R: Simulated data illustrating the sPCA

spcaIllus {adegenet} | R Documentation

## Simulated data illustrating the sPCA

### Description

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

### Format

`spcaIllus` is list of 5 components being either genind or genpop objects.

### Details

See "source" for a reference providing simulation details.

### Author(s)

Thibaut Jombart t.jombart@imperial.ac.uk

### Source

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.

### References

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

### See Also

`spca`

### Examples

    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)

[Package adegenet version 2.1.4 Index]