R: Generates a discriminant model from training data.

trainModel.array {TSEAL}

R Documentation

Generates a discriminant model from training data.

Description

It generates a discriminant model starting from the training data, which must be provided in 2 groups depending on their classification. The method first obtains the variances and correlations using MODWT, the f filter is applied with a number of levels lev. Then a subset of all the generated features will be obtained by means of a stepwise discriminant, which can be driven by a maximum number of features or by a minimum metric to be met. Finally, the selected discriminant model is trained with the subset obtained.

Usage

## S3 method for class 'array'
trainModel(
  data,
  labels,
  f,
  method,
  maxvars,
  VStep,
  lev = 0,
  features = c("Var", "Cor", "IQR", "PE", "DM"),
  nCores = 0,
  ...
)

Arguments

`data`	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)
`method`	Selected method for the discriminant. Valid values "linear" "quadratic"
`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 greater than 0.
`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 greater than 0. 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
`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.
`...`	Additional arguments

Value

A discriminant model object (lda or qda)

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))
model <- trainModel(ECGExample, labels, "d6", "linear",
  maxvars = 5, features = c("Var")
)
# or using VStep
modelV <- trainModel(ECGExample, labels, "d6", "linear",
    VStep = 14.5, features = c("Var")
)