getPCA: PCA using Jacobi algorithm or block algorithm.
In isglobal-brge/svdParallel: Parallel implementation of SVD

Description Usage Arguments Details Value Examples

Computation of the PCA of a matrix using different methods: Jacobi algorithm or block algorithm.

1 2	getPCA(x, center = TRUE, scale = TRUE, method = "blockSVD", tol = .Machine$double.eps, ...)

`x`	a real nxp matrix
`center`	a logical value indicating whether the variables should be shifted to be zero centered
`scale`	a logical value indicating whether the variables should be scaled to have unit variance
`method`	selects the method with which the function will compute the SVD. Can be: blockSVD, generalBlockSVD, Jacobi and JacobiR.
`tol`	a small positive error tolerance. Default is machine tolerance.
`...`	other parameters to be passed from the function of the method used.

Principal components and variance explained for each component using two-sided Jacobi algorithm or block algorithm.

a list with the coordinates of the variables (var.coord) and the idividuals (Y), the variance of each component and its percentage and the matrix of the principal components.