fscaret

From fscaret v0.8.5.6 by Jakub Szlek

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/.

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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