R: Classifies observations based on a pretrained model.

classify.MultiWaveAnalysis {TSEAL}

R Documentation

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 class 'MultiWaveAnalysis'
classify(data, model, ...)

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

Value

A factor with predicted class of each observation

Examples


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)