testModel: Computes a classification from a pretrained discriminant

In TSEAL: Time Series Analysis Library

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.

See Also

testModel

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)])

TSEAL documentation built on July 2, 2024, 5:06 p.m.