[adegenet-commits] r542 - pkg/man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Mon Feb 1 16:32:40 CET 2010
Author: jombart
Date: 2010-02-01 16:32:40 +0100 (Mon, 01 Feb 2010)
New Revision: 542
Added:
pkg/man/H3N2.Rd
Removed:
pkg/man/h3n2.Rd
Log:
renaming
Copied: pkg/man/H3N2.Rd (from rev 541, pkg/man/h3n2.Rd)
===================================================================
--- pkg/man/H3N2.Rd (rev 0)
+++ pkg/man/H3N2.Rd 2010-02-01 15:32:40 UTC (rev 542)
@@ -0,0 +1,105 @@
+\encoding{UTF-8}
+\name{microbov}
+\alias{microbov}
+\docType{data}
+\title{Microsatellites genotypes of 15 cattle breeds}
+\description{
+This data set gives the genotypes of 704 cattle individuals for 30
+microsatellites recommended by the FAO. The individuals are divided into
+two countries (Afric, France), two species (Bos taurus, Bos indicus) and
+15 breeds. Individuals were chosen in order to avoid pseudoreplication
+according to their exact genealogy.
+}
+\usage{data(microbov)}
+\format{
+ \code{microbov} is a genind object with 3 supplementary components:
+ \describe{
+ \item{coun}{a factor giving the country of each individual (AF:
+ Afric; FR: France).}
+ \item{breed}{a factor giving the breed of each individual.}
+ \item{spe}{is a factor giving the species of each individual
+ (BT: Bos taurus; BI: Bos indicus).}
+ }
+}
+\source{
+Data prepared by Katayoun Moazami-Goudarzi and Denis Lalo\"e (INRA,
+Jouy-en-Josas, France)
+}
+\references{
+ Lalo\"e D., Jombart T., Dufour A.-B. and Moazami-Goudarzi K. (2007)
+ Consensus genetic structuring and typological value of markers using
+ Multiple Co-Inertia Analysis. \emph{Genetics Selection Evolution}.
+ \bold{39}: 545--567.
+}
+\examples{
+data(microbov)
+microbov
+summary(microbov)
+
+# make Y, a genpop object
+Y <- genind2genpop(microbov)
+
+# make allelic frequency table
+temp <- makefreq(Y,missing="mean")
+X <- temp$tab
+nsamp <- temp$nobs
+
+# perform 1 PCA per marker
+
+if(require(ade4)){
+kX <- ktab.data.frame(data.frame(X),Y at loc.nall)
+
+kpca <- list()
+for(i in 1:30) {kpca[[i]] <- dudi.pca(kX[[i]],scannf=FALSE,nf=2,center=TRUE,scale=FALSE)}
+}
+
+sel <- sample(1:30,4)
+col = rep('red',15)
+col[c(2,10)] = 'darkred'
+col[c(4,12,14)] = 'deepskyblue4'
+col[c(8,15)] = 'darkblue'
+
+# display %PCA
+par(mfrow=c(2,2))
+for(i in sel) {
+s.multinom(kpca[[i]]$c1,kX[[i]],n.sample=nsamp[,i],coulrow=col,sub=Y at loc.names[i])
+add.scatter.eig(kpca[[i]]$eig,3,xax=1,yax=2,posi="top")
+}
+
+# perform a Multiple Coinertia Analysis
+kXcent <- kX
+for(i in 1:30) kXcent[[i]] <- as.data.frame(scalewt(kX[[i]],center=TRUE,scale=FALSE))
+mcoa1 <- mcoa(kXcent,scannf=FALSE,nf=3, option="uniform")
+
+# coordinated %PCA
+mcoa.axes <- split(mcoa1$axis,Y at loc.fac)
+mcoa.coord <- split(mcoa1$Tli,mcoa1$TL[,1])
+var.coord <- lapply(mcoa.coord,function(e) apply(e,2,var))
+
+par(mfrow=c(2,2))
+for(i in sel) {
+s.multinom(mcoa.axes[[i]][,1:2],kX[[i]],n.sample=nsamp[,i],coulrow=col,sub=Y at loc.names[i])
+add.scatter.eig(var.coord[[i]],2,xax=1,yax=2,posi="top")
+}
+
+# reference typology
+par(mfrow=c(1,1))
+s.label(mcoa1$SynVar,lab=microbov at pop.names,sub="Reference typology",csub=1.5)
+add.scatter.eig(mcoa1$pseudoeig,nf=3,xax=1,yax=2,posi="top")
+
+# typologial values
+tv <- mcoa1$cov2
+tv <- apply(tv,2,function(c) c/sum(c))*100
+rownames(tv) <- Y at loc.names
+tv <- tv[order(Y at loc.names),]
+
+par(mfrow=c(3,1),mar=c(5,3,3,4),las=3)
+for(i in 1:3){
+barplot(round(tv[,i],3),ylim=c(0,12),yaxt="n",main=paste("Typological value -
+structure",i))
+axis(side=2,at=seq(0,12,by=2),labels=paste(seq(0,12,by=2),"\%"),cex=3)
+abline(h=seq(0,12,by=2),col="grey",lty=2)
+}
+
+}
+\keyword{datasets}
Deleted: pkg/man/h3n2.Rd
===================================================================
--- pkg/man/h3n2.Rd 2010-02-01 15:32:11 UTC (rev 541)
+++ pkg/man/h3n2.Rd 2010-02-01 15:32:40 UTC (rev 542)
@@ -1,105 +0,0 @@
-\encoding{UTF-8}
-\name{microbov}
-\alias{microbov}
-\docType{data}
-\title{Microsatellites genotypes of 15 cattle breeds}
-\description{
-This data set gives the genotypes of 704 cattle individuals for 30
-microsatellites recommended by the FAO. The individuals are divided into
-two countries (Afric, France), two species (Bos taurus, Bos indicus) and
-15 breeds. Individuals were chosen in order to avoid pseudoreplication
-according to their exact genealogy.
-}
-\usage{data(microbov)}
-\format{
- \code{microbov} is a genind object with 3 supplementary components:
- \describe{
- \item{coun}{a factor giving the country of each individual (AF:
- Afric; FR: France).}
- \item{breed}{a factor giving the breed of each individual.}
- \item{spe}{is a factor giving the species of each individual
- (BT: Bos taurus; BI: Bos indicus).}
- }
-}
-\source{
-Data prepared by Katayoun Moazami-Goudarzi and Denis Lalo\"e (INRA,
-Jouy-en-Josas, France)
-}
-\references{
- Lalo\"e D., Jombart T., Dufour A.-B. and Moazami-Goudarzi K. (2007)
- Consensus genetic structuring and typological value of markers using
- Multiple Co-Inertia Analysis. \emph{Genetics Selection Evolution}.
- \bold{39}: 545--567.
-}
-\examples{
-data(microbov)
-microbov
-summary(microbov)
-
-# make Y, a genpop object
-Y <- genind2genpop(microbov)
-
-# make allelic frequency table
-temp <- makefreq(Y,missing="mean")
-X <- temp$tab
-nsamp <- temp$nobs
-
-# perform 1 PCA per marker
-
-if(require(ade4)){
-kX <- ktab.data.frame(data.frame(X),Y at loc.nall)
-
-kpca <- list()
-for(i in 1:30) {kpca[[i]] <- dudi.pca(kX[[i]],scannf=FALSE,nf=2,center=TRUE,scale=FALSE)}
-}
-
-sel <- sample(1:30,4)
-col = rep('red',15)
-col[c(2,10)] = 'darkred'
-col[c(4,12,14)] = 'deepskyblue4'
-col[c(8,15)] = 'darkblue'
-
-# display %PCA
-par(mfrow=c(2,2))
-for(i in sel) {
-s.multinom(kpca[[i]]$c1,kX[[i]],n.sample=nsamp[,i],coulrow=col,sub=Y at loc.names[i])
-add.scatter.eig(kpca[[i]]$eig,3,xax=1,yax=2,posi="top")
-}
-
-# perform a Multiple Coinertia Analysis
-kXcent <- kX
-for(i in 1:30) kXcent[[i]] <- as.data.frame(scalewt(kX[[i]],center=TRUE,scale=FALSE))
-mcoa1 <- mcoa(kXcent,scannf=FALSE,nf=3, option="uniform")
-
-# coordinated %PCA
-mcoa.axes <- split(mcoa1$axis,Y at loc.fac)
-mcoa.coord <- split(mcoa1$Tli,mcoa1$TL[,1])
-var.coord <- lapply(mcoa.coord,function(e) apply(e,2,var))
-
-par(mfrow=c(2,2))
-for(i in sel) {
-s.multinom(mcoa.axes[[i]][,1:2],kX[[i]],n.sample=nsamp[,i],coulrow=col,sub=Y at loc.names[i])
-add.scatter.eig(var.coord[[i]],2,xax=1,yax=2,posi="top")
-}
-
-# reference typology
-par(mfrow=c(1,1))
-s.label(mcoa1$SynVar,lab=microbov at pop.names,sub="Reference typology",csub=1.5)
-add.scatter.eig(mcoa1$pseudoeig,nf=3,xax=1,yax=2,posi="top")
-
-# typologial values
-tv <- mcoa1$cov2
-tv <- apply(tv,2,function(c) c/sum(c))*100
-rownames(tv) <- Y at loc.names
-tv <- tv[order(Y at loc.names),]
-
-par(mfrow=c(3,1),mar=c(5,3,3,4),las=3)
-for(i in 1:3){
-barplot(round(tv[,i],3),ylim=c(0,12),yaxt="n",main=paste("Typological value -
-structure",i))
-axis(side=2,at=seq(0,12,by=2),labels=paste(seq(0,12,by=2),"\%"),cex=3)
-abline(h=seq(0,12,by=2),col="grey",lty=2)
-}
-
-}
-\keyword{datasets}
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