testModel function

Computes a classification from a pretrained discriminant

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.

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

Returns

• 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)])

See Also

testModel