spcaIllus | R Documentation |
Datasets illustrating the spatial Principal Component Analysis (Jombart et
al. 2009). These data were simulated using various models using Easypop
(2.0.1). Spatial coordinates were defined so that different spatial
patterns existed in the data. The spca-illus
is a list containing the
following genind or genpop objects:
- dat2A:
2 patches
- dat2B: cline between two pop
- dat2C: repulsion among
individuals from the same gene pool
- dat3: cline and repulsion
-
dat4: patches and local alternance
spcaIllus
is list of 5 components being either genind or
genpop objects.
See "source" for a reference providing simulation details.
Thibaut Jombart t.jombart@imperial.ac.uk
Jombart, T., Devillard, S., Dufour, A.-B. and Pontier, D. Revealing cryptic spatial patterns in genetic variability by a new multivariate method. Heredity, 101, 92–103.
Jombart, T., Devillard, S., Dufour, A.-B. and Pontier, D. Revealing cryptic spatial patterns in genetic variability by a new multivariate method. Heredity, 101, 92–103.
Balloux F (2001) Easypop (version 1.7): a computer program for oppulation genetics simulations Journal of Heredity, 92: 301-302
spca
required_packages <- require(adespatial) && require(spdep) if (required_packages) { data(spcaIllus) attach(spcaIllus) opar <- par(no.readonly=TRUE) ## comparison PCA vs sPCA # PCA pca2A <- dudi.pca(dat2A$tab,center=TRUE,scale=FALSE,scannf=FALSE) pca2B <- dudi.pca(dat2B$tab,center=TRUE,scale=FALSE,scannf=FALSE) pca2C <- dudi.pca(dat2C$tab,center=TRUE,scale=FALSE,scannf=FALSE) pca3 <- dudi.pca(dat3$tab,center=TRUE,scale=FALSE,scannf=FALSE,nf=2) pca4 <- dudi.pca(dat4$tab,center=TRUE,scale=FALSE,scannf=FALSE,nf=2) # sPCA spca2A <-spca(dat2A,xy=dat2A$other$xy,ask=FALSE,type=1, plot=FALSE,scannf=FALSE,nfposi=1,nfnega=0) spca2B <- spca(dat2B,xy=dat2B$other$xy,ask=FALSE,type=1, plot=FALSE,scannf=FALSE,nfposi=1,nfnega=0) spca2C <- spca(dat2C,xy=dat2C$other$xy,ask=FALSE, type=1,plot=FALSE,scannf=FALSE,nfposi=0,nfnega=1) spca3 <- spca(dat3,xy=dat3$other$xy,ask=FALSE, type=1,plot=FALSE,scannf=FALSE,nfposi=1,nfnega=1) spca4 <- spca(dat4,xy=dat4$other$xy,ask=FALSE, type=1,plot=FALSE,scannf=FALSE,nfposi=1,nfnega=1) # an auxiliary function for graphics plotaux <- function(x,analysis,axis=1,lab=NULL,...){ neig <- NULL if(inherits(analysis,"spca")) neig <- nb2neig(analysis$lw$neighbours) xrange <- range(x$other$xy[,1]) xlim <- xrange + c(-diff(xrange)*.1 , diff(xrange)*.45) yrange <- range(x$other$xy[,2]) ylim <- yrange + c(-diff(yrange)*.45 , diff(yrange)*.1) s.value(x$other$xy,analysis$li[,axis],include.ori=FALSE,addaxes=FALSE, cgrid=0,grid=FALSE,neig=neig,cleg=0,xlim=xlim,ylim=ylim,...) par(mar=rep(.1,4)) if(is.null(lab)) lab = gsub("[P]","",x$pop) text(x$other$xy, lab=lab, col="blue", cex=1.2, font=2) add.scatter({barplot(analysis$eig,col="grey");box(); title("Eigenvalues",line=-1)},posi="bottomright",ratio=.3) } # plots plotaux(dat2A,pca2A,sub="dat2A - PCA",pos="bottomleft",csub=2) plotaux(dat2A,spca2A,sub="dat2A - sPCA glob1",pos="bottomleft",csub=2) plotaux(dat2B,pca2B,sub="dat2B - PCA",pos="bottomleft",csub=2) plotaux(dat2B,spca2B,sub="dat2B - sPCA glob1",pos="bottomleft",csub=2) plotaux(dat2C,pca2C,sub="dat2C - PCA",pos="bottomleft",csub=2) plotaux(dat2C,spca2C,sub="dat2C - sPCA loc1",pos="bottomleft",csub=2,axis=2) par(mfrow=c(2,2)) plotaux(dat3,pca3,sub="dat3 - PCA axis1",pos="bottomleft",csub=2) plotaux(dat3,spca3,sub="dat3 - sPCA glob1",pos="bottomleft",csub=2) plotaux(dat3,pca3,sub="dat3 - PCA axis2",pos="bottomleft",csub=2,axis=2) plotaux(dat3,spca3,sub="dat3 - sPCA loc1",pos="bottomleft",csub=2,axis=2) plotaux(dat4,pca4,lab=dat4$other$sup.pop,sub="dat4 - PCA axis1", pos="bottomleft",csub=2) plotaux(dat4,spca4,lab=dat4$other$sup.pop,sub="dat4 - sPCA glob1", pos="bottomleft",csub=2) plotaux(dat4,pca4,lab=dat4$other$sup.pop,sub="dat4 - PCA axis2", pos="bottomleft",csub=2,axis=2) plotaux(dat4,spca4,lab=dat4$other$sup.pop,sub="dat4 - sPCA loc1", pos="bottomleft",csub=2,axis=2) # color plot par(opar) colorplot(spca3, cex=4, main="colorplot sPCA dat3") text(spca3$xy[,1], spca3$xy[,2], dat3$pop) colorplot(spca4, cex=4, main="colorplot sPCA dat4") text(spca4$xy[,1], spca4$xy[,2], dat4$other$sup.pop) # detach data detach(spcaIllus) }
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