adegenet (version 1.2-7)

scaleGen-methods: Compute scaled allele frequencies

Description

The generic function scaleGen is an analogue to the scale function, but is designed with further arguments giving scaling options. Methods are defined for genind and genpop objects. Both return data.frames of scaled allele frequencies.

Usage

## S3 method for class 'genind':
scaleGen(x, center=TRUE, scale=TRUE, method=c("sigma", "binom"), missing=c("NA","0","mean"),truenames=TRUE)
## S3 method for class 'genpop':
scaleGen(x, center=TRUE, scale=TRUE, method=c("sigma", "binom"), missing=c("NA","0","mean"),truenames=TRUE)

Arguments

x

a genind and genpop object

center

a logical stating whether alleles frequencies should be centred to mean zero (default to TRUE). Alternatively, a vector of numeric values, one per allele, can be supplied: these values will be substracted from the allele frequencies.

scale

a logical stating whether alleles frequencies should be scaled (default to TRUE). Alternatively, a vector of numeric values, one per allele, can be supplied: these values will be substracted from the allele frequencies.

method

a character indicating the method to be used. See details.

truenames

a logical indicating whether true labels (as opposed to generic labels) should be used to name the output.

missing

a character giving the treatment for missing values. Can be "NA", "0" or "mean"

Value

• A matrix of scaled allele frequencies with genotypes (genind) or populations in (genpop) in rows and alleles in columns.

Details

The argument method is used as follows: - sigma: scaling is made using the usual standard deviation - binom: scaling is made using the theoretical variance of the allele frequency. This can be used to avoid that frequencies close to 0.5 have a stronger variance that those close to 0 or 1.

Examples

## load data
data(microbov)
obj <- genind2genpop(microbov)

## compare different scaling
X1 <- scaleGen(obj)
X2 <- scaleGen(obj,met="bin")

if(require(ade4)){
## compute PCAs
pcaObj <- dudi.pca(obj,scale=FALSE,scannf=FALSE) # pca with no scaling
pcaX1 <- dudi.pca(X1,scale=FALSE,scannf=FALSE,nf=100) # pca with usual scaling
pcaX2 <- dudi.pca(X2,scale=FALSE,scannf=FALSE,nf=100) # pca with scaling for binomial variance

## get the loadings of alleles for the two scalings
U1 <- pcaX1$c1
U2 <- pcaX2$c1


## find an optimal plane to compare loadings
## use a procustean rotation of loadings tables
pro1 <- procuste(U1,U2,nf=2)

## graphics
par(mfrow=c(2,2))
# eigenvalues
barplot(pcaObj$eig,main="Eigenvalues
 no scaling")
barplot(pcaX1$eig,main="Eigenvalues
 usual scaling")
barplot(pcaX2$eig,main="Eigenvalues
 'binomial' scaling")
# differences between loadings of alleles
s.match(pro1$scor1,pro1$scor2,clab=0,sub="usual -> binom (procustean rotation)")
}