quantileCV: A function to cross-validate the quantile classifier

In quantileDA: Quantile Classifier

Description

Balanced cross-validation for the quantile classifier

Usage

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

Arguments

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.

Details

quantileCV carries out cross-validation for a quantile classifier.

Value

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

Author(s)

Christian Hennig, Cinzia Viroli

Examples

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

quantileDA documentation built on May 2, 2019, 5:54 a.m.