scaleGen: Compute scaled allele frequencies
In adegenet: Exploratory Analysis of Genetic and Genomic Data

Description Usage Arguments Details Value Author(s) Examples

The generic function scaleGen is an analogue to the scale function, but is designed with further arguments giving scaling options.

scaleGen(x, ...)

## S4 method for signature 'genind'
scaleGen(x, center = TRUE, scale = TRUE,
  NA.method = c("asis", "mean", "zero"), truenames = TRUE)

## S4 method for signature 'genpop'
scaleGen(x, center = TRUE, scale = TRUE,
  NA.method = c("asis", "mean", "zero"), truenames = TRUE)

`x`	a genind and genpop object
`...`	further arguments passed to other methods.
`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.
`NA.method`	a method to replace NA; asis: leave NAs as is; mean: replace by the mean allele frequencies; zero: replace by zero
`truenames`	no longer used; kept for backward compatibility

Methods are defined for genind and genpop objects. Both return data.frames of scaled allele frequencies.

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

Thibaut Jombart t.jombart@imperial.ac.uk

## Not run: 
## load data
data(microbov)
obj <- genind2genpop(microbov)

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

## 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$eig,main="Eigenvalues\n no scaling")
barplot(pcaX1$eig,main="Eigenvalues\n usual scaling")
barplot(pcaX2$eig,main="Eigenvalues\n 'binomial' scaling")
# differences between loadings of alleles
s.match(pro1$scor1,pro1$scor2,clab=0,sub="usual -> binom (procustean rotation)")


## End(Not run)