PLS1 by NIPALS
NIPALS algorithm for PLS1 regression (y is univariate)
pls1_nipals(X, y, a, it = 50, tol = 1e-08, scale = FALSE)
X |
original X data matrix |
y |
original y-data |
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 are only allowed to be univariate. This simplifies the algorithm.
P |
matrix with loadings for X |
T |
matrix with scores for X |
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(PAC) res <- pls1_nipals(PAC$X,PAC$y,a=5)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.