gsim.cv {plsgenomics} | R Documentation

## Determination of the ridge regularization parameter and the bandwidth to be used for classification with GSIM for binary data

### Description

The function `gsim.cv` determines the best ridge regularization parameter and bandwidth to be used for classification with GSIM as described in Lambert-Lacroix and Peyre (2005).

### Usage

    gsim.cv(Xtrain, Ytrain,LambdaRange,hARange,hB=NULL, NbIterMax=50)

### Arguments

`Xtrain` | a (ntrain x p) data matrix of predictors. `Xtrain` must be a matrix. Each row corresponds to an observation and each column to a predictor variable.
`Ytrain` | a ntrain vector of responses. `Ytrain` must be a vector. `Ytrain` is a {1,2}-valued vector and contains the response variable for each observation.
`LambdaRange` | the vector of positive real value from which the best ridge regularization parameter has to be chosen by cross-validation.
`hARange` | the vector of strictly positive real value from which the best bandwidth has to be chosen by cross-validation for GSIM step A.
`hB` | a strictly positive real value. `hB` is the bandwidth for GSIM step B. if `hB` is equal to NULL, then hB value is chosen using a plug-in method.
`NbIterMax` | a positive integer. `NbIterMax` is the maximal number of iterations in the Newton-Rapson parts.

### Details

The cross-validation procedure described in Lambert-Lacroix and Peyre (2005) is used to determine the best ridge regularization parameter and bandwidth to be used for classification with GSIM for binary data (for categorical data see `mgsim` and `mgsim.cv`). At each cross-validation run, `Xtrain` is split into a pseudo training set (ntrain - 1 samples) and a pseudo test set (1 sample) and the classification error rate is determined for each value of ridge regularization parameter and bandwidth. Finally, the function `gsim.cv` returns the values of the ridge regularization parameter and bandwidth for which the mean classification error rate is minimal.

### Value

A list with the following components:

`Lambda` | the optimal regularization parameter.
`hA` | the optimal bandwidth parameter.

### Author(s)

Sophie Lambert-Lacroix (http://www-lmc.imag.fr/lmc-sms/Sophie.Lambert) and Julie Peyre (http://www-lmc.imag.fr/lmc-sms/Julie.Peyre/).

### References

S. Lambert-Lacroix and Julie Peyre (2005). Local likelyhood regression in generalized linear single-index models with applications to microarray data, TR0563 IAP Statistics network (http://www.stat.ucl.ac.be/IAP/).

### See Also

`mgsim`, `gsim`, `gsim.cv`.

### Examples

    # load plsgenomics library
    library(plsgenomics)

    # load Colon data
    data(Colon)
    IndexLearn <- c(sample(which(Colon$Y==2),12),sample(which(Colon$Y==1),8))

    Xtrain <- Colon$X[IndexLearn,]
    Ytrain <- Colon$Y[IndexLearn]
    Xtest <- Colon$X[-IndexLearn,]

    # preprocess data
    resP <- preprocess(Xtrain= Xtrain, Xtest=Xtest,Threshold = c(100,16000),Filtering=c(5,500),log10.scale=TRUE,row.stand=TRUE)

    # Determine optimum h and lambda
    hl <- gsim.cv(Xtrain=resP$pXtrain,Ytrain=Ytrain,hARange=c(7,20),LambdaRange=c(0.1,1),hB=NULL)

    # perform prediction by GSIM
    res <- gsim(Xtrain=resP$pXtrain,Ytrain=Ytrain,Xtest=resP$pXtest,Lambda=hl$Lambda,hA=hl$hA,hB=NULL)
    res$Cvg
    sum(res$Ytest!=Colon$Y[-IndexLearn])

[Package plsgenomics version 1.2 Index]