This function allows to obtain in a single step the complete MultiWaveAnalysis and the selection of the most discriminating variables of the MultiWaveAnalysis.
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
Returns
A MultiWaveAnalysis with the most discriminant variables based on the features indicated.
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 optionMWADiscrim <- generateStepDiscrim(ECGExample, labels,"haar", features = c("Var","Cor"), VStep =0.7)