library(fscaret)
# Load data sets
data(dataset.train)
data(dataset.test)
requiredPackages <- c("R.utils", "gsubfn", "ipred", "caret", "parallel", "MASS")
mySystem <- .Platform$OS.type
if(mySystem=="windows"){
myCores <- 1
} else {
myCores <- 2
}
myFirstRES <- fscaret(dataset.train, dataset.test, installReqPckg=FALSE,
preprocessData=FALSE, with.labels=TRUE, classPred=FALSE,
regPred=TRUE, skel_outfile=NULL,
impCalcMet="RMSE&MSE", myTimeLimit=5,
Used.funcRegPred=c("lm","pls","pcr"), Used.funcClassPred=NULL,
no.cores=myCores, method="boot", returnResamp="all",
supress.output=TRUE)
# Training data set after preprocessing
myFirstRES$PPTrainDF
# Testing data set after preprocessing
myFirstRES$PPTestDF
# Model predictions
myFirstRES$ModelPred
# Variable importance after scaling according to RMSE and MSE
myFirstRES$VarImp
# Reduced input vector (data set) after preprocessing
myFirstRES$PPlabels
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