R/gx.rotate.R

In rgr: Applied Geochemistry EDA

gx.rotate <-
function(save, nrot = 2)
{
     # Function to rotate the matrix of R-mode PCA scores generated by
     # function gx.mva or gx.robmva using the Kaiser Varimax procedure. 
     # The results are appended to the input object saved from gx.mva or
     # gx.robmva so that the rotations may be plotted with gx.rqpca.plot.  
     # Rotated scores, vscore = Z.A.inverse(A'A), where Z are the (0,1)
     # standardized data (SNDs) and A are the p x nrot Varimax loadings.
     #
     if(nrot > save$p) nrot <- save$p
     amat <- matrix(nrow = save$n, ncol = nrot)
     amat <- save$rload[, 1:nrot]
     kaiser <- varimax(amat, normalize = FALSE)
     vscore <- save$snd %*% kaiser$loadings %*% solve(t(kaiser$loadings)
         %*% kaiser$loadings)
     vcontrib <- numeric(nrot)
     for (j in 1:nrot) vcontrib[j] <- var(vscore[, j])
     cat("  Score variances:", signif(vcontrib, 4), "\n")
     invisible(list(main = save$main, input = save$input, proc = save$proc,
         n = save$n, nc = save$nc, p = save$p, matnames = save$matnames,
         wts = save$wts, mean = save$mean, cov = save$cov, sd = save$sd,
         snd = save$snd, r = save$r, eigenvalues = save$eigenvalues,
         econtrib = save$econtrib, eigenvectors = save$eigenvectors,
         rload = save$rload, rcr = save$rcr, rqscore = save$rqscore,
         md = save$md, ppm = save$ppm, epm = save$epm, nr = nrot,
         vload = kaiser$loadings, vscore = vscore))
}