KFCV.MultiWaveAnalysis function

KFCV

Performs k-fold cross-validation where groups are chosen randomly. In case the value k is not divisor of the number of observations the last group will have nobs mod k observations.

## S3 method for class 'MultiWaveAnalysis'
KFCV(data, labels, method, k = 5L, returnClassification = FALSE, ...)

Arguments

• data: MultiWaveAnalysis (MWA) object obtained with MultiWaveAnalysis and preferably obtained a subset of its characteristics (StepDiscrim,StepDiscrimV)
• labels: labeled vector that classify the observations.
• method: Selected method for discrimination. Valid options "linear" "quadratic"
• k: the number of folds in KFCV. Must be a positive integer and lower or equal than the number of observations
• 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"))
MWA <- MultiWaveAnalysis(ECGExample, "haar", features = c("var"))
MWADiscrim <- StepDiscrim(MWA, c(rep(1, 5), rep(2, 5)), 5,
              features = c("var"))
CM <- KFCV(MWADiscrim, c(rep(1, 5), rep(2, 5)), "linear", 5,
  returnClassification = FALSE
)