Calculates principal component space using non-linear iterative partial least squares algorithm (NIPALS)
pca.nipals(x, ncomp = min(ncol(x), nrow(x) - 1), tol = 10^-10)
a list with scores, loadings and eigenvalues for the components
a matrix with data values (preprocessed)
number of components to calculate
tolerance (if difference in eigenvalues is smaller - convergence achieved)
Geladi, Paul; Kowalski, Bruce (1986), "Partial Least Squares Regression:A Tutorial", Analytica Chimica Acta 185: 1-17