impCalc

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impCalc

impCalc function is designed to scale variable importance according to MSE and RMSE calculations. It also stores the raw MSE and RMSE derived from models. impCalc function shouldn't be used alone unless user has trained models from caret package in RData files.

Keywords
models, design
Usage
impCalc(skel_outfile, xTest, yTest, lk_col)
Arguments
skel_outfile
Skeleton name of output file
xTest
Input vector of testing data set
yTest
Output vector of testing data set
lk_col
Number of columns of whole data set
Details

impCalc function lists RData files in working directory assuming there are only models derived by caret. In a loop function loads models and tries to get the variable importance.

Aliases
  • impCalc
Examples
# 
# Hashed to comply with new CRAN check
# 
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
skel_outfile <- paste("_default_",sep="")
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_test,ncol=lk_col_test)

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])


# Calling lower function to create models to calculate on
myVarImp <- regVarImp(model, xTrain, yTrain, xTest,
	    fitControl, myTimeLimit, no.cores, lk_col,
	    supress.output, mySystem)


myImpCalc <- impCalc(skel_outfile, xTest, yTest, lk_col)
Documentation reproduced from package fscaret, version 0.8.5.6, License: GPL-2 | GPL-3

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