R: Variable Ranking model automation codes to write it as a Function - r

Question

How can I write the list of commands below into just one Function?
For example: VariableRanking <- function(formula, variables,.....) {
Insert commands........ }
#Variable Ranking Model automation
#exclusion of the variables that are not model variables
exclude <- c("~,", "+" ) # exclude target which is bound_count for Property
formula <- toString(formula)
formula
#listing the entire model formula out
variables_pre <- unlist(strsplit(formula, split = " "))
variables_pre
#keeping only the model variables
variables <- sort(variables_pre[!variables_pre %in% exclude])
variables
#Exclude "," on the target variable
variables[1] <- substr(variables[1], 1, nchar(variables[1])-1)
variables
#Assigning the variables into a data frame
d <- c(1:length(variables))
d
d= data.frame(d)
d
d= t(d)
d
colnames(d)=variables
d
# exclude target variable on the data frame
allvariables <- colnames(d)[-1]
allvariables
# container for models
listOfModels <- vector("list", length(allvariables))
listOfModels
# loop over variables
for (i in seq_along(allvariables)) {
# exclude variable i
currentvariable <- allvariables[-i]
# programmatically assemble regression formula
regressionFormula <- as.formula(
paste(variables[1],"~", paste(currentvariable, collapse="+")))
# fit model
currentModel <- glm(formula = regressionFormula, family=binomial(link = "logit"), data=dataL_TT)
# store model in container
listOfModels[[i]] <- currentModel
}
listOfModels
#List of AICs for each model
lapply(listOfModels,function(xx) xx$aic)
#Assign X as the AIC of the full model
X <- modelTT$aic
X
# Difference of AICs of each model to the AIC of the full model
AICdifference <- lapply(listOfModels,function(xx) xx$aic - X)
AICdifference
# Naming the AIC Difference
AICdifference2 = data.frame(variables=allvariables, AICdiff=unlist(AICdifference))
AICdifference2
#Graph the Barchart of the AIC decrease of each variables and save it to pdf
pdf("Barchart.pdf",width=12,height=10)
par(mar=c(2,18,2,5))
barplot(sort(AICdifference2$AICdiff, decreasing = F), main="Variable Ranking based on AIC decrease",
horiz=TRUE, xlab="AIC Increase", names.arg= AICdifference2$variables[order(AICdifference2$AICdiff, decreasing = F)],
las=1, col= 'dodgerblue4')
dev.off()
Is it possible? because it has a lot of parameters.
So basically I just need the output of the AICdifference2 data frame.
And the barplot saved as pdf and pop up

Answer 1

Try this:
FOO <- function(myformula, data, fullmodel_AIC, plotname){
exclude <- c("~,", "+" ) # exclude target which is bound_count for Property
myformula <- toString(myformula)
variables_pre <- unlist(strsplit(myformula, split = " "))
variables <- sort(variables_pre[!variables_pre %in% exclude])
variables[1] <- substr(variables[1], 1, nchar(variables[1])-1)
d <- t(data.frame(c(1:length(variables))))
colnames(d)=variables
allvariables <- colnames(d)[-1]
listOfModels <- vector("list", length(allvariables))
for (i in seq_along(allvariables)) {
# exclude variable i
currentvariable <- allvariables[-i]
# programmatically assemble regression formula
regressionFormula <- as.formula(
paste(variables[1],"~", paste(currentvariable, collapse="+")))
# fit model
currentModel <- glm(formula = regressionFormula, family=binomial(link = "logit"), data = data)
# store model in container
listOfModels[[i]] <- currentModel
}
AICdifference <- lapply(listOfModels,function(xx) xx$aic - fullmodel_AIC)
AICdifference2 <- data.frame(variables=allvariables, AICdiff=unlist(AICdifference))
pdf(paste0(plotname, ".pdf"),width=12,height=10)
par(mar=c(2,18,2,5))
barplot(sort(AICdifference2$AICdiff, decreasing = F), main="Variable Ranking based on AIC decrease",
horiz=TRUE, xlab="AIC Increase", names.arg= AICdifference2$variables[order(AICdifference2$AICdiff, decreasing = F)],
las=1, col= 'dodgerblue4')
dev.off()
return(AICdifference2)
}
You need four parameters: The myformula, the data (dataL_TT in your code), the fullmodel_AIC (modelTT$aic in your code), and a string to name your plot.
Try calling it with FOO(myformula, dataL_TT, modelTT$aic, "test") and insert your formula object for myformula.
I've changed formula to myformula because formula is a base function of the stats package, and it is generally unwise to use object names which are base functions.