R: Generates and validates a discriminant model generated...

KFCV.array {TSEAL}

R Documentation

Generates and validates a discriminant model generated directly from the data.

Description

It generates and validates a discriminant model starting from the data. First , a MultiWaveAnalysis object is obtained according to the selected characteristics ,filter and levels. Then, the most important features are selected using a stepwise discriminant that allows to select a maximum number of variables (maxvars) or a minimum enhancement step (VStep). Finally, the model is trained using the subset of features and validated using K-Fold Cross Validation (KFCV).

Usage

## S3 method for class 'array'
KFCV(
  data,
  labels,
  f,
  method,
  maxvars,
  VStep,
  k = 5L,
  lev = 0L,
  features = c("Var", "Cor", "IQR", "PE", "DM"),
  returnClassification = FALSE,
  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 and greater than 0. For more information see StepDiscrim documentation
`k`	The number of folds in KFCV. Must be a positive integer lower or equal than the number of observations
`lev`	Determines the number of decomposition levels for MODWT (by default the optimum is calculated using the "conservative" strategy). Must be a positive integer (including 0 to auto-select the level)
`features`	A list of characteristics that will be used for the classification process. To see the available features see `availableFeatures`
`returnClassification`	Allows to select if the raw result classification is returned.
`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

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"))
labels <- c(rep(1, 5), rep(2, 5))
CM <- KFCV(ECGExample, labels, "haar", "linear",
  maxvars = 5,
  features = c("Var"), returnClassification = FALSE
)
# or with VStep
CMV <- KFCV(ECGExample, labels, "haar", "linear",
 k = 5,
 VStep = 5,
 features = c("Var"), returnClassification = FALSE
)