prm: Robust PLS

In chemometrics: Multivariate Statistical Analysis in Chemometrics

Robust PLS

Description

Robust PLS by partial robust M-regression.

Usage

prm(X, y, a, fairct = 4, opt = "l1m",usesvd=FALSE)

Arguments

X	predictor matrix
y	response variable
a	number of PLS components
fairct	tuning constant, by default fairct=4
opt	if "l1m" the mean centering is done by the l1-median, otherwise if "median" the coordinate-wise median is taken
usesvd	if TRUE, SVD will be used if X has more columns than rows

Details

M-regression is used to robustify PLS, with initial weights based on the FAIR weight function.

Value

coef	vector with regression coefficients
intercept	coefficient for intercept
wy	vector of length(y) with residual weights
wt	vector of length(y) with weights for leverage
w	overall weights
scores	matrix with PLS X-scores
loadings	matrix with PLS X-loadings
fitted.values	vector with fitted y-values
mx	column means of X
my	mean of y

Author(s)

Peter Filzmoser <[e-mail]>

References

S. Serneels, C. Croux, P. Filzmoser, and P.J. Van Espen. Partial robust M-regression. Chemometrics and Intelligent Laboratory Systems, Vol. 79(1-2), pp. 55-64, 2005.

See Also

mvr

Examples

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

chemometrics documentation built on Aug. 25, 2023, 5:18 p.m.