs = x0)
Based on 200 replicates
Simulated p-value: 0.004975124 
Monte-Carlo test
Observation: 20.4 
Call: as.randtest(sim = rnorm(200), obs = x0)
Based on 200 replicates
Simulated p-value: 0.004975124 
> par(mfrow = c(1,1))
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "randtest.amova"
> 
> ### * randtest.amova
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: randtest.amova
> ### Title: Permutation tests on an analysis of molecular variance (in C).
> ### Aliases: randtest.amova
> ### Keywords: multivariate nonparametric
> 
> ### ** Examples
> 
> data(humDNAm)
> amovahum <- amova(humDNAm$samples, sqrt(humDNAm$distances), humDNAm$structures)
> amovahum
$call
amova(samples = humDNAm$samples, distances = sqrt(humDNAm$distances), 
    structures = humDNAm$structures)

$results
                                Df     Sum Sq    Mean Sq
Between regions                  4  78.238115 19.5595288
Between samples Within regions   5   9.284744  1.8569488
Within samples                 662 316.197379  0.4776395
Total                          671 403.720238  0.6016695

$componentsofcovariance
                                                Sigma          %
Variations  Between regions                0.13380659  21.119144
Variations  Between samples Within regions 0.02213345   3.493396
Variations  Within samples                 0.47763955  75.387459
Total variations                           0.63357958 100.000000

$statphi
                          Phi
Phi-samples-total   0.2461254
Phi-samples-regions 0.0442870
Phi-regions-total   0.2111914

> randtesthum <- randtest.amova(amovahum, 49)
> plot(randtesthum)
> 
> 
> 
> cleanEx(); ..nameEx <- "randtest.between"
> 
> ### * randtest.between
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: randtest.between
> ### Title: Monte-Carlo Test on the between-groups inertia percentage (in
> ###   C).
> ### Aliases: randtest.between
> ### Keywords: multivariate nonparametric
> 
> ### ** Examples
> 
> data(meaudret)
> pca1 <- dudi.pca(meaudret$mil, scan = FALSE, nf = 3)
> rand1 <- randtest(between(pca1, meaudret$plan$dat, scan = FALSE), 99)
> rand1
Monte-Carlo test
Observation: 0.3722686 
Call: randtest.between(xtest = between(pca1, meaudret$plan$dat, scan = FALSE), 
    nrepet = 99)
Based on 99 replicates
Simulated p-value: 0.01 
> plot(rand1, main = "Monte-Carlo test")
> 
> 
> 
> cleanEx(); ..nameEx <- "randtest.coinertia"
> 
> ### * randtest.coinertia
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: randtest.coinertia
> ### Title: Monte-Carlo test on a coinertia analysis (in C).
> ### Aliases: randtest.coinertia
> ### Keywords: multivariate nonparametric
> 
> ### ** Examples
> 
> data(doubs)
> dudi1 <- dudi.pca(doubs$mil, scale = TRUE, scan = FALSE, nf = 3)
> dudi2 <- dudi.pca(doubs$poi, scale = FALSE, scan = FALSE, nf = 2)
> coin1 <- coinertia(dudi1,dudi2, scan = FALSE, nf = 2)
> plot(randtest(coin1))
>  
> 
> 
> cleanEx(); ..nameEx <- "randtest.discrimin"
> 
> ### * randtest.discrimin
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: randtest.discrimin
> ### Title: Monte-Carlo Test on a Discriminant Analysis (in C).
> ### Aliases: randtest.discrimin
> ### Keywords: multivariate nonparametric
> 
> ### ** Examples
> 
> data(meaudret)
> pca1 <- dudi.pca(meaudret$mil, scan = FALSE, nf = 3)
> rand1 <- randtest(discrimin(pca1, meaudret$plan$dat, scan = FALSE), 99)
> rand1
Monte-Carlo test
Observation: 0.3034897 
Call: randtest.discrimin(xtest = discrimin(pca1, meaudret$plan$dat, 
    scan = FALSE), nrepet = 99)
Based on 99 replicates
Simulated p-value: 0.01 
> #Monte-Carlo test
> #Observation: 0.3035 
> #Call: as.randtest(sim = sim, obs = obs)
> #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$dat  3  2.7314  11.2993     27     30 1.636e-09 ***
Residuals         16                                             
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
> #                   Df Pillai approx F num Df den Df  Pr(>F)    
> # meaudret$plan$dat  3   2.73    11.30     27     30 1.6e-09 ***
> # Residuals         16                                          
> # ---
> # Signif. codes:  0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 
> # 2.731/9 = 0.3034
> 
> 
> 
> cleanEx(); ..nameEx <- "rankrock"
> 
> ### * rankrock
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: rankrock
> ### Title: Ordination Table
> ### Aliases: rankrock
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(rankrock)
> dudi1 <- dudi.pca(rankrock, scannf = FALSE, nf = 3)
> scatter(dudi1, clab.r = 1.5)
> 
> 
> 
> cleanEx(); ..nameEx <- "reconst"
> 
> ### * reconst
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: reconst
> ### Title: Reconstitution of Data from a Duality Diagram
> ### Aliases: reconst reconst.pca
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(rhone)
> dd1 <- dudi.pca(rhone$tab, nf = 2, scann = FALSE)
> rh1 <- reconst(dd1, 1)
> rh2 <- reconst(dd1, 2)
> par(mfrow = c(4,4))
> par(mar = c(2.6,2.6,1.1,1.1))
> for (i in 1:15) {
+     plot(rhone$date, rhone$tab[,i])
+     lines(rhone$date, rh1[,i], lty = 2)
+     lines(rhone$date, rh2[,i], lty = 1)
+     scatterutil.sub(names(rhone$tab)[i], 2, "topright")
+ }
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "rhone"
> 
> ### * rhone
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: rhone
> ### Title: Physico-Chemistry Data
> ### Aliases: rhone
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(rhone)
> pca1 <- dudi.pca(rhone$tab, nf = 2, scann = FALSE)
> rh1 <- reconst(pca1, 1)
> rh2 <- reconst(pca1, 2)
> par(mfrow = c(4,4))
> par(mar = c(2.6,2.6,1.1,1.1))
> for (i in 1:15) {
+     plot(rhone$date, rhone$tab[,i])
+     lines(rhone$date, rh1[,i], lwd = 2)
+     lines(rhone$date, rh2[,i])
+     scatterutil.sub(names(rhone$tab)[i], 2, "topright")
+ }
> par(mfrow = c(1,1))
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "rlq"
> 
> ### * rlq
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: rlq
> ### Title: RLQ analysis
> ### Aliases: rlq print.rlq plot.rlq summary.rlq as.coinertia randtest.rlq
> ### Keywords: multivariate spatial
> 
> ### ** Examples
> 
> data(aviurba)
>    coa1 <- dudi.coa(aviurba$fau, scannf = FALSE, nf = 2)
>    dudimil <- dudi.hillsmith(aviurba$mil, scannf = FALSE, nf = 2, row.w = coa1$lw)
>    duditrait <- dudi.hillsmith(aviurba$traits, scannf = FALSE, nf = 2, row.w = coa1$cw)
>    rlq1 <- rlq(dudimil, coa1, duditrait, scannf = FALSE, nf = 2)
>    plot(rlq1)
>    summary(rlq1)

Eigenvalues decomposition:
        eig     covar      sdR      sdQ      corr
1 0.4782826 0.6915798 1.558312 1.158357 0.3831293
2 0.1418508 0.3766308 1.308050 1.219367 0.2361331

Inertia & coinertia R:
    inertia      max     ratio
1  2.428337 2.996911 0.8102800
12 4.139332 5.345110 0.7744148

Inertia & coinertia Q:
    inertia      max     ratio
1  1.341791 2.603139 0.5154512
12 2.828648 4.202981 0.6730098

Correlation L:
       corr       max     ratio
1 0.3831293 0.6435487 0.5953384
2 0.2361331 0.5220054 0.4523576

RV:
 0.0394881 
>    randtest.rlq(rlq1)
Monte-Carlo test
Observation: 0.7278339 
Call: randtest.rlq(xtest = rlq1)
Based on 999 replicates
Simulated p-value: 0.011 
> 
> 
> 
> cleanEx(); ..nameEx <- "rpjdl"
> 
> ### * rpjdl
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: rpjdl
> ### Title: Avifauna and Vegetation
> ### Aliases: rpjdl
> ### Keywords: datasets
> 
> ### ** Examples
> 
> ## Not run: 
> ##D data(rpjdl)
> ##D xy <- dudi.coa(rpjdl$fau, scann = FALSE)$l1
> ##D s.distri(xy, rpjdl$fau, 2, 1, cstar = 0.3, cell = 0)
> ##D 
> ##D xy1 <- dudi.pca(rpjdl$fau, scal = FALSE, scann = FALSE)$l1
> ##D s.distri(xy1, rpjdl$fau, 2, 1, cstar = 0.3, cell = 0)
> ##D 
> ##D cca1 <- cca(rpjdl$fau, rpjdl$mil, scan = FALSE)
> ##D plot(cca1)
> ##D 
> ## End(Not run)
> 
> 
> cleanEx(); ..nameEx <- "rtest"
> 
> ### * rtest
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: rtest
> ### Title: Class of the Permutation Tests (in R).
> ### Aliases: rtest as.rtest plot.rtest print.rtest
> ### Keywords: methods
> 
> ### ** Examples
> 
> par(mfrow = c(2,2))
> for (x0 in c(2.4,3.4,5.4,20.4)) {
+     l0 <- as.rtest(sim = rnorm(200), obs = x0)
+     print(l0)
+     plot(l0,main=paste("p.value = ", round(l0$pvalue, dig = 5)))
+ }
Monte-Carlo test
Observation: 2.4 
Call: as.rtest(sim = rnorm(200), obs = x0)
Based on 200 replicates
Simulated p-value: 0.009950249 
Monte-Carlo test
Observation: 3.4 
Call: as.rtest(sim = rnorm(200), obs = x0)
Based on 200 replicates
Simulated p-value: 0.004975124 
Monte-Carlo test
Observation: 5.4 
Call: as.rtest(sim = rnorm(200), obs = x0)
Based on 200 replicates
Simulated p-value: 0.004975124 
Monte-Carlo test
Observation: 20.4 
Call: as.rtest(sim = rnorm(200), obs = x0)
Based on 200 replicates
Simulated p-value: 0.004975124 
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "rtest.between"
> 
> ### * rtest.between
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: rtest.between
> ### Title: Monte-Carlo Test on the between-groups inertia percentage (in
> ###   R).
> ### Aliases: rtest.between
> ### Keywords: multivariate nonparametric
> 
> ### ** Examples
> 
> data(meaudret)
> pca1 <- dudi.pca(meaudret$mil, scan = FALSE, nf = 3)
> rand1 <- rtest(between(pca1, meaudret$plan$dat, scan = FALSE), 99)
> rand1
Monte-Carlo test
Observation: 0.3722686 
Call: as.rtest(sim = sim, obs = obs)
Based on 99 replicates
Simulated p-value: 0.01 
> plot(rand1, main = "Monte-Carlo test")
> 
> 
> 
> cleanEx(); ..nameEx <- "rtest.discrimin"
> 
> ### * rtest.discrimin
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: rtest.discrimin
> ### Title: Monte-Carlo Test on a Discriminant Analysis (in R).
> ### Aliases: rtest.discrimin
> ### Keywords: multivariate nonparametric
> 
> ### ** Examples
> 
> data(meaudret)
> pca1 <- dudi.pca(meaudret$mil, scan = FALSE, nf = 3)
> rand1 <- rtest(discrimin(pca1, meaudret$plan$dat, scan = FALSE), 99)
> rand1
Monte-Carlo test
Observation: 0.3034897 
Call: as.rtest(sim = sim, obs = obs)
Based on 99 replicates
Simulated p-value: 0.01 
> #Monte-Carlo test
> #Observation: 0.3035 
> #Call: as.rtest(sim = sim, obs = obs)
> #Based on 999 replicates
> #Simulated p-value: 0.001 
> plot(rand1, main = "Monte-Carlo test")
> summary.manova(manova(as.matrix(meaudret$mil)~meaudret$plan$dat), "Pillai")
                  Df  Pillai approx F num Df den Df    Pr(>F)    
meaudret$plan$dat  3  2.7314  11.2993     27     30 1.636e-09 ***
Residuals         16                                             
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
> #                   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
> 
> 
> 
> cleanEx(); ..nameEx <- "s.arrow"
> 
> ### * s.arrow
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: s.arrow
> ### Title: Plot of the factorial maps for the projection of a vector basis
> ### Aliases: s.arrow
> ### Keywords: multivariate hplot
> 
> ### ** Examples
> 
> s.arrow(cbind.data.frame(runif(55,-2,3), runif(55,-3,2)))
> 
> 
> 
> cleanEx(); ..nameEx <- "s.chull"
> 
> ### * s.chull
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: s.chull
> ### Title: Plot of the factorial maps with polygons of contour by level of
> ###   a factor
> ### Aliases: s.chull
> ### Keywords: multivariate hplot
> 
> ### ** Examples
> 
> xy <- cbind.data.frame(x = runif(200,-1,1), y = runif(200,-1,1))
> posi <- factor(xy$x > 0) : factor(xy$y > 0)
> coul <- c("black", "red", "green", "blue")
> s.chull(xy, posi, cpoi = 1.5, col = coul)
> 
> 
> 
> cleanEx(); ..nameEx <- "s.class"
> 
> ### * s.class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: s.class
> ### Title: Plot of factorial maps with representation of point classes
> ### Aliases: s.class
> ### Keywords: multivariate hplot
> 
> ### ** Examples
> 
> xy <- cbind.data.frame(x = runif(200,-1,1), y = runif(200,-1,1))
> posi <- factor(xy$x > 0) : factor(xy$y > 0)
> coul <- c("black", "red", "green", "blue")
> par(mfrow = c(2,2))
> s.class(xy, posi, cpoi = 2)
> s.class(xy, posi, cell = 0, cstar = 0.5)
> s.class(xy, posi, cell = 2, axesell = FALSE, csta = 0, col = coul)
> s.chull(xy, posi, cpoi = 1)
> par(mfrow = c(1,1))
> 
> ## Not run: 
> ##D data(banque)
> ##D dudi1 <- dudi.acm(banque, scannf = FALSE)
> ##D coul = rainbow(length(levels(banque[,20])))
> ##D par(mfrow = c(2,2))
> ##D s.label(dudi1$li, sub = "Factorial map from ACM", csub = 1.5, 
> ##D     possub = "topleft")
> ##D s.class(dudi1$li, banque[,20], sub = names(banque)[20], 
> ##D     possub = "bottomright", cell = 0, cstar = 0.5, cgrid = 0, csub = 1.5)
> ##D s.class(dudi1$li, banque[,20], csta = 0, cell = 2, cgrid = 0, 
> ##D     clab = 1.5)
> ##D s.class(dudi1$li, banque[,20], sub = names(banque)[20], 
> ##D     possub = "topright", cgrid = 0, col = coul)
> ##D par(mfrow = c(1,1))
> ##D 
> ##D par(mfrow = n2mfrow(ncol(banque)))
> ##D for (i in 1:(ncol(banque)))
> ##D     s.class(dudi1$li, banque[,i], clab = 1.5, sub = names(banque)[i], 
> ##D     csub = 2, possub = "topleft", cgrid = 0, csta = 0, cpoi = 0)
> ##D s.label(dudi1$li, clab = 0, sub = "Common background")
> ##D par(mfrow = c(1,1))
> ## End(Not run)
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "s.corcircle"
> 
> ### * s.corcircle
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: s.corcircle
> ### Title: Plot of the factorial maps of a correlation circle
> ### Aliases: s.corcircle
> ### Keywords: multivariate hplot
> 
> ### ** Examples
> 
> data (olympic)
> dudi1 <- dudi.pca(olympic$tab, scan = FALSE) # a normed PCA
> par(mfrow = c(2,2))
> s.corcircle(dudi1$co, lab = names(olympic$tab))
> s.corcircle(dudi1$co, cgrid = 0, full = FALSE, clab = 0.8)
> s.corcircle(dudi1$co, lab = as.character(1:11), cgrid = 2, 
+     full = FALSE, sub = "Correlation circle", csub = 2.5, 
+     possub = "bottomleft", box = TRUE)
> s.arrow(dudi1$co, clab = 1)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "s.distri"
> 
> ### * s.distri
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: s.distri
> ### Title: Plot of a frequency distribution
> ### Aliases: s.distri
> ### Keywords: multivariate hplot
> 
> ### ** Examples
> 
> xy <- cbind.data.frame(x = runif(200,-1,1), y = runif(200,-1,1))
> distri <- data.frame(w1 = rpois(200, xy$x * (xy$x > 0)))
> s.value(xy, distri$w1, cpoi = 1)
> s.distri(xy, distri, add.p = TRUE)
> 
> w1 <- as.numeric((xy$x> 0) & (xy$y > 0))
> w2 <- ((xy$x > 0) & (xy$y < 0)) * (1 - xy$y) * xy$x
> w3 <- ((xy$x < 0) & (xy$y > 0)) * (1 - xy$x) * xy$y
> w4 <- ((xy$x < 0) & (xy$y < 0)) * xy$y * xy$x
> 
> distri <- data.frame(a = w1 / sum(w1), b = w2 / sum(w2), 
+     c = w3 / sum(w3), d = w4 / sum(w4))
> s.value(xy, unlist(apply(distri, 1, sum)), cleg = 0, csi = 0.75)
> s.distri(xy, distri, clab = 2, add.p = TRUE)
> 
> data(rpjdl)
> xy <- dudi.coa(rpjdl$fau, scan = FALSE)$li
> par(mfrow = c(3,4))
> for (i in c(1,5,8,20,21,23,26,33,36,44,47,49)){
+ s.distri(xy, rpjdl$fau[,i], cell = 1.5, sub = rpjdl$frlab[i], 
+     csub = 2, cgrid = 1.5)}
> par(mfrow = c(1,1))
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "s.hist"
> 
> ### * s.hist
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: s.hist
> ### Title: Display of a scatterplot and its two marginal histograms
> ### Aliases: s.hist
> ### Keywords: multivariate hplot
> 
> ### ** Examples
> 
> data(rpjdl)
> coa1 <- dudi.coa(rpjdl$fau, scannf = FALSE, nf = 4)
> s.hist(coa1$li)
[1] 10 20 30
> s.hist(coa1$li, cgrid = 2, cbr = 3, adj = 0.5, clab = 0)
[1]  5 10 15 20 25 30
> s.hist(coa1$co, cgrid = 2, cbr = 3, adj = 0.5, clab = 0)
[1]  5 10 15
> 
> 
> 
> cleanEx(); ..nameEx <- "s.image"
> 
> ### * s.image
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: s.image
> ### Title: Graph of a variable using image and contour
> ### Aliases: s.image
> ### Keywords: hplot
> 
> ### ** Examples
> 
> 
> if (require(splancs, quiet = TRUE)){
+    wxy=data.frame(expand.grid(-3:3,-3:3))
+    names(wxy)=c("x","y")
+    z=(1/sqrt(2))*exp(-(wxy$x^2+wxy$y^2)/2)
+    par(mfrow=c(2,2))
+    s.value(wxy,z)
+    s.image(wxy,z)
+    s.image(wxy,z,kgrid=5)
+    s.image(wxy,z,kgrid=15)
+ }

Spatial Point Pattern Analysis Code in S-Plus

 Version 2 - Spatial and Space-Time analysis
> 
> ## Not run: 
> ##D data(t3012)
> ##D if (require(splancs, quiet = TRUE)){
> ##D     par(mfrow = c(4,4))
> ##D     for(k in 1:12)  s.image(t3012$xy,scalewt(t3012$temp[,k]), kgrid = 3)
> ##D     par(mfrow = c(1,1))
> ##D }
> ##D     
> ##D data(elec88)
> ##D if (require(splancs, quiet = TRUE)){
> ##D    par(mfrow = c(4,4))
> ##D    for(k in 1:12)
> ##D    s.image(t3012$xy, scalewt(t3012$temp[,k]), kgrid = 3, sub = names(t3012$temp)[k],
> ##D         csub = 3, area = elec88$area)
> ##D     par(mfrow = c(1,1))
> ##D }
> ## End(Not run)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "s.kde2d"
> 
> ### * s.kde2d
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: s.kde2d
> ### Title: Scatter Plot with Kernel Density Estimate
> ### Aliases: s.kde2d
> ### Keywords: multivariate hplot
> 
> ### ** Examples
> 
> # To recognize groups of points
> data(casitas)
> casitas.fuz = fuzzygenet(casitas)
> casitas.pop <- as.factor(rep(c("dome", "cast", "musc", "casi"), c(24,11,9,30)))
> casitas.pca = dudi.pca(casitas.fuz, scannf = FALSE, scale = FALSE)
> if (require(MASS, quiet=TRUE)) {s.kde2d(casitas.pca$li)
+ s.class(casitas.pca$li,casitas.pop, cell = 0, add.p = TRUE)
+ }
> 
> 
> 
> cleanEx(); ..nameEx <- "s.label"
> 
> ### * s.label
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: s.label
> ### Title: Scatter Plot
> ### Aliases: s.label
> ### Keywords: multivariate hplot
> 
> ### ** 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$neig, addax = FALSE)
> data(irishdata)
> s.label(irishdata$xy, inc = FALSE, contour = irishdata$contour, 
+     addax = FALSE)
> 
> par(mfrow = c(2,2))
> cha <- ls()
> s.label(cbind.data.frame(runif(length(cha)), 
+     runif(length(cha))), lab = cha)
> x <- runif(50,-2,2) ; y <- runif(50,-2,2) ; z <- x^2 + y^2
> s.label(data.frame(x,y), lab = as.character(z < 1))
> s.label(data.frame(x,y), clab = 0, cpoi = 1, add.plot = TRUE)
> symbols(0, 0, circles = 1, add = TRUE, inch = FALSE)
> s.label(cbind.data.frame(runif(100,0,10), runif(100,5,12)), 
+     incl = FALSE, clab = 0)
> s.label(cbind.data.frame(runif(100,-3,12),
+     runif(100,2,10)), cl = 0, cp = 2, include = FALSE)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "s.match"
> 
> ### * s.match
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: s.match
> ### Title: Plot of Paired Coordinates
> ### Aliases: s.match
> ### Keywords: multivariate hplot
> 
> ### ** Examples
> 
> X <- data.frame(x = runif(50,-1,2), y = runif(50,-1,2))
> Y <- X + rnorm(100, sd = 0.3)
> par(mfrow = c(2,2))
> s.match(X, Y)
> s.match(X, Y, edge = FALSE, clab = 0)
> s.match(X, Y, edge = FALSE, clab = 0)
> s.label(X, clab = 1, add.plot = TRUE)
> s.label(Y, clab = 0.75, add.plot = TRUE)
> s.match(Y, X, clab = 0)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "s.traject"
> 
> ### * s.traject
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: s.traject
> ### Title: Trajectory Plot
> ### Aliases: s.traject
> ### Keywords: multivariate hplot
> 
> ### ** Examples
> 
> rw <- function(a){
+     x <- 0
+     for(i in 1:49) x <- c(x,x[length(x)] + runif(1,-1,1))
+     x
+ }
> y <- unlist(lapply(1:5, rw))
> x <- unlist(lapply(1:5, rw))
> z <- gl(5,50)
> s.traject(data.frame(x,y), z, edge = FALSE)
> 
> 
> 
> cleanEx(); ..nameEx <- "s.value"
> 
> ### * s.value
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: s.value
> ### Title: Representation of a value in a graph
> ### Aliases: s.value
> ### Keywords: multivariate hplot
> 
> ### ** Examples
> 
> xy <- cbind.data.frame(x = runif(500), y = runif(500))
> z <- rnorm(500)
> s.value(xy, z)
> 
> s.value(xy, z, method = "greylevel")
> 
> data(rpjdl)
> fau.coa <- dudi.coa(rpjdl$fau, scan = FALSE, nf = 3)
> s.value(fau.coa$li, fau.coa$li[,3], csi = 0.75, cleg = 0.75)
> 
> data(irishdata)
> par(mfrow = c(3,4))
> irq0 <- data.frame(scale(irishdata$tab, scale = TRUE))
> for (i in 1:12) {
+     z <- irq0[,i] ; nam <- names(irq0)[i]
+     s.value(irishdata$xy, irq0[,1], area = irishdata$area, csi = 3,
+     csub = 2, sub = nam, cleg = 1.5, cgrid = 0, inc = FALSE,
+     xlim = c(16,205), ylim = c(-50,268), adda = FALSE, grid = FALSE)
+ }
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "santacatalina"
> 
> ### * santacatalina
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: santacatalina
> ### Title: Indirect Ordination
> ### Aliases: santacatalina
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(santacatalina)
> par(mfrow = c(2,2))
> table.value(log(santacatalina + 1), grid = TRUE)
> table.value(log(santacatalina + 1)[,sample(10)], grid = TRUE)
> coa1 <- dudi.coa(log(santacatalina + 1), scan = FALSE) # 2 factors
> table.value(log(santacatalina + 1)[order(coa1$li[,1]), 
+     order(coa1$co[,1])], grid = TRUE) 
> scatter(coa1, posi = "bottom")
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "sarcelles"
> 
> ### * sarcelles
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: sarcelles
> ### Title: Array of Recapture of Rings
> ### Aliases: sarcelles
> ### Keywords: datasets
> 
> ### ** Examples
> 
> ## Not run: 
> ##D # depends of pixmap
> ##D if (require(pixmap, quietly=TRUE)) {
> ##D     bkgnd.pnm <- read.pnm(system.file("pictures/sarcelles.pnm", package = "ade4"))
> ##D     data(sarcelles)
> ##D     par(mfrow = c(4,3))
> ##D     for(i in 1:12) {
> ##D     s.distri(sarcelles$xy, sarcelles$tab[,i], pixmap = bkgnd.pnm, 
> ##D        sub = sarcelles$col.names[i], clab = 0, csub = 2)
> ##D     s.value(sarcelles$xy, sarcelles$tab[,i], add.plot = TRUE, cleg = 0)
> ##D     }
> ##D }
> ## End(Not run)
> 
> 
> cleanEx(); ..nameEx <- "scalewt"
> 
> ### * scalewt
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: scalewt
> ### Title: Centring and Scaling a Matrix of Any Weighting
> ### Aliases: scalewt
> ### Keywords: utilities
> 
> ### ** Examples
> 
> scalewt(matrix(1:12,4,3))
           [,1]       [,2]       [,3]
[1,] -1.3416408 -1.3416408 -1.3416408
[2,] -0.4472136 -0.4472136 -0.4472136
[3,]  0.4472136  0.4472136  0.4472136
[4,]  1.3416408  1.3416408  1.3416408
> scale((matrix(1:12,4,3)))
           [,1]       [,2]       [,3]
[1,] -1.1618950 -1.1618950 -1.1618950
[2,] -0.3872983 -0.3872983 -0.3872983
[3,]  0.3872983  0.3872983  0.3872983
[4,]  1.1618950  1.1618950  1.1618950
attr(,"scaled:center")
[1]  2.5  6.5 10.5
attr(,"scaled:scale")
[1] 1.290994 1.290994 1.290994
> scale(matrix(1,4,3))
     [,1] [,2] [,3]
[1,]  NaN  NaN  NaN
[2,]  NaN  NaN  NaN
[3,]  NaN  NaN  NaN
[4,]  NaN  NaN  NaN
attr(,"scaled:center")
[1] 1 1 1
attr(,"scaled:scale")
[1] 0 0 0
> scalewt(matrix(1,4,3))
     [,1] [,2] [,3]
[1,]    0    0    0
[2,]    0    0    0
[3,]    0    0    0
[4,]    0    0    0
> 
> 
> 
> cleanEx(); ..nameEx <- "scatter"
> 
> ### * scatter
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: scatter
> ### Title: Scatter Plot
> ### Aliases: scatter scatterutil.base add.scatter.eig scatterutil.chull
> ###   scatterutil.eigen scatterutil.ellipse scatterutil.eti.circ
> ###   scatterutil.eti scatterutil.grid scatterutil.legend.bw.square
> ###   scatterutil.legend.square.grey scatterutil.legendgris
> ###   scatterutil.scaling scatterutil.star scatterutil.sub
> ### Keywords: multivariate hplot
> 
> ### ** Examples
> 
> par(mfrow = c(3,3))
> 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 <- c(0.5,0.2,-0.5,-0.2) ; y <- c(0.2,0.5,-0.2,-0.5)
> eti <- c("toto", "kjbk", "gdgiglgl", "sdfg")
> plot(x, y, xlim = c(-1,1), ylim = c(-1,1))
> scatterutil.eti.circ(x, y, eti, 2.5)
> abline(0, 1, lty = 2) ; abline(0, -1, lty = 2)
> 
> x <- c(0.5,0.2,-0.5,-0.2) ; y <- c(0.2,0.5,-0.2,-0.5)
> eti <- c("toto", "kjbk", "gdgiglgl", "sdfg")
> plot(x, y, xlim = c(-1,1), ylim = c(-1,1))
> scatterutil.eti(x, y, eti, 1.5)
> 
> plot(runif(10,-3,5), runif(10,-1,1), asp = 1)
> scatterutil.grid(2)
> abline(h = 0, v = 0, lwd = 3)
> 
> x <- runif(10,0,1) ; y <- rnorm(10) ; z <- rep(1,10)
> plot(x,y) ; scatterutil.star(x, y, z, 0.5)
> plot(x,y) ; scatterutil.star(x, y, z, 1)
> 
> x <- c(runif(10,0,0.5), runif(10,0.5,1))
> y <- runif(20)
> plot(x, y, asp = 1) # asp=1 is essential to have perpendicular axes
> scatterutil.ellipse(x, y, rep(c(1,0), c(10,10)), cell = 1.5, ax = TRUE)
> scatterutil.ellipse(x, y, rep(c(0,1), c(10,10)), cell = 1.5, ax = TRUE)
> 
> x <- c(runif(100,0,0.75), runif(100,0.25,1))
> y <- c(runif(100,0,0.75), runif(100,0.25,1))
> z <- factor(rep(c(1,2), c(100,100)))
> plot(x, y, pch = rep(c(1,20), c(100,100)))
> scatterutil.chull(x, y, z, opt = c(0.25,0.50,0.75,1))
> par(mfrow = c(1,1))
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "scatter.acm"
> 
> ### * scatter.acm
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: scatter.acm
> ### Title: Plot of the factorial maps in multiple correspondence analysis
> ### Aliases: scatter.acm
> ### Keywords: multivariate hplot
> 
> ### ** Examples
> 
> data(lascaux)
> scatter(dudi.acm(lascaux$ornem, sca = FALSE), csub = 3)
> 
> 
> 
> cleanEx(); ..nameEx <- "scatter.coa"
> 
> ### * scatter.coa
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: scatter.coa
> ### Title: Plot of the factorial maps for a correspondence analysis
> ### Aliases: scatter.coa
> ### Keywords: multivariate hplot
> 
> ### ** Examples
> 
> data(housetasks)
> par(mfrow = c(2,2))
> w <- dudi.coa(housetasks, scan = FALSE)
> scatter.dudi(w, sub = "0 / To be avoided")
> scatter.coa(w, method = 1, sub = "1 / Standard", posieig = "none")
> scatter.coa(w, method = 2, 
+     sub = "2 / Columns -> averaging -> Rows", posieig = "none")
> scatter.coa(w, method = 3, 
+     sub = "3 / Rows -> averaging -> Columns ", posieig = "none")
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "scatter.dudi"
> 
> ### * scatter.dudi
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: scatter.dudi
> ### Title: Plot of the Factorial Maps
> ### Aliases: scatter.dudi
> ### Keywords: multivariate hplot
> 
> ### ** Examples
> 
> data(deug)
> scatter(dd1 <- dudi.pca(deug$tab, scannf = FALSE, nf = 4), 
+     posieig = "bottom")
> 
> data(rhone)
> dd1 <- dudi.pca(rhone$tab, nf = 4, scann = FALSE)
> scatter(dd1, sub = "Principal component analysis")
> 
> 
> 
> cleanEx(); ..nameEx <- "scatter.fca"
> 
> ### * scatter.fca
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: scatter.fca
> ### Title: Plot of the factorial maps for a fuzzy correspondence analysis
> ### Aliases: scatter.fca
> ### Keywords: multivariate hplot
> 
> ### ** Examples
> 
> data(coleo)
> coleo.fuzzy <- prep.fuzzy.var(coleo$tab, coleo$col.blocks)
2 missing data found in block 1 
1 missing data found in block 3 
2 missing data found in block 4 
> fca1 <- dudi.fca(coleo.fuzzy, sca = FALSE, nf = 3)
> scatter(fca1, labels = coleo$moda.names, clab.moda = 1.5,
+     sub = names(coleo$col.blocks), csub = 3)
> 
> 
> 
> cleanEx(); ..nameEx <- "sco.boxplot"
> 
> ### * sco.boxplot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: sco.boxplot
> ### Title: Representation of the link between a variable and a set of
> ###   qualitative variables
> ### Aliases: sco.boxplot
> ### Keywords: multivariate hplot
> 
> ### ** Examples
> 
> w1 <- rnorm(100,-1)
> w2 <- rnorm(100)
> w3 <- rnorm(100,1)
> f1 <- gl(3,100)
> f2 <- gl(30,10)
> sco.boxplot(c(w1,w2,w3), data.frame(f1,f2))
> 
> data(banque)
> banque.acm <- dudi.acm(banque, scan = FALSE, nf = 4)
> par(mfrow = c(1,3))
> sco.boxplot(banque.acm$l1[,1], banque[,1:7], clab = 1.8)
> sco.boxplot(banque.acm$l1[,1], banque[,8:14], clab = 1.8)
> sco.boxplot(banque.acm$l1[,1], banque[,15:21], clab = 1.8)
> par(mfrow = c(1,1))
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "sco.distri"
> 
> ### * sco.distri
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: sco.distri
> ### Title: Representation by mean- standard deviation of a set of weight
> ###   distributions on a numeric score
> ### Aliases: sco.distri
> ### Keywords: multivariate hplot
> 
> ### ** Examples
> 
> w <-seq(-1, 1, le = 200)
> distri <- data.frame(lapply(1:50, 
+     function(x) sample((200:1)) * ((w >= (-x/50)) & (w <= x/50)) ))
> names(distri) <- paste("w", 1:50, sep = "")
> par(mfrow = c(1,2))
> sco.distri(w, distri, csi = 1.5)
> sco.distri(w, distri, y.rank = FALSE, csi = 1.5)
> par(mfrow = c(1,1))
> 
> data(rpjdl)
> coa2 <- dudi.coa(rpjdl$fau, FALSE)
> sco.distri(coa2$li[,1], rpjdl$fau, lab = rpjdl$frlab, clab = 0.8)
> 
> data(doubs)
> par(mfrow = c(2,2))
> poi.coa <- dudi.coa(doubs$poi, scann = FALSE)
> sco.distri(poi.coa$l1[,1], doubs$poi)
> poi.nsc <- dudi.nsc(doubs$poi, scann = FALSE)
> sco.distri(poi.nsc$l1[,1], doubs$poi)
> s.label(poi.coa$l1)
> s.label(poi.nsc$l1)
> 
> data(rpjdl)
> fau.coa <- dudi.coa(rpjdl$fau, scann = FALSE)
> sco.distri(fau.coa$l1[,1], rpjdl$fau)
> fau.nsc <- dudi.nsc(rpjdl$fau, scann = FALSE)
> sco.distri(fau.nsc$l1[,1], rpjdl$fau)
> s.label(fau.coa$l1)
> s.label(fau.nsc$l1)
> 
> par(mfrow = c(1,1))
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "sco.quant"
> 
> ### * sco.quant
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: sco.quant
> ### Title: Graph to Analyse the Relation between a Score and Quantitative
> ###   Variables
> ### Aliases: sco.quant
> ### Keywords: hplot multivariate
> 
> ### ** Examples
> 
> w <- runif(100, -5, 10)
> fw <- cut (w, 5)
> levels(fw) <- LETTERS[1:5]
> wX <- data.frame(matrix(w + rnorm(900, sd = (1:900) / 100), 100, 9))
> sco.quant(w, wX, fac = fw, abline = TRUE, clab = 2, csub = 3)
> 
> 
> 
> cleanEx(); ..nameEx <- "score"
> 
> ### * score
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: score
> ### Title: Graphs for One Dimension
> ### Aliases: score scoreutil.base
> ### Keywords: multivariate hplot
> 
> ### ** Examples
> 
> ## Not run: 
> ##D par(mar = c(1,1,1,1))
> ##D scoreutil.base (runif(20,3,7), xlim = NULL, grid = TRUE, cgrid = 0.8, 
> ##D     include.origin = TRUE, origin = 0, sub = "Uniform", csub = 1)
> ## End(Not run)
> # returns the value of the user coordinate of the low line.  
> # The user window id defined with c(0,1) in ordinate.
> # box()
> 
> 
> 
> cleanEx(); ..nameEx <- "score.acm"
> 
> ### * score.acm
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: score.acm
> ### Title: Graphs to study one factor in a Multiple Correspondence Analysis
> ### Aliases: score.acm
> ### Keywords: multivariate hplot
> 
> ### ** Examples
> 
> data(banque)
> banque.acm <- dudi.acm(banque, scann = FALSE, nf = 3)
> score(banque.acm, which = which(banque.acm$cr[,1] > 0.2), csub = 3)
> 
> 
> 
> cleanEx(); ..nameEx <- "score.coa"
> 
> ### * score.coa
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: score.coa
> ### Title: Graphs to analyse a factor in a correspondence analysis
> ### Aliases: score.coa
> ### Keywords: multivariate hplot
> 
> ### ** Examples
> 
> layout(matrix(c(1,1,2,3), 2, 2), resp = FALSE)
> data(aviurba)
> dd1 <- dudi.coa(aviurba$fau, scan = FALSE)
> score(dd1, clab.r = 0, clab.c = 0.75)
> abline(v = 1, lty = 2, lwd = 3)
> sco.distri(dd1$l1[,1], aviurba$fau)
> sco.distri(dd1$c1[,1], data.frame(t(aviurba$fau)))
> 
> # 1 reciprocal scaling correspondence score -> species amplitude + sample diversity
> # 2 sample score -> averaging -> species amplitude
> # 3 species score -> averaging -> sample diversity
> 
> layout(matrix(c(1,1,2,3), 2, 2), resp = FALSE)
> data(rpjdl)
> rpjdl1 <- dudi.coa(rpjdl$fau, scan = FALSE)
> score(rpjdl1, clab.r = 0, clab.c = 0.75)
> if (require(MASS, quietly = TRUE)) {
+    data(caith)
+    score(dudi.coa(caith, scan = FALSE), clab.r = 1.5, clab.c = 1.5, cpoi = 3)
+    data(housetasks)
+    score(dudi.coa(housetasks, scan = FALSE), clab.r = 1.25, clab.c = 1.25, 
+         csub = 0, cpoi = 3)
+ }
> par(mfrow = c(1,1))
> score(rpjdl1, dotchart = TRUE, clab.r = 0)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "score.mix"
> 
> ### * score.mix
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: score.mix
> ### Title: Graphs to Analyse a factor in a Mixed Analysis
> ### Aliases: score.mix
> ### Keywords: multivariate hplot
> 
> ### ** Examples
> 
> data(lascaux)
> w <- cbind.data.frame(lascaux$colo, lascaux$ornem)
> dd <- dudi.mix(w, scan = FALSE, nf = 4, add = TRUE)
> score(dd, which = which(dd$cr[,1] > 0.3))
> 
> 
> 
> cleanEx(); ..nameEx <- "score.pca"
> 
> ### * score.pca
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: score.pca
> ### Title: Graphs to Analyse a factor in PCA
> ### Aliases: score.pca
> ### Keywords: multivariate hplot
> 
> ### ** Examples
> 
> data(deug)
> dd1 <- dudi.pca(deug$tab, scan = FALSE)
> score(dd1, csub = 3)
>  
> # The correlations are :
> dd1$co[,1]
[1] 0.7924753 0.6531896 0.7410261 0.5287294 0.5538660 0.7416171 0.3336153
[8] 0.2755026 0.4171874
> # [1] 0.7925 0.6532 0.7410 0.5287 0.5539 0.7416 0.3336 0.2755 0.4172
> 
> 
> 
> cleanEx(); ..nameEx <- "seconde"
> 
> ### * seconde
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: seconde
> ### Title: Students and Subjects
> ### Aliases: seconde
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(seconde)
> scatter(dudi.pca(seconde, scan = FALSE), clab.r = 1, clab.c = 1.5)
> 
> 
> 
> cleanEx(); ..nameEx <- "sepan"
> 
> ### * sepan
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: sepan
> ### Title: Separated Analyses in a K-tables
> ### Aliases: sepan plot.sepan print.sepan summary.sepan
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(escopage)
> w <- data.frame(scale(escopage$tab))
> w <- ktab.data.frame(w, escopage$blo, tabnames = escopage$tab.names)
> sep1 <- sepan(w)
> sep1
class: sepan list 
$call: sepan(X = w)
  vector     length mode      content             
1 $tab.names 4      character tab names           
2 $blo       4      numeric   column number       
3 $rank      4      numeric   tab rank            
4 $Eig       27     numeric   All the eigen values
  data.frame nrow ncol content                  
1 $Li        84   2    row coordinates          
2 $L1        84   2    row normed scores        
3 $Co        27   2    column coordinates       
4 $C1        27   2    column normed coordinates
5 $TL        84   2    factors for Li L1        
6 $TC        27   2    factors for Co C1        
> summary(sep1)
Separate Analyses of a 'ktab' object
  names    nrow ncol rank lambda1 lambda2 lambda3 lambda4    
1 repos    21   5    5    2.135   1.444   0.777   0.268   ...
2 visual   21   3    3    2.7     0.144   0.014              
3 olfactif 21   10   10   4.478   2.365   0.996   0.729   ...
4 general  21   9    9    5.373   1.706   0.642   0.334   ...
> plot(sep1)
> 
> 
> 
> cleanEx(); ..nameEx <- "skulls"
> 
> ### * skulls
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: skulls
> ### Title: Morphometric Evolution
> ### Aliases: skulls
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(skulls)
> pca1 <- dudi.pca(skulls, scan = FALSE)
> fac <- gl(5, 30)
> levels(fac) <- c("-4000", "-3300", "-1850", "-200", "+150")
> dis.skulls <- discrimin(pca1, fac, scan = FALSE)
> plot(dis.skulls, 1, 1)
> 
> 
> 
> cleanEx(); ..nameEx <- "statis"
> 
> ### * statis
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: statis
> ### Title: STATIS, a method for analysing K-tables
> ### Aliases: statis print.statis plot.statis
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(jv73)
> kta1 <- ktab.within(within.pca(jv73$morpho, jv73$fac.riv, scann = FALSE))
> statis1 <- statis(kta1, scann = FALSE)
> plot(statis1)
> 
> dudi1 <- dudi.pca(jv73$poi, scann = FALSE, scal = FALSE)
> wit1 <- within(dudi1, jv73$fac.riv, scann = FALSE)
> kta3 <- ktab.within(wit1)
> data(jv73)
> statis3 <- statis(kta3, scann = FALSE)
> plot(statis3)
> 
> s.arrow(statis3$C.li, cgrid = 0)
> 
> kplot(statis3, traj = TRUE, arrow = FALSE, unique = TRUE, 
+     clab = 0, csub = 3, cpoi = 3)
> statis3
STATIS Analysis
class:statis 
table number: 12 
row number: 19   total column number: 92 

     **** Interstructure ****

eigen values: 5.337 1.525 1.294 1.037 0.6419 ...
 $RV       matrix       12      12     RV coefficients
 $RV.eig   vector       12       eigenvalues
 $RV.coo   data.frame   12      4    array scores
 $tab.names    vector       12        array names
 $RV.tabw  vector       12      array weigths

RV coefficient
              Doubs    Drugeon Dessoubre   Allaine    Audeux  Cusancin
Doubs     1.0000000                                                   
Drugeon   0.4214976 1.00000000                                        
Dessoubre 0.5560554 0.09888345 1.0000000                              
Allaine   0.4162722 0.33016274 0.4264883 1.0000000                    
Audeux    0.5275275 0.09998470 0.7460391 0.3923156 1.0000000          
Cusancin  0.2648507 0.14357569 0.4098816 0.6750590 0.4465916 1.0000000
Loue      0.4396741 0.31081167 0.2031117 0.3872305 0.1128074 0.3069628
Lison     0.4181009 0.07442131 0.4814447 0.2312130 0.3968212 0.3982919
Furieuse  0.2782714 0.18165914 0.4089889 0.3844109 0.3291450 0.6693345
Cuisance  0.4196404 0.22095719 0.4596960 0.4881191 0.3045202 0.5962413
Doulonnes 0.2183520 0.06715160 0.4014573 0.2401718 0.3781006 0.2987118
Clauge    0.4599651 0.51530332 0.2912210 0.4142577 0.2568859 0.2351574
               Loue     Lison  Furieuse  Cuisance Doulonnes Clauge
Doubs                                                             
Drugeon                                                           
Dessoubre                                                         
Allaine                                                           
Audeux                                                            
Cusancin                                                          
Loue      1.0000000                                               
Lison     0.3597613 1.0000000                                     
Furieuse  0.3862660 0.5209446 1.0000000                           
Cuisance  0.6487130 0.6310371 0.7768327 1.0000000                 
Doulonnes 0.3005171 0.5346002 0.4748992 0.5445763 1.0000000       
Clauge    0.5192117 0.2895775 0.3259230 0.4934249 0.3310817      1

      **** Compromise ****

eigen values: 2.012 0.903 0.5025 0.3003 0.2282 ...

 $nf: 3 axis-components saved
 $rank: 19 
 data.frame nrow ncol content                       
 $C.li      19   3    row coordinates               
 $C.Co      92   3    column coordinates            
 $T4        48   2    principal vectors (each table)
 $TL        228  2    factors (not used)            
 $TC        92   2    factors for Co                
 $T4        48   2    factors for T4                

> 
> 
> 
> cleanEx(); ..nameEx <- "steppe"
> 
> ### * steppe
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: steppe
> ### Title: Transect in the Vegetation
> ### Aliases: steppe
> ### Keywords: datasets
> 
> ### ** Examples
> 
> par(mfrow = c(3,1))
> data(steppe)
> w1 <- col(as.matrix(steppe$tab[,1:15]))
> w1 <- as.numeric(w1[steppe$tab[,1:15] > 0])
> w2 <- row(as.matrix(steppe$tab[,1:15]))
> w2 <- as.numeric(w2[steppe$tab[,1:15] > 0])
> plot(w2, w1, pch = 20)
> plot(dudi.pca(steppe$tab, scan = FALSE, scale = FALSE)$li[,1],
+     pch = 20, ylab = "PCA", xlab = "", type = "b")
> plot(dudi.coa(steppe$tab, scan = FALSE)$li[,1], pch = 20, 
+     ylab = "COA", xlab = "", type = "b")
> par(mfrow = c(1,1))
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "supcol"
> 
> ### * supcol
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: supcol
> ### Title: Projections of Supplementary Columns
> ### Aliases: supcol supcol.coa supcol.default
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(rpjdl)
> rpjdl.coa <- dudi.coa(rpjdl$fau, scan = FALSE, nf = 4)
> rpjdl.coa$co[1:3,]
        Comp1      Comp2     Comp3      Comp4
AR  1.3906689 -0.4366455 0.0899963 -0.4036248
CP -0.8298783 -0.2134175 0.9950055 -0.8763367
ST -0.6045373  0.1035953 0.6820577 -0.4254950
> supcol (rpjdl.coa,rpjdl$fau[,1:3])$cosup # the same
        Comp1      Comp2     Comp3      Comp4
AR  1.3906689 -0.4366455 0.0899963 -0.4036248
CP -0.8298783 -0.2134175 0.9950055 -0.8763367
ST -0.6045373  0.1035953 0.6820577 -0.4254950
> 
> data(doubs)
> dudi1 <- dudi.pca(doubs$poi, scal = FALSE, scan = FALSE)
> s.arrow(dudi1$co)
> s.arrow(supcol.default(dudi1,data.frame(scalewt(doubs$mil)))$cosup,
+     add.p = TRUE, clab = 2)
> symbols(0, 0, circles = 1, inches = FALSE, add = TRUE)
> 
> 
> 
> cleanEx(); ..nameEx <- "suprow"
> 
> ### * suprow
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: suprow
> ### Title: Projections of Supplementary Rows
> ### Aliases: suprow suprow.coa suprow.pca suprow.default
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(euro123)
> par(mfrow = c(2,2))
> w <- euro123[[2]]
> dudi1 <- dudi.pca(w, scal = FALSE, scan = FALSE)
> s.arrow(dudi1$c1, sub = "Classical", possub = "bottomright", csub = 2.5)
> s.label(suprow(dudi1,w), add.plot = TRUE, clab = 0.75)
> 
> s.arrow(dudi1$c1,
+     sub = "Without centring", possub = "bottomright", csub = 2.5)
> s.label(suprow.default(dudi1,w), clab = 0.75, add.plot = TRUE)
> 
> triangle.plot(w, clab = 0.75, label = row.names(w), scal = FALSE)
> triangle.plot(w, clab = 0.75, label = row.names(w), scal = TRUE)
> 
> data(rpjdl)
> rpjdl.coa <- dudi.coa(rpjdl$fau, scann = FALSE, nf = 4)
> rpjdl.coa$li[1:3,]
     Axis1       Axis2     Axis3     Axis4
1 1.449656 -1.59644004 1.2912481 2.0346309
2 1.308057 -1.56619615 1.2428411 1.6905682
3 1.042200  0.00201931 0.2788701 0.1574388
> suprow(rpjdl.coa,rpjdl$fau[1:3,]) # idem
$tabsup
  AR CP ST CC        UE PV JT    GT LA OO PP GG PM PC PR AA SE CB TT TM    MS
1  0  0  0  0 0.0000000  0  0 0.000  0  0  0  0  0  0  0  0  0  0  0  0 0.125
2  0  0  0  0 0.0000000  0  0 0.125  0  0  0  0  0  0  0  0  0  0  0  0 0.125
3  0  0  0  0 0.1428571  0  0 0.000  0  0  0  0  0  0  0  0  0  0  0  0 0.000
     MO        OH        OL        SO LM ER HP SH SB SA SC SM SN SP SU PB Rl PO
1 0.125 0.1250000 0.1250000 0.0000000  0  0  0  0  0  0  0  0  0  0  0  0  0  0
2 0.125 0.1250000 0.1250000 0.0000000  0  0  0  0  0  0  0  0  0  0  0  0  0  0
3 0.000 0.1428571 0.1428571 0.1428571  0  0  0  0  0  0  0  0  0  0  0  0  0  0
     AC LS CH    CA        CN        SS FC MC EC EH        El    PD
1 0.125  0  0 0.125 0.1250000 0.0000000  0  0  0  0 0.0000000 0.125
2 0.125  0  0 0.125 0.0000000 0.1250000  0  0  0  0 0.0000000 0.000
3 0.000  0  0 0.000 0.1428571 0.1428571  0  0  0  0 0.1428571 0.000

$lisup
     Axis1       Axis2     Axis3     Axis4
1 1.449656 -1.59644004 1.2912481 2.0346309
2 1.308057 -1.56619615 1.2428411 1.6905682
3 1.042200  0.00201931 0.2788701 0.1574388

> 
> data(deug)
> deug.dudi <- dudi.pca(df = deug$tab, center = deug$cent, 
+     scale = FALSE, scannf = FALSE)
> suprow(deug.dudi, deug$tab[1:3,]) # the supplementary individuals are centered
$tabsup
  Algebra Analysis Proba Informatic Economy Option1 Option2 English Sport
1     -10     -4.0   -14       -4.0    -8.1      -3       4    -1.0  11.5
2     -13      4.5    -3        2.0    12.0       4       2     6.0  11.5
3     -13     11.0   -11        4.5    12.0       4       7    -0.4  11.5

$lisup
      Axis1     Axis2
1 -14.86352 12.399345
2 -16.49579 -8.738351
3 -21.97732 -9.038219

> deug.dudi$li[1:3,]  # idem
      Axis1     Axis2
1 -14.86352 12.399345
2 -16.49579 -8.738351
3 -21.97732 -9.038219
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "symbols.phylog"
> 
> ### * symbols.phylog
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: symbols.phylog
> ### Title: Representation of a quantitative variable in front of a
> ###   phylogenetic tree
> ### Aliases: symbols.phylog dotchart.phylog
> ### Keywords: hplot
> 
> ### ** Examples
> 
> data(mjrochet)
> mjrochet.phy <- newick2phylog(mjrochet$tre)
> tab0 <- data.frame(scalewt(log(mjrochet$tab)))
> par(mfrow=c(3,2))
> for (j in 1:6) {
+     w <- tab0[,j]
+     symbols.phylog(phylog = mjrochet.phy, w, csi = 1.5, cleg = 1.5,
+      sub = names(tab0)[j], csub = 3)
+ }
> par(mfrow=c(1,1))
> 
> par(mfrow=c(2,3))
> for (j in 1:6) {
+     w <- tab0[,j]
+     dotchart.phylog(mjrochet.phy, w, cdot=1.5,
+         sub=names(tab0)[j],csub=3,cnodes=2,ceti=1.5)
+ }
> par(mfrow=c(1,1))
> 
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "syndicats"
> 
> ### * syndicats
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: syndicats
> ### Title: Two Questions asked on a Sample of 1000 Respondents
> ### Aliases: syndicats
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(syndicats)
> par(mfrow = c(1,2))
> dudi1 <- dudi.coa(syndicats, scan = FALSE)
> score (dudi1, 1, TRUE)
> score (dudi1, 1, FALSE)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "t3012"
> 
> ### * t3012
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: t3012
> ### Title: Average temperatures of 30 French cities
> ### Aliases: t3012
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(t3012)
> data(elec88)
> area.plot(elec88$area)
> s.arrow(t3012$xy, ori = as.numeric(t3012$xy["Paris",]),
+  add.p = TRUE)
> 
> 
> 
> cleanEx(); ..nameEx <- "table.cont"
> 
> ### * table.cont
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: table.cont
> ### Title: Plot of Contingency Tables
> ### Aliases: table.cont
> ### Keywords: hplot
> 
> ### ** Examples
> 
> data(chats)
> chatsw <- data.frame(t(chats))
> chatscoa <- dudi.coa(chatsw, scann = FALSE)
> par(mfrow = c(2,2))
> table.cont(chatsw, abmean.x = TRUE, csi = 2, abline.x = TRUE, 
+     clabel.r = 1.5, clabel.c = 1.5)
> table.cont(chatsw, abmean.y = TRUE, csi = 2, abline.y = TRUE, 
+     clabel.r = 1.5, clabel.c = 1.5)
> table.cont(chatsw, x = chatscoa$c1[,1], y = chatscoa$l1[,1],
+     abmean.x = TRUE, csi = 2, abline.x = TRUE, clabel.r = 1.5, clabel.c = 1.5)
> table.cont(chatsw, x = chatscoa$c1[,1], y = chatscoa$l1[,1],
+     abmean.y = TRUE, csi = 2, abline.y = TRUE, clabel.r = 1.5, clabel.c = 1.5)
> par(mfrow = c(1,1))
> 
> ## Not run: 
> ##D data(rpjdl)
> ##D w <- data.frame(t(rpjdl$fau))
> ##D wcoa <- dudi.coa(w, scann = FALSE)
> ##D table.cont(w, abmean.y = TRUE, x = wcoa$c1[,1], y = rank(wcoa$l1[,1]),
> ##D     csi = 0.2, clabel.c = 0, row.labels = rpjdl$lalab, clabel.r = 0.75)
> ## End(Not run)
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "table.dist"
> 
> ### * table.dist
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: table.dist
> ### Title: Graph Display for Distance Matrices
> ### Aliases: table.dist
> ### Keywords: hplot
> 
> ### ** Examples
> 
> data(eurodist)
> table.dist(eurodist, labels = attr(eurodist, "Labels"))
> 
> 
> 
> cleanEx(); ..nameEx <- "table.paint"
> 
> ### * table.paint
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: table.paint
> ### Title: Plot of the arrays by grey levels
> ### Aliases: table.paint
> ### Keywords: hplot
> 
> ### ** Examples
> 
> data(rpjdl)
> X <- data.frame(t(rpjdl$fau))
> Y <- data.frame(t(rpjdl$mil))
> layout(matrix(c(1,2,2,2,1,2,2,2,1,2,2,2,1,2,2,2), 4, 4))
> coa1 <- dudi.coa(X, scan = FALSE)
> x <- rank(coa1$co[,1])
> y <- rank(coa1$li[,1])
> table.paint(Y, x = x, y = 1:8, clabel.c = 0, cleg = 0)
> abline(v = 114.9, lwd = 3, col = "red")
> abline(v = 66.4, lwd = 3, col = "red")
> table.paint(X, x = x, y = y, clabel.c = 0, cleg = 0,
+     row.lab = paste("  ", row.names(X), sep = ""))
> abline(v = 114.9, lwd = 3, col = "red")
> abline(v = 66.4, lwd = 3, col = "red")
> 
> 
> 
> cleanEx(); ..nameEx <- "table.phylog"
> 
> ### * table.phylog
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: table.phylog
> ### Title: Plot arrays in front of a phylogenetic tree
> ### Aliases: table.phylog
> ### Keywords: hplot
> 
> ### ** Examples
> 
> data(newick.eg)
> w.phy <- newick2phylog(newick.eg[[9]])
> w.tab <- data.frame(matrix(rnorm(620), 31, 20))
> row.names(w.tab) <- sort(names(w.phy$leaves))
> table.phylog(w.tab, w.phy, csi = 1.5, f = 0.5,
+  clabel.n = 0.75, clabel.c = 0.5)
> 
> 
> 
> cleanEx(); ..nameEx <- "table.value"
> 
> ### * table.value
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: table.value
> ### Title: Plot of the Arrays
> ### Aliases: table.value table.prepare
> ### Keywords: hplot
> 
> ### ** Examples
> 
> data(olympic)
> w <- olympic$tab
> w <- data.frame(scale(w))
> wpca <- dudi.pca(w, scann = FALSE)
> par(mfrow = c(1,3))
> table.value(w, csi = 2, clabel.r = 2, clabel.c = 2)
> table.value(w, y = rank(wpca$li[,1]), x = rank(wpca$co[,1]), csi = 2,
+     clabel.r = 2, clabel.c = 2)
> table.value(w, y = wpca$li[,1], x = wpca$co[,1], csi = 2, 
+     clabel.r = 2, clabel.c = 2)
> par(mfrow = c(1,1))
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "tarentaise"
> 
> ### * tarentaise
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: tarentaise
> ### Title: Mountain Avifauna
> ### Aliases: tarentaise
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(tarentaise)
> coa1 <- dudi.coa(tarentaise$ecol, sca = FALSE, nf = 2)
> s.class(coa1$li, tarentaise$envir$alti, wt = coa1$lw)
> 
> acm1 <- dudi.acm(tarentaise$envir, sca = FALSE, nf = 2)
> s.class(acm1$li, tarentaise$envir$alti)
> 
> 
> 
> cleanEx(); ..nameEx <- "taxo.eg"
> 
> ### * taxo.eg
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: taxo.eg
> ### Title: Examples of taxonomy
> ### Aliases: taxo.eg
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(taxo.eg)
> taxo.eg[[1]]
      genre famille ordre
esp8     g2    fam2  ORD2
esp3     g1    fam1  ORD1
esp1     g1    fam1  ORD1
esp2     g1    fam1  ORD1
esp4     g1    fam1  ORD1
esp14    g8    fam5  ORD2
esp15    g8    fam5  ORD2
esp9     g3    fam2  ORD2
esp13    g7    fam4  ORD2
esp12    g6    fam4  ORD2
esp11    g5    fam3  ORD2
esp10    g4    fam3  ORD2
esp5     g1    fam1  ORD1
esp6     g1    fam1  ORD1
esp7     g1    fam1  ORD1
> as.taxo(taxo.eg[[1]])
      genre famille ordre
esp3     g1    fam1  ORD1
esp1     g1    fam1  ORD1
esp2     g1    fam1  ORD1
esp4     g1    fam1  ORD1
esp5     g1    fam1  ORD1
esp6     g1    fam1  ORD1
esp7     g1    fam1  ORD1
esp8     g2    fam2  ORD2
esp9     g3    fam2  ORD2
esp10    g4    fam3  ORD2
esp11    g5    fam3  ORD2
esp12    g6    fam4  ORD2
esp13    g7    fam4  ORD2
esp14    g8    fam5  ORD2
esp15    g8    fam5  ORD2
> class(taxo.eg[[1]])
[1] "data.frame"
> class(as.taxo(taxo.eg[[1]]))
[1] "data.frame" "taxo"      
> 
> tax.phy <- taxo2phylog(as.taxo(taxo.eg[[1]]))
> plot.phylog(tax.phy,clabel.l=1)
> 
> par(mfrow = c(1,2))
> table.phylog(tax.phy$Bindica,tax.phy)
> table.phylog(tax.phy$Bscores,tax.phy)
> par(mfrow = c(1,1))
> 
> radial.phylog(taxo2phylog(as.taxo(taxo.eg[[2]])))
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "tintoodiel"
> 
> ### * tintoodiel
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: tintoodiel
> ### Title: Tinto and Odiel estuary geochemistry
> ### Aliases: tintoodiel
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(tintoodiel)
> 
> ## Not run: 
> ##D if (require(pixmap, quiet = TRUE)){
> ##D estuary.pnm <- read.pnm(system.file("pictures/tintoodiel.pnm",
> ##D     package = "ade4"))
> ##D s.label(tintoodiel$xy,pixmap = estuary.pnm, neig = tintoodiel$neig,
> ##D     clab = 0, cpoi = 2, cneig = 3, addax = FALSE, cgrid = 0, grid = FALSE)
> ##D }
> ## End(Not run)
> 
> estuary.pca <- dudi.pca(tintoodiel$tab, scan = FALSE, nf = 4)
> if (require(maptools, quiet = TRUE) & require(spdep, quiet = TRUE)) {
+ estuary.listw <- nb2listw(neig2nb(tintoodiel$neig))
+ estuary.pca.ms <- multispati(estuary.pca, estuary.listw, scan = FALSE,
+     nfposi = 3,nfnega = 2)
+ summary(estuary.pca.ms)
+ par(mfrow = c(1,2))
+ barplot(estuary.pca$eig)
+ barplot(estuary.pca.ms$eig)
+ par(mfrow = c(1,1))
+ }
Loading required package: foreign
Loading required package: tripack
Loading required package: SparseM

Multivariate Spatial Analysis
Call: multispati(dudi = estuary.pca, listw = estuary.listw, scannf = FALSE, 
    nfposi = 3, nfnega = 2)

Scores from the first duality diagramm:
         var       cum     ratio        moran
RS1 4.401792  4.401792 0.2751120  0.222781944
RS2 3.133961  7.535753 0.4709846  0.217365304
RS3 1.818129  9.353883 0.5846177  0.004424936
RS4 1.379856 10.733739 0.6708587 -0.209208617

Eigenvalues decomposition:
            eig      var      moran
CS1   1.5292058 3.707268  0.4124886
CS2   0.6486201 2.415632  0.2685095
CS3   0.4969485 1.126228  0.4412505
CS15 -0.2964507 1.648786 -0.1797994
CS16 -0.4623624 1.365391 -0.3386300
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "tithonia"
> 
> ### * tithonia
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: tithonia
> ### Title: Phylogeny and quantitative traits of flowers
> ### Aliases: tithonia
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(tithonia)
> phy <- newick2phylog(tithonia$tre)
> tab <- log(tithonia$tab + 1)
> table.phylog(scalewt(tab), phy)
> gearymoran(phy$Wmat, tab)
class: krandtest 
test number:   14 
permutation number:   999 
   test   obs   P(X<=obs) P(X>=obs)
1  morho1 0.732 0.995     0.007    
2  morho2 0.382 0.756     0.246    
3  morho3 0.371 0.736     0.266    
4  morho4 0.257 0.252     0.75     
5  morho5 0.446 0.904     0.098    
6  morho6 0.409 0.864     0.138    
7  demo7  0.442 0.954     0.048    
8  demo8  0.482 0.966     0.036    
9  demo9  0.304 0.385     0.617    
10 demo10 0.274 0.191     0.811    
11 demo11 0.446 0.893     0.109    
12 demo12 0.264 0.121     0.881    
13 demo13 0.609 0.996     0.006    
14 demo14 0.39  0.818     0.184    

> gearymoran(phy$Amat, tab)
class: krandtest 
test number:   14 
permutation number:   999 
   test   obs    P(X<=obs) P(X>=obs)
1  morho1 0.538  0.996     0.006    
2  morho2 0.1    0.761     0.241    
3  morho3 0.07   0.668     0.334    
4  morho4 -0.092 0.256     0.746    
5  morho5 0.351  0.985     0.017    
6  morho6 0.177  0.861     0.141    
7  demo7  0.45   0.999     0.003    
8  demo8  0.255  0.947     0.055    
9  demo9  -0.011 0.487     0.515    
10 demo10 -0.097 0.314     0.688    
11 demo11 0.27   0.92      0.082    
12 demo12 -0.199 0.119     0.883    
13 demo13 0.636  1         0.001    
14 demo14 0.051  0.658     0.344    

> 
> 
> 
> cleanEx(); ..nameEx <- "tortues"
> 
> ### * tortues
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: tortues
> ### Title: Morphological Study of the Painted Turtle
> ### Aliases: tortues
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(tortues)
> xyz <- as.matrix(tortues[,1:3])
> ref <- -svd(xyz)$u[,1]
> pch0 <- c(1,20)[as.numeric(tortues$sex)]
> plot(ref, xyz[,1], ylim = c(40,180), pch = pch0)
> abline(lm(xyz[,1]~ -1 + ref))
> points(ref,xyz[,2], pch = pch0)
> abline(lm(xyz[,2]~ -1 + ref))
> points(ref,xyz[,3], pch = pch0)
> abline(lm(xyz[,3]~ -1 + ref))
> 
> 
> 
> cleanEx(); ..nameEx <- "toxicity"
> 
> ### * toxicity
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: toxicity
> ### Title: Homogeneous Table
> ### Aliases: toxicity
> ### Keywords: datasets
> 
> ### ** 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)
> 
> 
> 
> cleanEx(); ..nameEx <- "triangle.class"
> 
> ### * triangle.class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: triangle.class
> ### Title: Triangular Representation and Groups of points
> ### Aliases: triangle.class
> ### Keywords: hplot
> 
> ### ** Examples
> 
> data(euro123)
> par(mfrow = c(2,2))
> 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$an, cell = 0, csta = 1, scale = FALSE, 
+     draw.line = FALSE, show.posi = FALSE)
> 
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "triangle.plot"
> 
> ### * triangle.plot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: triangle.plot
> ### Title: Triangular Plotting
> ### Aliases: triangle.plot triangle.biplot triangle.param
> ###   triangle.posipoint add.position.triangle
> ### Keywords: hplot
> 
> ### ** Examples
> 
> data (euro123)
> tot <- rbind.data.frame(euro123$in78, euro123$in86, euro123$in97)
> row.names(tot) <- paste(row.names(euro123$in78), rep(c(1,2,3), rep(12,3)),
+     sep = "")
> triangle.plot(tot, label = row.names(tot), clab = 1)
> 
> par(mfrow = c(2,2))
> triangle.plot(euro123$in78, clab = 0, cpoi = 2, addmean = TRUE, 
+     show = FALSE)
> triangle.plot(euro123$in86, label = row.names(euro123$in78), clab = 0.8)
> triangle.biplot(euro123$in78, euro123$in86)
> triangle.plot(rbind.data.frame(euro123$in78, euro123$in86), clab = 1, 
+     addaxes = TRUE, sub = "Principal axis", csub = 2, possub = "topright")
> 
> triangle.plot(euro123[[1]], min3 = c(0,0.2,0.3), max3 = c(0.5,0.7,0.8),
+     clab = 1, label = row.names(euro123[[1]]), addax = TRUE)
> triangle.plot(euro123[[2]], min3 = c(0,0.2,0.3), max3 = c(0.5,0.7,0.8), 
+     clab = 1, label = row.names(euro123[[1]]), addax = TRUE)
> triangle.plot(euro123[[3]], min3 = c(0,0.2,0.3), max3 = c(0.5,0.7,0.8), 
+     clab = 1, label = row.names(euro123[[1]]), addax = TRUE)
> triangle.plot(rbind.data.frame(euro123[[1]], euro123[[2]], euro123[[3]]))
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "trichometeo"
> 
> ### * trichometeo
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: trichometeo
> ### Title: Pair of Ecological Data
> ### Aliases: trichometeo
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(trichometeo)
> faulog <- log(trichometeo$fau + 1)
> pca1 <- dudi.pca(trichometeo$meteo, scan = FALSE)
> niche1 <- niche(pca1, faulog, scan = FALSE)
> s.label(niche1$ls, clab = 0)
> s.distri(niche1$ls, faulog, clab = 0.6, add.p = TRUE, 
+     cell = 0, csta = 0.3)
> s.arrow(7 * niche1$c1, clab = 1, add.p = TRUE)
> 
> 
> 
> cleanEx(); ..nameEx <- "ungulates"
> 
> ### * ungulates
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ungulates
> ### Title: Phylogeny and quantitative traits of ungulates.
> ### Aliases: ungulates
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(ungulates)
> ung.phy <- newick2phylog(ungulates$tre)
> plot.phylog(ung.phy,clabel.l=1.25,clabel.n=0.75)
> ung.x <- log(ungulates$tab[,1])
> ung.y <- log((ungulates$tab[,2]+ungulates$tab[,3])/2)
> names(ung.x) <- names(ung.phy$leaves)
> names(ung.y) <- names(ung.x)
> plot(ung.x,ung.y)
> abline(lm(ung.y~ung.x))
> symbols.phylog(ung.phy,ung.x-mean(ung.x))
> dotchart.phylog(ung.phy,ung.x,cle=1.5,cno=1.5,cdot=1)
> orthogram(ung.x,ung.phy$Bscores,nrep=9999)
class: krandtest 
test number:   4 
permutation number:   9999 
  test  obs    P(X<=obs) P(X>=obs)
1 R2Max 0.4703 0.9904    0.0098   
2 SkR2k 7.2903 0.1405    0.8597   
3 Dmax  0.2474 0.8206    0.1796   
4 SCE   0.4154 0.8176    0.1826   

> ung.z <- residuals(lm(ung.y~ung.x))
> names(ung.z) <- names(ung.phy$leaves)
> dotchart.phylog(ung.phy,ung.z,cle=1.5,cno=1.5,cdot=1,ceti=0.75)
> orthogram(ung.z,ung.phy$Bscores,nrep=9999)
class: krandtest 
test number:   4 
permutation number:   9999 
  test  obs    P(X<=obs) P(X>=obs)
1 R2Max 0.3567 0.7442    0.256    
2 SkR2k 5.3205 0.0096    0.9906   
3 Dmax  0.4273 0.986     0.0142   
4 SCE   1.0475 0.9806    0.0196   

> 
> 
> 
> cleanEx(); ..nameEx <- "uniquewt.df"
> 
> ### * uniquewt.df
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: uniquewt.df
> ### Title: Elimination of Duplicated Rows in a Array
> ### Aliases: uniquewt.df
> ### Keywords: utilities
> 
> ### ** Examples
> 
> data(ecomor)
> forsub.r <- uniquewt.df(ecomor$forsub)
> attr(forsub.r, "factor")
  [1] 1  1  1  2  1  3  3  4  4  4  5  5  4  4  6  6  4  5  7  5  4  4  5  8  1 
 [26] 4  6  6  9  5  1  10 4  4  4  5  5  5  4  4  4  4  4  5  4  3  11 12 13 5 
 [51] 10 7  4  4  5  5  5  5  4  4  4  7  13 13 13 14 8  7  12 15 5  1  5  5  5 
 [76] 5  5  5  9  5  12 5  7  16 7  7  8  12 12 12 12 12 12 12 12 17 18 5  5  5 
[101] 5  5  4  19 19 5  5  4  5  5  6  2  1  7  2  2  13 13 20 4  14 21 14 21 14
[126] 14 22 22 4 
Levels: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
> forsub.r[1,]
     foliage ground twig bush trunk aerial
E033       1      0    0    1     0      0
> ecomor$forsub[126,] #idem
     foliage ground twig bush trunk aerial
E072       0      0    1    0     1      0
> 
> dudi.pca(ecomor$forsub, scale = FALSE, scann = FALSE)$eig
[1] 0.37533941 0.24104252 0.15616250 0.09072903 0.07514625 0.04457409
> # [1] 0.36845 0.24340 0.15855 0.09052 0.07970 0.04490
> w1 <- attr(forsub.r, "len.class") / sum(attr(forsub.r,"len.class"))
> dudi.pca(forsub.r, row.w = w1, scale = FALSE, scann = FALSE)$eig
[1] 0.37533941 0.24104252 0.15616250 0.09072903 0.07514625 0.04457409
> # [1] 0.36845 0.24340 0.15855 0.09052 0.07970 0.04490
> 
> 
> 
> cleanEx(); ..nameEx <- "variance.phylog"
> 
> ### * variance.phylog
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: variance.phylog
> ### Title: The phylogenetic ANOVA
> ### Aliases: variance.phylog
> ### Keywords: models
> 
> ### ** Examples
> 
> data(njplot)
> njplot.phy <- newick2phylog(njplot$tre)
> variance.phylog(njplot.phy,njplot$tauxcg)
$lm

Call:
lm(formula = fmla, data = df)

Coefficients:
(Intercept)           A1           A2           A3           A4           A5  
 -4.105e-15    2.884e-01    3.291e-01   -1.967e-02    4.661e-02    3.326e-01  
         A6           A7           A8           A9          A10          A11  
  9.722e-02   -4.594e-01    5.077e-02   -2.489e-01    3.056e-01    2.126e-01  
        A12          A13  
 -1.691e-01   -1.293e-01  


$anova
Analysis of Variance Table

Response: z
          Df Sum Sq Mean Sq F value    Pr(>F)    
A1         1 2.9935  2.9935  9.2111  0.006080 ** 
A2         1 3.8986  3.8986 11.9960  0.002209 ** 
A3         1 0.0139  0.0139  0.0429  0.837899    
A4         1 0.0782  0.0782  0.2407  0.628576    
A5         1 3.9834  3.9834 12.2569  0.002019 ** 
A6         1 0.3402  0.3402  1.0469  0.317332    
A7         1 7.5986  7.5986 23.3809 7.855e-05 ***
A8         1 0.0928  0.0928  0.2855  0.598470    
A9         1 2.2304  2.2304  6.8630  0.015646 *  
A10        1 3.3618  3.3618 10.3442  0.003977 ** 
A11        1 1.6270  1.6270  5.0064  0.035705 *  
A12        1 1.0293  1.0293  3.1673  0.088946 .  
A13        1 0.6023  0.6023  1.8532  0.187190    
Residuals 22 7.1498  0.3250                      
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

$sumry
             Df Sum Sq   Mean Sq F value Pr(>F)
Phylogenetic 13 27.85018 2.14232 6.59193 6e-05 
Residuals    22 7.14982  0.32499               

> par(mfrow = c(1,2))
> table.phylog(njplot.phy$Ascores, njplot.phy, clabel.row = 0,
+  clabel.col = 0.1, clabel.nod = 0.6, csize = 1)
> dotchart.phylog(njplot.phy, njplot$tauxcg, clabel.nodes = 0.6)
> orthogram(njplot$tauxcg, njplot.phy$Ascores)
class: krandtest 
test number:   4 
permutation number:   999 
  test  obs    P(X<=obs) P(X>=obs)
1 R2Max 0.217  0.846     0.156    
2 SkR2k 10.129 0.001     1        
3 Dmax  0.436  1         0.001    
4 SCE   2.414  1         0.001    

> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "veuvage"
> 
> ### * veuvage
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: veuvage
> ### Title: Example for Centring in PCA
> ### Aliases: veuvage
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(veuvage)
> par(mfrow = c(3,2))
> for (j in 1:6) plot(veuvage$age, veuvage$tab[,j],
+     xlab = "âge", ylab = "pourcentage de veufs",
+     type = "b", main = names(veuvage$tab)[j])
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "westafrica"
> 
> ### * westafrica
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: westafrica
> ### Title: Freshwater fish zoogeography in west Africa
> ### Aliases: westafrica
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(westafrica)
> 
> s.label(westafrica$cadre, xlim = c(30,500), ylim = c(50,290),
+     cpoi = 0, clab = 0, grid = FALSE, addax = 0)
> old.par <- par(no.readonly = TRUE)
> par(mar = c(0.1, 0.1, 0.1, 0.1))
> rect(30,0,500,290)
> polygon(westafrica$atlantic,col = "lightblue")
> points(westafrica$riv.xy, pch = 20, cex = 1.5)
> apply(westafrica$lines, 1, function(x) segments(x[1], x[2], x[3],
+     x[4], lwd = 1))
NULL
> apply(westafrica$riv.xy,1, function(x) segments(x[1], x[2], x[3],
+     x[4], lwd = 1))
NULL
> text(c(175,260,460,420), c(275,200,250,100), c("Senegal","Niger",
+     "Niger","Volta"))
> par(srt = 270)
> text(westafrica$riv.xy$x2, westafrica$riv.xy$y2-10, 
+     westafrica$riv.names, adj = 0, cex = 0.75)
> par(old.par)
> rm(old.par)
> 
> # multivariate analysis
> afri.w <- data.frame(t(westafrica$tab))
> afri.dist <- dist.binary(afri.w,1)
> afri.pco <- dudi.pco(afri.dist, scan = FALSE, nf = 3)
> par(mfrow = c(3,1))
> barplot(afri.pco$li[,1])
> barplot(afri.pco$li[,2])
> barplot(afri.pco$li[,3])
> 
> if (require(spdep, quiet = TRUE)){
+     #multivariate spatial analysis
+     afri.neig <- neig(n.line = 33)
+     afri.nb <- neig2nb(afri.neig)
+     afri.listw <- nb2listw(afri.nb)
+     afri.ms <- multispati(afri.pco, afri.listw, scan = FALSE,
+         nfposi = 6, nfnega = 0)
+     par(mfrow = c(3,1))
+     barplot(afri.ms$li[,1])
+     barplot(afri.ms$li[,2])
+     barplot(afri.ms$li[,3])
+ 
+     par(mfrow = c(2,2))
+     s.label(afri.ms$li, clab = 0.75, cpoi = 0, neig = afri.neig,
+        cneig = 1.5)
+     s.value(afri.ms$li, afri.ms$li[,3])
+     s.value(afri.ms$li, afri.ms$li[,4])
+     s.value(afri.ms$li, afri.ms$li[,5])
+     summary(afri.ms)
+ }
Loading required package: tripack
Loading required package: maptools
Loading required package: foreign
Loading required package: SparseM

Multivariate Spatial Analysis
Call: multispati(dudi = afri.pco, listw = afri.listw, scannf = FALSE, 
    nfposi = 6, nfnega = 0)

Scores from the first duality diagramm:
           var        cum     ratio     moran
RS1 0.05660710 0.05660710 0.1635437 0.9598444
RS2 0.03641095 0.09301805 0.2687386 0.7372155
RS3 0.02776793 0.12078598 0.3489629 0.6735561

Eigenvalues decomposition:
           eig        var     moran
CS1 0.05455534 0.05641792 0.9669861
CS2 0.03084339 0.03406832 0.9053395
CS3 0.02052642 0.02544985 0.8065439
CS4 0.01671907 0.01891409 0.8839480
CS5 0.01342381 0.01539323 0.8720593
CS6 0.01043363 0.01371212 0.7609053
> 
> par(mfrow = c(1,1))
> plot(hclust(afri.dist,"ward"),h=-0.2)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "within"
> 
> ### * within
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: within
> ### Title: Within Analyses
> ### Aliases: within print.within plot.within
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(meaudret)
> par(mfrow = c(2,2))
> pca1 <- dudi.pca(meaudret$mil, scan = FALSE, nf = 4)
> s.traject(pca1$li, meaudret$plan$sta, 
+     sub = "Principal Component Analysis", csub = 1.5)
> wit1 <- within(pca1, meaudret$plan$sta, scan = FALSE, nf = 2)
> s.traject(wit1$li, meaudret$plan$sta, 
+     sub = "Within site Principal Component Analysis", csub = 1.5)
> s.corcircle (wit1$as)
> par(mfrow = c(1,1))
> plot(wit1)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "within.pca"
> 
> ### * within.pca
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: within.pca
> ### Title: Normed within Principal Component Analysis
> ### Aliases: within.pca
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(meaudret)
> wit1 <- within.pca(meaudret$mil, meaudret$plan$dat, 
+     scan = FALSE, scal = "partial")
> kta1 <- ktab.within(wit1, colnames = rep(c("S1","S2","S3","S4","S5"), 4))
> unclass(kta1)
$spring
                 S1         S2          S3         S4         S5
Temp  -1.372813e+00 -0.3922323 -0.39223227  0.5883484  1.5689291
Debit -1.615156e+00 -0.4251482 -0.01830782  0.8157149  1.2428973
pH     2.041241e-01 -1.8371173  0.20412415  1.2247449  0.2041241
Condu  2.364459e-17  1.9069252 -0.47673129 -0.4767313 -0.9534626
Dbo5  -9.621078e-01  1.9351487 -0.41545565 -0.3061252 -0.2514600
Oxyd  -6.364382e-01  1.9923283 -0.49808208 -0.4980821 -0.3597259
Ammo  -7.376580e-01  1.9837018 -0.45854414 -0.4087024 -0.3787973
Nitra -5.281643e-01 -1.6155614  0.40389035  0.4038903  1.3359450
Phos  -1.047323e+00  1.6189391 -1.06650488  0.1419449  0.3529441

$summer
              S1         S2         S3          S4         S5
Temp  -1.1666667 -1.1666667  0.5000000  1.33333333  0.5000000
Debit -1.2683846 -0.7763389 -0.2296214  0.86381368  1.4105312
pH     0.9354143 -1.4031215 -0.7349684 -0.06681531  1.2694909
Condu -1.5297323  0.9448347  1.1697953  0.04499213 -0.6298898
Dbo5  -1.0875080  1.4231586  0.6175971  0.21481639 -1.1680641
Oxyd  -0.7664063  1.9508524 -0.2786932 -0.20901990 -0.6967330
Ammo  -1.1379075  1.3255250  0.8424990  0.07982640 -1.1099429
Nitra -0.8553618 -1.4868370  0.4936988  0.69462267  1.1538773
Phos  -1.6393299  0.6112322  1.1546918  0.48335936 -0.6099535

$autumn
              S1          S2          S3          S4         S5
Temp  -1.6035675  1.06904497 -0.26726124  1.06904497 -0.2672612
Debit -1.8766905 -0.08487545  0.66957300  0.95249116  0.3395018
pH     0.5619515 -1.31122014 -0.84292723  0.09365858  1.4985373
Condu -0.7012869  1.87009833  0.11688115 -0.35064344 -0.9350492
Dbo5  -0.6044785  1.97542987 -0.19199318 -0.57447961 -0.6044785
Oxyd  -0.7363901  1.97092649 -0.23102435 -0.41151212 -0.5919999
Ammo  -0.6470895  1.95761335 -0.09597377 -0.57374709 -0.6408030
Nitra -0.5674453 -1.62198962  1.13991221  0.83861383  0.2109089
Phos  -1.1374069  1.70830946  0.47677844 -0.44686983 -0.6008112

$winter
              S1         S2          S3         S4         S5
Temp   0.5000000  0.5000000 0.500000000  0.5000000 -2.0000000
Debit -1.5362076 -0.4891485 0.003125549  1.4330644  0.5891661
pH    -1.8864844  0.6859943 0.685994341  0.6859943 -0.1714986
Condu -0.9819805  0.9274260 1.472970759 -0.7092081 -0.7092081
Dbo5  -1.0927369  1.2659029 1.027053299 -0.1373385 -1.0628807
Oxyd  -0.9763927  1.2666716 1.134726600 -0.4486128 -0.9763927
Ammo  -1.1833898  1.0449484 1.310452499 -0.3584306 -0.8135805
Nitra -1.9709202  0.4077766 0.577683516  0.2378697  0.7475904
Phos  -1.0886790  0.4133145 1.744869109 -0.4175755 -0.6519291

$lw
[1] 1 1 1 1 1 1 1 1 1

$cw
 [1] 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
[20] 0.2

$blo
spring summer autumn winter 
     5      5      5      5 

$TL
   T L
1  1 1
2  1 2
3  1 3
4  1 4
5  1 5
6  1 6
7  1 7
8  1 8
9  1 9
10 2 1
11 2 2
12 2 3
13 2 4
14 2 5
15 2 6
16 2 7
17 2 8
18 2 9
19 3 1
20 3 2
21 3 3
22 3 4
23 3 5
24 3 6
25 3 7
26 3 8
27 3 9
28 4 1
29 4 2
30 4 3
31 4 4
32 4 5
33 4 6
34 4 7
35 4 8
36 4 9

$TC
   T C
1  1 1
2  1 2
3  1 3
4  1 4
5  1 5
6  2 1
7  2 2
8  2 3
9  2 4
10 2 5
11 3 1
12 3 2
13 3 3
14 3 4
15 3 5
16 4 1
17 4 2
18 4 3
19 4 4
20 4 5

$T4
   T 4
1  1 1
2  1 2
3  1 3
4  1 4
5  2 1
6  2 2
7  2 3
8  2 4
9  3 1
10 3 2
11 3 3
12 3 4
13 4 1
14 4 2
15 4 3
16 4 4

$call
ktab.within(dudiwit = wit1, colnames = rep(c("S1", "S2", "S3", 
    "S4", "S5"), 4))

$tabw
autumn spring summer winter 
  0.25   0.25   0.25   0.25 

> # See pta
> plot(wit1)
> 
> 
> 
> cleanEx(); ..nameEx <- "witwit.coa"
> 
> ### * witwit.coa
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: witwit.coa
> ### Title: Internal Correspondence Analysis
> ### Aliases: witwit.coa summary.witwit
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(ardeche)
> coa1 <- dudi.coa(ardeche$tab, scann = FALSE, nf = 4)
> ww <- witwit.coa(coa1, ardeche$row.blocks, ardeche$col.blocks, scann = FALSE)
> ww
Duality diagramm
class: witwit coa dudi
$call: witwit.coa(dudi = coa1, row.blocks = ardeche$row.blocks, col.blocks = ardeche$col.blocks, 
    scannf = FALSE)

$nf: 2 axis-components saved
$rank: 31
eigen values: 0.06858 0.06325 0.04253 0.03564 0.02911 ...
  vector length mode    content       
1 $cw    35     numeric column weights
2 $lw    43     numeric row weights   
3 $eig   31     numeric eigen values  

  data.frame nrow ncol content             
1 $tab       43   35   modified array      
2 $li        43   2    row coordinates     
3 $l1        43   2    row normed scores   
4 $co        35   2    column coordinates  
5 $c1        35   2    column normed scores
other elements: lbvar lbw cbvar cbw 
> s.class(ww$co, ardeche$sta.fac, clab = 1.5, cell = 0, axesell = FALSE)
> s.label(ww$co, add.p = TRUE, clab = 0.75)
> summary(ww)
Internal correspondence analysis
class: witwit coa dudi
$call: witwit.coa(dudi = coa1, row.blocks = ardeche$row.blocks, col.blocks = ardeche$col.blocks, 
    scannf = FALSE)
2 axis-components saved
eigen values: 0.06858 0.06325 0.04253 0.03564 0.02911 ...

Eigen value decomposition among row blocks
     Axis1  Axis2  weights
Eph  0.0511 0.0563 0.2879 
Ple  0.1154 0.0263 0.0653 
Col  0.0204 0.0709 0.3703 
Tri  0.1403 0.069  0.2766 
mean 0.0686 0.0632        

    Axis1 Axis2
Eph 215   256  
Ple 110   27   
Col 110   415  
Tri 566   302  
sum 1000  1000 

Eigen value decomposition among column blocks
      Comp1  Comp2  weights
jul82 0.0109 0.0706 0.1859 
aug82 0.0414 0.1063 0.1797 
nov82 0.017  7e-04  0.1054 
feb83 0.1915 0.0321 0.1364 
apr83 0.1384 0.0614 0.1895 
jul83 0.0244 0.0736 0.2031 
mean  0.0686 0.0632        

      Comp1 Comp2
jul82 30    207  
aug82 108   302  
nov82 26    1    
feb83 381   69   
apr83 383   184  
jul83 72    236  
sum   1000  1000 

> 
> 
> 
> cleanEx(); ..nameEx <- "worksurv"
> 
> ### * worksurv
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: worksurv
> ### Title: French Worker Survey (1970)
> ### Aliases: worksurv
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(worksurv)
> acm1 <- dudi.acm(worksurv, row.w = attr(worksurv,"counts"), 
+     scan = FALSE)
> par(mfrow = c(2,2))
> apply(worksurv,2, function(x) s.class(acm1$li, factor(x),
+     attr(worksurv, 'counts')))
NULL
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "yanomama"
> 
> ### * yanomama
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: yanomama
> ### Title: Distance Matrices
> ### Aliases: yanomama
> ### Keywords: datasets
> 
> ### ** Examples
> 
>     data(yanomama)
>     gen <- quasieuclid(as.dist(yanomama$gen)) # depends of mva
>     ant <- quasieuclid(as.dist(yanomama$ant)) # depends of mva
>     par(mfrow = c(2,2))
>     plot(gen, ant)
>     t1 <- mantel.randtest(gen, ant, 99);
>     plot(t1, main = "gen-ant-mantel") ; print(t1)
Monte-Carlo test
Observation: 0.2999879 
Call: mantel.randtest(m1 = gen, m2 = ant, nrepet = 99)
Based on 99 replicates
Simulated p-value: 0.05 
>     t1 <- procuste.rtest(pcoscaled(gen), pcoscaled(ant), 99)
>     plot(t1, main = "gen-ant-procuste") ; print(t1)
Monte-Carlo test
Observation: 0.6819023 
Call: procuste.rtest(df1 = pcoscaled(gen), df2 = pcoscaled(ant), nrepet = 99)
Based on 99 replicates
Simulated p-value: 0.01 
>     t1 <- RV.rtest(pcoscaled(gen), pcoscaled(ant), 99)
>     plot(t1, main = "gen-ant-RV") ; print(t1)
Monte-Carlo test
Observation: 0.4272698 
Call: RV.rtest(df1 = pcoscaled(gen), df2 = pcoscaled(ant), nrepet = 99)
Based on 99 replicates
Simulated p-value: 0.03 
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "zealand"
> 
> ### * zealand
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: zealand
> ### Title: Road distances in New-Zealand
> ### Aliases: zealand
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(zealand)
> 
> d0 = mat2dist(as.matrix(zealand$road))
> d1 = cailliez (d0)
> d2 = lingoes(d0)
> s.label(zealand$xy,lab=as.character(1:13),neig=zealand$neig)
> par(mfrow = c(2,2))
>     s.label(cmdscale(dist(zealand$xy)),lab = as.character(1:13),
+      neig = zealand$neig, sub = "Distance canonique", csub = 2)
>     s.label(cmdscale(d0), lab = as.character(1:13), neig = zealand$neig,
+         sub = "Distance routière", csub = 2)
>     s.label(cmdscale(d1), lab = as.character(1:13), neig = zealand$neig,
+         sub = "Distance routière / Cailliez", csub = 2)
>     s.label(cmdscale(d2), lab = as.character(1:13), neig = zealand$neig,
+         sub = "Distance routière / Lingoës", csub = 2)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  194.16 7.01 208.28 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')