adegenet (version 2.1.10)

scaleGen: 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.

Usage

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

Value

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

Arguments

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

Author

Thibaut Jombart

Details

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

Examples

if (FALSE) {
## load data
data(microbov)
obj <- genind2genpop(microbov)

## apply scaling
X1 <- scaleGen(obj)

## compute PCAs with and without scaling
pcaObj <- dudi.pca(obj, scale = FALSE, scannf = FALSE) # pca with no scaling
pcaX1  <- dudi.pca(X1, scale = FALSE, scannf = FALSE, nf = 100) # pca scaled using scaleGen()
pcaX2  <- dudi.pca(obj, scale = TRUE, scannf = FALSE, nf = 100) # pca scaled in-PCA

## get the loadings of alleles for the two scalings
U1 <- pcaObj$c1
U2 <- pcaX1$c1
U3 <- pcaX2$c1

## find an optimal plane to compare loadings
## use a procustean rotation of loadings tables
pro1 <- procuste(U1, U2, nf = 2)
pro2 <- procuste(U2, U3, nf = 2)
pro3 <- procuste(U1, U3, nf = 2)

## graphics
par(mfrow=c(2, 3))
# eigenvalues
barplot(pcaObj$eig, main = "Eigenvalues\n no scaling")
barplot(pcaX1$eig, main = "Eigenvalues\n scaleGen scaling")
barplot(pcaX2$eig, main = "Eigenvalues\n in-PCA scaling")
# differences between loadings of alleles
s.match(pro1$scorX, pro1$scorY, clab = 0,
        sub = "no scaling -> scaling (procustean rotation)")
s.match(pro2$scorX, pro2$scorY, clab = 0,
        sub = "scaling scaleGen -> in-PCA scaling")
s.match(pro3$scorX, pro3$scorY, clab = 0,
        sub = "no scaling -> in-PCA scaling")

}