TSEAL (version 0.1.3)

classify.MultiWaveAnalysis: Classifies observations based on a pretrained model.

Description

This function allows to classify observations based on a pretrained model that could have been obtained in several ways (such as using the train model function).

Usage

# S3 method for MultiWaveAnalysis
classify(data, model, ...)

Value

A factor with predicted class of each observation

Arguments

data

Data to be classified by the model. Remember that it must be an object of type MultiWaveAnalysis. Note that it should have the same variables selected as those used to generate the model.

model

pretrained discriminant model (lda or qda)

...

Additional arguments

See Also

• trainModel

Examples

# \donttest{
load(system.file("extdata/ECGExample.rda",package = "TSEAL"))
# We simulate that the second series has been obtained after
Series1 <- ECGExample[, , 1:9]
Series2 <- ECGExample[, , 10, drop = FALSE]

# Training a discriminant model
MWA <- MultiWaveAnalysis(Series1, "haar", features = c("var"))
MWADiscrim <- StepDiscrim(MWA, c(rep(1, 5), rep(2, 4)), maxvars = 5,
                          features = c("var"))
model <- trainModel(MWADiscrim, c(rep(1, 5), rep(2, 4)), "linear")

# Using the discriminant trained on new data
MWA2 <- MultiWaveAnalysis(Series2, "haar", features = c("var"))
MWA2Discrim <- SameDiscrim(MWA2, MWADiscrim)
prediction <- classify(MWA2Discrim, model)
# }