scaleGen | R Documentation |
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) ## 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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.