fscaret

0th

Percentile

feature selection caret

Main function for fast feature selection. It utilizes other functions as regPredImp or impCalc to obtain results in a list of data frames.

Keywords
methods, array, optimize, iteration
Usage
fscaret(trainDF, testDF, installReqPckg = FALSE, preprocessData = FALSE,
	with.labels = FALSE, classPred = FALSE, regPred = TRUE, skel_outfile = NULL,
	impCalcMet = "RMSE&MSE", myTimeLimit = 24 * 60 * 60, Used.funcRegPred = NULL,
	Used.funcClassPred = NULL, no.cores = NULL, method = "boot", returnResamp = "all",
	missData=NULL, supress.output=FALSE, ...)
Arguments
trainDF
Data frame of training data set, MISO (multiple input single output) type
testDF
Data frame of testing data set, MISO (multiple input single output) type
installReqPckg
If TRUE prior to calculations it installs all required packages, please be advised to be logged as root (admin) user
preprocessData
If TRUE data preprocessing is performed prior to modeling
with.labels
If TRUE header of the input files are read
classPred
If TRUE classification models are applied (for v0.8 it is not available)
regPred
If TRUE regression models are applied
skel_outfile
Skeleton output file, e.g. skel_outfile=c("_myoutput_")
impCalcMet
Variable importance calculation scaling according to RMSE and MSE, for both please enter impCalcMet="RMSE&MSE"
myTimeLimit
Time limit in seconds for single model development
Used.funcRegPred
Vector of regression models to be used, for all available models please enter Used.funcRegPred="all"
Used.funcClassPred
Vector of classification models to be used (for v0.8 it is not available)
no.cores
Number of cores to be used for modeling, if NULL all available cores are used, should be numeric type or NULL
method
Method passed to fitControl of caret package
returnResamp
Returned resampling method passed to fitControl of caret package
missData
Handling of missing data values. Possible values: "delRow" - delete observations with missing values, "delCol" - delete attributes with missing values, "meanCol" - replace missing values with column mean.
supress.output
If TRUE output of modeling phase by caret functions are supressed. Only info which model is currently calculated and resulting variable importance.
...
Additional arguments, preferably passed to fitControl of caret package
Value

  • $ModelPredList of outputs from caret model fitting
  • $VarImpData frames of variable importance
  • $PPlabelsData frame of resulting preprocessed data set with original input numbers and names
  • $PPTrainDFTraining data set after preprocessing
  • $PPTestDFTesting data set after preprocessing

Note

Be advised when using fscaret function as it requires hard disk operations for saving fitted models and data frames. Files are written in R temp session folder, for more details see tempdir(), getwd() and setwd()

References

Kuhn M. (2008) Building Predictive Models in R Using the caret Package Journal of Statistical Software 28(5) http://www.jstatsoft.org/.

Aliases
  • fscaret
Examples
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
Documentation reproduced from package fscaret, version 0.8.5.6, License: GPL-2 | GPL-3

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