quantilecldiff: A function to apply the quantile classifier that uses a...

In quantileDA: Quantile Classifier

Description

A function to apply the quantile classifier that uses a different optimal quantile probability for each variable

Usage

quantilecldiff(train, test, cl, theta = NULL, cl.test = NULL)

Arguments

train	A matrix of data (the training set) with observations in rows and variables in columns. It can be a matrix or a dataframe.
test	A matrix of data (the test 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 of the training set. It can be factor or numerical.
theta	A vector of quantile probabilities (optional)
cl.test	If available, a vector of class labels for each sample of the test set (optional)

Details

quantilecldiff carries out the quantile classifier by using a different optimal quantile probability for each variable selected in the training set.

Value

A list with components

thetas	The vector of quantile probabilities
theta.choice	The mean of optimal quantile probabilities
me.train	Misclassification error for the best quantile probability in the training set
me.test	Misclassification error for the best quantile probability in the test set (only if cl.test is available)
cl.train	Predicted classification in the training set
cl.test	Predicted classification in the test set

Author(s)

Christian Hennig, Cinzia Viroli

See Also

See Also quantilecl

Examples

data(ais)
x=ais[,3:13]
cl=as.double(ais[,1])
set.seed(22)
index=sample(1:202,152,replace=FALSE)
train=x[index,]
test=x[-index,]
cl.train=cl[index]
cl.test=cl[-index]
out.q=quantilecldiff(train,test,cl.train,cl.test=cl.test)
out.q$me.test
out.q$theta.choice

Example output

[1] 0.06
[1] 0.4045455

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