mclust (version 2.0-1)

cv1EMtrain: Select discriminant models using cross validation

Description

For the ten available discriminant models the leave-one-out cross validation error is calulated. The models for one-dimensional data are "E" and "V"; for higher dimensions they are "EII", "VII", "EEI", "VEI", "EVI", "VVI", "EEE", "EEV", "VEV" and "VVV".

Usage

cv1EMtrain(data, labels, modelNames)

Arguments

data

A data matrix

labels

Labels for each row in the data matrix

modelNames

Vector of model names that should be tested.

Value

• Returns a vector where each element is the error rate for the corresponding model.

See Also

bicEMtrain

Examples

data(lansing)
odd_seq(from=1, to=nrow(lansing), by=2)
round(cv1EMtrain(data=lansing[odd,-3], labels=lansing[odd,3]), 3)

cv1Modd <- mstepEEV(data=lansing[odd,-3], z=unmap(lansing[odd,3]))
cv1Zodd <- do.call("estepEEV", c(cv1Modd, list(data=lansing[odd,-3])))$z
compClass(map(cv1Zodd), lansing[odd,3])

even _ (1:nrow(lansing))[-odd]
cv1Zeven _ do.call("estepEEV", c(cv1Modd, list(data=lansing[even,-3])))$z
compClass(map(cv1Zodd), lansing[odd,3])$error