Determination of the number of latent components to be used for classification with PLS and LDA
pls.lda.cv determines the best number of latent components to be used for
classification with PLS dimension reduction and linear discriminant analysis as described in
a (ntrain x p) data matrix containing the predictors for the training data set.
a vector of length ntrain giving the classes of the ntrain observations. The classes must be coded as 1,...,K (K>=2).
the vector of integers from which the best number of latent
components has to be chosen by cross-validation. If
the number of cross-validation iterations to be performed for the choice of the number of latent components.
the proportion of observations to be included in the training set at each cross-validation iteration.
The class priors to be used for linear discriminant analysis. If unspecified, the class proportions in the training set are used.
The cross-validation procedure described in Boulesteix (2004) is used to
determine the best number of latent components to be used for classification.
At each cross-validation run,
Xtrain is split into a pseudo training
set and a pseudo test set and the classification error rate is determined for each
number of latent components. Finally, the function
the number of latent components for which the mean classification rate over
nrun partitions is minimal.
The number of latent components to be used for classification.
A. L. Boulesteix (2004). PLS dimension reduction for classification with microarray data, Statistical Applications in Genetics and Molecular Biology 3, Issue 1, Article 33.
A. L. Boulesteix, K. Strimmer (2007). Partial least squares: a versatile tool for the analysis of high-dimensional genomic data. Briefings in Bioinformatics 7:32-44.
S. de Jong (1993). SIMPLS: an alternative approach to partial least squares regression, Chemometrics Intell. Lab. Syst. 18, 251–263.
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# load plsgenomics library library(plsgenomics) # load leukemia data data(leukemia) # Determine the best number of components to be used for classification using the # cross-validation procedure # choose the best number from 2,3,4 pls.lda.cv(Xtrain=leukemia$X,Ytrain=leukemia$Y,ncomp=2:4,nruncv=20) # choose the best number from 1,2,3 pls.lda.cv(Xtrain=leukemia$X,Ytrain=leukemia$Y,ncomp=3,nruncv=20)