chemometrics (version 1.4.1)

prm_dcv: Repeated double-cross-validation for robust PLS

Description

Performs a careful evaluation by repeated double-CV for robust PLS, called PRM (partial robust M-estimation).

Usage

prm_dcv(X,Y,a=10,repl=10,segments0=4,segments=7,segment0.type="random", segment.type="random",sdfact=2,fairct=4,trim=0.2,opt="median",plot.opt=FALSE, ...)

Arguments

X

predictor matrix

Y

response variable

a

number of PLS components

repl

Number of replicattion for the double-CV

segments0

the number of segments to use for splitting into training and test data, or a list with segments (see mvrCv)

segments

the number of segments to use for selecting the optimal number if components, or a list with segments (see mvrCv)

segment0.type

the type of segments to use. Ignored if 'segments0' is a list

segment.type

the type of segments to use. Ignored if 'segments' is a list

sdfact

factor for the multiplication of the standard deviation for the determination of the optimal number of components, see mvr_dcv

fairct

tuning constant, by default fairct=4

trim

trimming percentage for the computation of the SEP

opt

if "l1m" the mean centering is done by the l1-median, otherwise if "median", by the coordinate-wise median

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

...

additional parameters

Value

Details

In this cross-validation (CV) scheme, the optimal number of components is determined by an additional CV in the training set, and applied to the test set. The procedure is repeated repl times. The optimal number of components is the model with the smallest number of components which is still in the range of the MSE+sdfact*sd(MSE), where MSE and sd are taken from the minimum.

References

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

See Also

mvr

Examples

data(NIR)
X <- NIR$xNIR[1:30,]      # first 30 observations - for illustration
y <- NIR$yGlcEtOH[1:30,1] # only variable Glucose
NIR.Glc <- data.frame(X=X, y=y)
res <- prm_dcv(X,y,a=3,repl=2)