prm {chemometrics}R Documentation

Robust PLS

Description

Robust PLS by partial robust M-regression.

Usage

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

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 by the coordinate-wise median

Details

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

Value

coef vector with regression coefficients
wy vector of length(y) with residual weights
wt vector of length(y) with weights for leverage
scores matrix with PLS X-scores
loadings matrix with PLS X-loadings
fitted.values vector with fitted y-values

Author(s)

Peter Filzmoser <P.Filzmoser@tuwien.ac.at>

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)

[Package chemometrics version 0.4 Index]