# Compute scaled allele frequencies The generic function `scaleGen` is an analogue to the `scale` function, but is designed with further arguments giving scaling options. methods ```r 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 ) ``` ## 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 ## Returns A matrix of scaled allele frequencies with genotypes (genind ) or populations in (genpop ) in rows and alleles in columns. ## Details Methods are defined for genind and genpop objects. Both return data.frames of scaled allele frequencies. ## Examples ```r ## Not run: ## 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") ## End(Not run) ``` ## Author(s) Thibaut Jombart t.jombart@imperial.ac.uk

scaleGen function