R: A function to cross-validate the quantile classifier

quantileCV {quantileDA}

R Documentation

A function to cross-validate the quantile classifier

Balanced cross-validation for the quantile classifier

quantileCV(x, cl, nfold = min(table(cl)), 
folds = balanced.folds(cl, nfold), theta=NULL, seed = 1, varying = FALSE)

`x`	A matrix of data (the training set) with observations in rows and variables in columns (it can be a matrix or a dataframe)
`cl`	A vector of class labels for each sample (factor or numerical)
`nfold`	Number of cross-validation folds. Default is the smallest class size. Admitted values are from 1 to the smallest class size as maximum fold number.
`folds`	A list with nfold components, each component a vector of indices of the samples in that fold. By default a (random) balanced cross-validation is used
`theta`	A vector of quantile probabilities (optional)
`seed`	Fix the seed of the running. Default is 1
`varying`	If TRUE a different quantile for each variable is selected in the training set. If FALSE (default) an unique quantile is used.

quantileCV carries out cross-validation for a quantile classifier.

A list with components

`test.rates`	Mean of misclassification errors in the cross-validation test sets for each quantile probability (available if `varying` is FALSE)
`train.rates`	Mean of misclassification errors in the cross-validation train sets for each quantile probability (available if `varying` is FALSE)
`thetas`	The fitted quantile probabilities
`theta.choice`	Value of the chosen quantile probability in the training set
`me.test`	Misclassification errors in the cross validation test sets for the best quantile probability
`me.train`	Misclassification errors in the cross validation training sets for the best quantile probability
`me.median`	Misclassification errors in the cross validation test sets of the median classifier
`me.centroid`	Misclassification errors in the cross validation test sets of the centroid classifier
`folds`	The cross-validation folds used

Christian Hennig, Cinzia Viroli

data(ais)
x=ais[,3:13]
cl=as.double(ais[,1])
out=quantileCV(x,cl,nfold=2)

[Package quantileDA version 1.2 Index]