pls.lda.cv {plsgenomics} | R Documentation

## 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 (http://www.statistik.lmu.de/~socher/)

### 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.

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

    # 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)

[Package plsgenomics version 1.2 Index]