StepDiscrim {TSEAL} | R Documentation |
Select the most discriminating variables
Description
Stepwise discriminant analysis to determine the best subset of variables. Introduces variables so as to maximize at each step the Lawley-Hotelling trace (=Rao's V). This measure is proportional to the mean Mahalanobis distance.
Usage
StepDiscrim(
MWA,
labels,
maxvars,
features = c("Var", "Cor", "IQR", "PE", "DM"),
nCores = 0
)
Arguments
MWA |
MultiWaveAnalysis object obtained with MultiWaveAnalysis function |
labels |
Labeled vector that classify the observations. |
maxvars |
The number of desired values. Must be a positive integer |
features |
A list of characteristics that will be used for the
classification process. To see the available features see
|
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 |
Details
Based on StepDiscrim of R.E. Strauss
Value
A MultiWaveAnalysis object with the maxvars most discriminant variables. This object contains: * Features: A list with the initial computed features * StepSelection: The maxvars most discriminant variables * Observations: Number of total observations * NLevels: Number of levels selected for the decomposition process * filter: Filter used in the decomposition process
See Also
Examples
load(system.file("extdata/ECGExample.rda",package = "TSEAL"))
MWA <- MultiWaveAnalysis(ECGExample, "haar", features = c("var"))
MWADiscrim <- StepDiscrim(
MWA, c(rep(1, 5), rep(2, 5)), 5,
c("Var")
)