PLS2 by NIPALS
NIPALS algorithm for PLS2 regression (y is multivariate)
pls2_nipals(X, Y, a, it = 50, tol = 1e-08, scale = FALSE)
X |
original X data matrix |
Y |
original Y-data matrix |
a |
number of PLS components |
it |
number of iterations |
tol |
tolerance for convergence |
scale |
if TRUE the X and y data will be scaled in addition to centering, if FALSE only mean centering is performed |
The NIPALS algorithm is the originally proposed algorithm for PLS. Here, the Y-data matrix is multivariate.
P |
matrix with loadings for X |
T |
matrix with scores for X |
Q |
matrix with loadings for Y |
U |
matrix with scores for Y |
D |
D-matrix within the algorithm |
W |
weights for X |
C |
weights for Y |
B |
final regression coefficients |
Peter Filzmoser <P.Filzmoser@tuwien.ac.at>
K. Varmuza and P. Filzmoser: Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press, Boca Raton, FL, 2009.
data(cereal) res <- pls2_nipals(cereal$X,cereal$Y,a=5)
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