cv.lspcr.glm: Cross-validation for LS-PCR model for logistic regression
In lsplsGlm: Classification using LS-PLS for Logistic Regression

Description Usage Arguments Details Value Author(s) See Also Examples

Finds the optimal number of component for LS-PCR model for logistic regression.

1	cv.lspcr.glm(Y, X, D, ncompmax, folds = 5, proportion = 0.9)

`Y`	a vector of length `n` giving the classes of the `n` observations. The classes must be coded as 1 or 0.
`X`	a data matrix (`nxp`) of genes. NAs and Inf are not allowed. Each row corresponds to an observation and each column to a gene.
`D`	a data matrix (`nxq`) of clinical data. NAs and Inf are not allowed. Each row corresponds to an observation and each column to a clinical variable.
`ncompmax`	a positive integer. `ncompmax` is the maximal number of selected components.
`folds`	a positive integer indicating the number of folds in K-folds cross-validation procedure.
`proportion`	proportion of the dataset in the learning sample. `proportion` has to be between 0 and 1.

This function finds the optimal number of component for a LS-PCR model. At each cross validation run, X, D and Y are split into one training set and one test set (of proportion proportion and 1-proportion). Then the classification error rate is computed for each value of ncomp between 1 and ncompmax. At the end we choose the number of component for which the classification error rate is minimal. This function returns also p.cvg. It's a vector of size ncompmax which contains convergence proportion of the logistic regression for each number of component between 1 and ncompmax.

`ncompopt`	the optimal number of component.
`p.cvg`	convergence proportion of the logistic regression.

Caroline Bazzoli, Thomas Bouleau, Sophie Lambert-Lacroix

fit.lspcr.glm.

#data
data(BreastCancer)
#vector of responses
Y<-BreastCancer$Y
#Genetic data
X<-BreastCancer$X
#Clinical data
D<-BreastCancer$D
#SIS selection
X<-scale(X)

X<-SIS.selection(X=X,Y=Y,pred=50)
#cross validation to find the optimal number of component
cv<-cv.lspcr.glm(Y=Y,X=X,D=D,folds=5,ncompmax=5,proportion=0.9)
ncompopt<-cv$ncompopt