pls.regression.cv {plsgenomics} | R Documentation

## Determination of the number of latent components to be used in PLS regression

### Description

The function `pls.regression.cv` determines the best number of latent components to be used for PLS regression using the cross-validation approach described in Boulesteix and Strimmer (2005).

### Usage

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

### 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 (ntrain x m) data matrix of responses. `Ytrain` may be a vector (if m=1), a matrix or a data frame. If `Ytrain` is a matrix or a data frame, each row is an observation and each column is a response variable. If `Ytrain` is a vector, it contains the unique response variable for each observation.
`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.

### Details

The cross-validation procedure described in Boulesteix and Strimmer (2005) 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 squared error is determined for each number of latent components. Finally, the function `pls.regression.cv` returns the number of latent components for which the mean squared error over the `nrun` partitions is minimal.

### Value

The number of latent components to be used in PLS regression, as determined by cross-validation.

### Author(s)

Anne-Laure Boulesteix (http://www.statistik.lmu.de/~socher/) and Korbinian Strimmer (http://www.statistik.lmu.de/~strimmer/).

### References

A. L. Boulesteix and K. Strimmer (2005). Predicting Transcription Factor Activities from Combined Analysis of Microarray and ChIP Data: A Partial Least Squares Approach.

S. de Jong (1993). SIMPLS: an alternative approach to partial least squares regression, Chemometrics Intell. Lab. Syst. 18, 251–263.

### See Also

`pls.regression`, `TFA.estimate`, `pls.lda.cv`.

### Examples

    # load plsgenomics library
    library(plsgenomics)

    # load Ecoli data
    data(Ecoli)

    # determine the best number of components for PLS regression using the cross-validation approach
    # choose the best number from 1,2,3,4
    pls.regression.cv(Xtrain=Ecoli$CONNECdata,Ytrain=Ecoli$GEdata,ncomp=4,nruncv=20)
    # choose the best number from 2,3
    pls.regression.cv(Xtrain=Ecoli$CONNECdata,Ytrain=Ecoli$GEdata,ncomp=c(2,3),nruncv=20)

[Package plsgenomics version 1.2 Index]