StepDiscrim function

Select the most discriminating variables

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.

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

Arguments

• MWA: MultiWaveAnalysis object obtained with MultiWaveAnalysis function
• labels: Labeled vector that classify the observations.
• maxvars: The number of desired values. Must be a positive integer
• 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

Returns

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

Details

Based on StepDiscrim of R.E. Strauss

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,
  c("Var")
)

See Also

• MultiWaveAnalysis
• StepDiscrimV