qrpca: QR based PCA
In RafaelSdeSouza/qrprcomp: QR based PCA

qrpca

R Documentation

QR based PCA

Performs a QR based principal component analysis, The result is returned as an object of class prcomp.

qrpca(x, center = TRUE, scale = FALSE, cuda = FALSE)

`x`	a numeric matrix or data frame which provides the data for the principal component analysis.
`center`	a logical value indicating whether the empirical mean of (the columns) of `x` should be subtracted.
`scale`	a logical value indicating whether the columns of `x` should be scaled to have unit variance before the analysis takes place. The default is `FALSE` for consistency with `prcomp`. @param cuda a logical value indicating whether cuda acceleration should be used. The default is `FALSE`.

qrpca computes a principal component (PC) using QR decomposition as intermediate step. Matrix operations mostly uses torch for performance.

qrpca returns a list with class prcomp containing the following elements:

`sdev`	the additional standard deviation explained by each component.
`rotation`	the matrix of loadings, containing the principal axes as columns.
`x`	the scores matrix containing the principal components as columns.
`center, scale`	the centering and scaling used, or `FALSE`