generateStepDiscrim {TSEAL}

Generate StepDiscrim from raw data

Description

This function allows to obtain in a single step the complete MultiWaveAnalysis and the selection of the most discriminating variables of the MultiWaveAnalysis.

Usage

generateStepDiscrim(
  series,
  labels,
  f,
  maxvars,
  VStep,
  lev = 0,
  features = c("Var", "Cor", "IQR", "PE", "DM"),
  nCores = 0
)

Arguments

series

Sample from the population (dim x length x cases)

labels

Labeled vector that classify the observations

f

Selected filter for the MODWT (to see the available filters use the function availableFilters

maxvars

Maximum number of variables included by the StepDiscrim algorithm (Note that if you defined this, can not define VStep). Must be a positive integer

VStep

Minimum value of V above which all other variables are considered irrelevant and therefore will not be included. (Note that if you defined this, can not defined maxvars).Must be a positive number. For more information see StepDiscrim documentation.

lev

Determines the number of decomposition levels for MODWT (by default the optimum is calculated). Must be a positive integer, where 0 corresponds to the default behavior.

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

Value

A MultiWaveAnalysis with the most discriminant variables based on the features indicated.

See Also

• MultiWaveAnalysis

• StepDiscrim

• StepDiscrimV

Examples

load(system.file("extdata/ECGExample.rda",package = "TSEAL"))
# The dataset has the first 5 elements of class 1
# and the last 5 of class 2.
labels <- c(rep(1, 5), rep(2, 5))
MWADiscrim <- generateStepDiscrim(ECGExample, labels, "haar",
  features = c("Var"), maxvars = 5
)
# or using the VStep option
MWADiscrim <- generateStepDiscrim(ECGExample, labels, "haar",
 features = c("Var", "Cor"), VStep = 0.7
)