Repeated doublecrossvalidation for robust PLS
Description
Performs a careful evaluation by repeated doubleCV for robust PLS, called PRM (partial robust Mestimation).
Usage
1 2 
Arguments
X 
predictor matrix 
Y 
response variable 
a 
number of PLS components 
repl 
Number of replicattion for the doubleCV 
segments0 
the number of segments to use for splitting into training and
test data, or a list with segments (see 
segments 
the number of segments to use for selecting the optimal number if
components, or a list with segments (see 
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

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 l1median, otherwise if "median", by the coordinatewise 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 crossvalidation (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 <P.Filzmoser@tuwien.ac.at>
References
K. Varmuza and P. Filzmoser: Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press, Boca Raton, FL, 2009.
See Also
mvr
Examples
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