regVarImp

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regVarImp

The function uses the caret package advantage to perform fitting of numerous regression models.

Keywords
robust, models
Usage
regVarImp(model, xTrain, yTrain, xTest,
	  fitControl, myTimeLimit, no.cores,
	  lk_col, supress.output, mySystem)
Arguments
model
Chosed models as called from function fscaret(), argument Used.funcRegPred.
xTrain
Training data set, data frame of input vector
yTrain
Training data set, vector of observed outputs
xTest
Testing data set, data frame of input vector
fitControl
Fitting controls passed to caret function
myTimeLimit
Time limit in seconds for single model fitting
no.cores
Number of used cores for calculations
lk_col
Number of columns for whole data set (inputs + output)
supress.output
If TRUE output of models are supressed.
mySystem
Called from fscaret() result of function .Platform$OS.type
References

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

Aliases
  • regVarImp
Examples
# Load library
library(fscaret)

# Read working directory
myWD <- getwd()

# Set working directory to tmp
setwd(tempdir())

# Load dataset
data(dataset.train)
data(dataset.test)

# Make objects
trainDF <- dataset.train
testDF <- dataset.test
model <- c("lm","pls","pcr")
fitControl <- trainControl(method = "boot", returnResamp = "all") 
myTimeLimit <- 5
no.cores <- 2
supress.output <- TRUE

mySystem <- .Platform$OS.type

if(mySystem=="windows"){
no.cores <- 1
}
# Scan dimensions of trainDF [lk_row x lk_col]
lk_col = ncol(trainDF)
lk_row = nrow(trainDF)

# Read labels of trainDF
labelsFrame <- as.data.frame(colnames(trainDF))

# Create a train data set matrix
trainMatryca_nr <- matrix(data=NA,nrow=lk_row,ncol=lk_col)

row=0
col=0

for(col in 1:(lk_col)) {
   for(row in 1:(lk_row)) {
     trainMatryca_nr[row,col] <- (as.numeric(trainDF[row,col]))
    }
}

# Pointing standard data set train
xTrain <- data.frame(trainMatryca_nr[,-lk_col])
yTrain <- as.vector(trainMatryca_nr[,lk_col])


#--------Scan dimensions of trainDataFrame1 [lk_row x lk_col]
lk_col_test = ncol(testDF)
lk_row_test = nrow(testDF)

testMatryca_nr <- matrix(data=NA,nrow=lk_row,ncol=lk_col)

row=0
col=0

for(col in 1:(lk_col_test)) {
   for(row in 1:(lk_row_test)) {
     testMatryca_nr[row,col] <- (as.numeric(testDF[row,col]))
    }
}

# Pointing standard data set test
xTest <- data.frame(testMatryca_nr[,-lk_col])
yTest <- as.vector(testMatryca_nr[,lk_col])


myVarImp <- regVarImp(model, xTrain, yTrain, xTest,
	    fitControl, myTimeLimit, no.cores, lk_col,
	    supress.output, mySystem)

summary(myVarImp)

print(myVarImp)

# Get back to previous working directory
setwd(myWD)
Documentation reproduced from package fscaret, version 0.8.5.1, License: GPL-2 | GPL-3

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