pls.lda.cv {plsgenomics}

Determination of the number of latent components to be used for classification with PLS and LDA

Description

The function 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 Boulesteix (2004).

Usage

pls.lda.cv(Xtrain, Ytrain,  ncomp, nruncv=20, alpha=2/3, priors=NULL)

Arguments

Xtrain

a (ntrain x p) data matrix containing the predictors for the training data set. Xtrain may be a matrix or a data frame. Each row is an observation and each column is a predictor variable.

Ytrain

a vector of length ntrain giving the classes of the ntrain observations. The classes must be coded as 1,...,K (K>=2).

ncomp

the vector of integers from which the best number of latent components has to be chosen by cross-validation. If ncomp is of length 1, the best number of components is chosen from 1,...,ncomp.

nruncv

the number of cross-validation iterations to be performed for the choice of the number of latent components.

alpha

the proportion of observations to be included in the training set at each cross-validation iteration.

priors

The class priors to be used for linear discriminant analysis. If unspecified, the class proportions in the training set are used.

Details

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 pls.lda.cv returns the number of latent components for which the mean classification rate over the nrun partitions is minimal.

Value

The number of latent components to be used for classification.

Author(s)

Anne-Laure Boulesteix (https://www.ibe.med.uni-muenchen.de/mitarbeiter/professoren/boulesteix/index.html)

References

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.

See Also

pls.lda, pls.regression.cv.

Examples

## Not run: 
## between 5~15 seconds
# 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)


## End(Not run)