TSEAL (version 0.1.3)

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

Value

A discriminant model object (lda or qda)

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

See Also

• StepDiscrim

• StepDiscrimV

• trainModel

Examples

# \donttest{
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")
)
# }