prm: Robust PLS

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/prm.R

Description

Robust PLS by partial robust M-regression.

Usage

1
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 <[email protected]>

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

1
2
data(PAC)
res <- prm(PAC$X,PAC$y,a=5)

chemometrics documentation built on May 29, 2017, 10:42 a.m.