Sequential application of decorrelation, scaling, feature selection, and PCA/Whitening then fit
filteredFit(formula = formula,
data=NULL,
filtermethod=univariate_KS,
fitmethod=e1071::svm,
filtermethod.control=list(pvalue=0.10,limit=0),
Scale="none",
PCA=FALSE,
WHITE=c("none","CCA"),
DECOR=FALSE,
DECOR.control=list(thr=0.80,method="fast",type="NZLM"),
...
)
The fitted model
The output of the feature selection function
The character vector with all the selected features
The set of features used for training
The parameters passed to the fitting method
Indicates if the fitting was to a factor
The number of possible outcomes
the base formula to extract the outcome
the data to be used for training the KNN method
the method for feature selection
the fit function to be used
the set of parameters required by the feature selection function
Scale the data using the provided method
Decorrelate the input data using PCA
Whittening process: "PCA" or "CCA"
Decorrelate the input data estimating the UPSTM
Parameters to the decorrelation function
parameters for the fitting function
Jose G. Tamez-Pena