prm_dcv: Repeated double-cross-validation for robust PLS

In chemometrics: Multivariate Statistical Analysis in Chemometrics

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

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.

Value

b	estimated regression coefficients
intercept	estimated regression intercept
resopt	array [nrow(Y) x ncol(Y) x repl] with residuals using optimum number of components
predopt	array [nrow(Y) x ncol(Y) x repl] with predicted Y using optimum number of components
optcomp	matrix [segments0 x repl] optimum number of components for each training set
residcomp	array [nrow(Y) x ncomp x repl] with residuals using optimum number of components
pred	array [nrow(Y) x ncol(Y) x ncomp x repl] with predicted Y for all numbers of components
SEPall	matrix [ncomp x repl] with SEP values
SEPtrim	matrix [ncomp x repl] with trimmed SEP values
SEPcomp	vector of length ncomp with trimmed SEP values; use the element afinal for the optimal trimmed SEP
afinal	final optimal number of components
SEPopt	trimmed SEP over all residuals using optimal number of 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

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)

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