pls1_nipals {chemometrics}

PLS1 by NIPALS

Description

NIPALS algorithm for PLS1 regression (y is univariate)

Usage

pls1_nipals(X, y, a, it = 50, tol = 1e-08, scale = FALSE)

Arguments

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

Details

The NIPALS algorithm is the originally proposed algorithm for PLS. Here, the y-data are only allowed to be univariate. This simplifies the algorithm.

Value

P

matrix with loadings for X

T

matrix with scores for X

W

weights for X

C

weights for Y

b

final regression coefficients

Author(s)

Peter Filzmoser <[e-mail]>

References

K. Varmuza and P. Filzmoser: Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press, Boca Raton, FL, 2009.

See Also

mvr, pls2_nipals

Examples

data(PAC)
res <- pls1_nipals(PAC$X,PAC$y,a=5)