R: Computes a classification from a pretrained discriminant

testModel {TSEAL}

R Documentation

Computes a classification from a pretrained discriminant

Description

This function uses a pretrained linear discriminant to classify a set of test data. As output it returns a confusion matrix and optionally the raw classification result.

Usage

testModel(model, test, labels, returnClassification = FALSE, ...)

Arguments

`model`	Trained linear discriminant. see `trainModel`
`test`	MultiWaveAnalysis class object to be used as test set.
`labels`	Vector that determines the class to which each of the observations provided in the test set belongs.
`returnClassification`	Allows to select if the raw result classification is returned.
`...`	Additional arguments

Value

if returnClassification is false return a object of class confusionMatrix
if returnClassification is true, it returns a list containing an object of the confusionMatrix class and a vector with the classification result.

Examples


load(system.file("extdata/ECGExample.rda",package = "TSEAL"))
# The dataset has the first 5 elements of class 1
# and the last 5 of class 2.
labels <- c(rep(1, 5), rep(2, 5))
MWA <- generateStepDiscrim(ECGExample, labels, "haar", maxvars = 5, features = c("var"))
aux <- extractSubset(MWA, c(1, 2, 9, 10))
MWATest <- aux[[1]]
MWATrain <- aux[[2]]
ldaDiscriminant <- trainModel(MWATrain, labels[3:8], "linear")
CM <- testModel(ldaDiscriminant, MWATest, labels[c(1, 2, 9, 10)])