pca_wsaf.RdConduct principal components analysis (PCA) on a matrix of within-sample allele frequencies (WSAF). Missing values must have been already imputed. Output includes the raw components, the variance in the data explained by each component, and the loadings of each component also returned.
pca_wsaf(x)a matrix of within-sample allele frequencies, as produced by the
function get_wsaf().
Invisibly returns a list of class `prcomp` with the following components
"sdev" the standard deviations of the principal components (i.e., the square roots of the eigenvalues of the covariance/correlation matrix, though the calculation is actually done with the singular values of the data matrix).
"rotation" the
matrix of variable loadings (i.e., a matrix whose columns contain the
eigenvectors). The function princomp returns this in the element
loadings.
"center, scale" the centering and scaling used.
"x" the value of the rotated data (the centred data
multiplied by the rotation matrix). Hence, cov(x) is the
diagonal matrix diag(sdev^2).
"var" the variance in the data explained by each component.
"contribution" the percent contribution of a variable (i.e. a locus) to the overall variation.
Contributions of each variable are computed from the loading values
(stored as "rotation" within the prcomp object). The percent
contribution of a variable is defined as the absolute loading value for
this variable, divided by the sum of loadings over all variables and
multiplied by 100.