StepDiscrimV {TSEAL}

Select the most discriminating variables

Description

Stepwise discriminant analysis to determine the best subset of variables. Introduces variables so as to maximize at each step the Lawley-Hotelling trace (=Rao's V). This measure is proportional to the mean Mahalanobis distance.The process ends when in one step the value of the Lawley-Hotelling trace is less than a given value.

Usage

StepDiscrimV(
  MWA,
  labels,
  VStep,
  features = c("Var", "Cor", "IQR", "PE", "DM"),
  nCores = 0
)

Arguments

MWA

MultiWaveAnalysis object obtained with MultiWaveAnalysis function

labels

Labeled vector that classify the observations.

VStep

Determine the minimum value of V to continue adding new variables. Ex if an determinate step the maximum V is 0.2 but VStep is 0.3 the algorithm end. Must be greater than 0.

features

A list of characteristics that will be used for the classification process. To see the available features see availableFeatures

nCores

Determines the number of processes that will be used in the function, by default it uses all but one of the system cores. Must be a positive integer, where 0 corresponds to the default behavior

Details

Based on StepDiscrim of R.E. Strauss

Value

A MultiWaveAnalysis object with the most discriminant variables. This Object contains: * Features: A list with the initial computed features * StepSelection: The most discriminant variables selected by this function * Observations: Number of total observations * NLevels: Number of levels selected for the decomposition process * filter: Filter used in the decomposition process

See Also

• MultiWaveAnalysis

• StepDiscrim

Examples

load(system.file("extdata/ECGExample.rda",package = "TSEAL"))
MWA <- MultiWaveAnalysis(ECGExample, "haar", features = c("var"))
MWADiscrim <- StepDiscrimV(
  MWA, c(rep(1, 5), rep(2, 5)), 0.1,
  c("Var")
)