prm_cv: Cross-validation for robust PLS

In chemometrics: Multivariate Statistical Analysis in Chemometrics

Cross-validation for robust PLS

Description

Cross-validation (CV) is carried out with robust PLS based on partial robust M-regression. A plot with the choice for the optimal number of components is generated. This only works for univariate y-data.

Usage

prm_cv(X, y, a, fairct = 4, opt = "median", subset = NULL, segments = 10, 
segment.type = "random", trim = 0.2, sdfact = 2, plot.opt = TRUE)

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
subset	optional vector defining a subset of objects
segments	the number of segments to use or a list with segments (see mvrCv)
segment.type	the type of segments to use. Ignored if 'segments' is a list
trim	trimming percentage for the computation of the SEP
sdfact	factor for the multiplication of the standard deviation for the determination of the optimal number of components, see mvr_dcv
plot.opt	if TRUE a plot will be generated that shows the selection of the optimal number of components for each step of the CV, see mvr_dcv

Details

A function for robust PLS based on partial robust M-regression is available at prm. The optimal number of robust PLS components is chosen according to the following criterion: Within the CV scheme, the mean of the trimmed SEPs SEPtrimave is computed for each number of components, as well as their standard errors SEPtrimse. Then one searches for the minimum of the SEPtrimave values and adds sdfact*SEPtrimse. The optimal number of components is the most parsimonious model that is below this bound.

Value

predicted	matrix with length(y) rows and a columns with predicted values
SEPall	vector of length a with SEP values for each number of components
SEPtrim	vector of length a with trimmed SEP values for each number of components
SEPj	matrix with segments rows and a columns with SEP values within the CV for each number of components
SEPtrimj	matrix with segments rows and a columns with trimmed SEP values within the CV for each number of components
optcomp	final optimal number of PLS components
SEPopt	trimmed SEP value for final optimal number of PLS components

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

prm

Examples

data(cereal)
set.seed(123)
res <- prm_cv(cereal$X,cereal$Y[,1],a=5,segments=4,plot.opt=TRUE)

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