R/IV.calc.data.R

In ayhandis/creditR: A Credit Risk Scoring and Validation Package

#' @title Calculate Information Value for a Given Data
#'
#' @description
#' This function allows the calculation of the IV for a given data.
#'
#' @param data A data set needs to be specified.
#' @param default_flag  The default flag need to specified as string.
#' @keywords creditR
#' @export
#' @examples
#' default_f <- c('1','0','0', '1','1','0','0','1','1')
#' birth_year <- c(1980, 1985, 1971,1971,1985,1971,1980,1980,1985)
#' job <- c(1,1,2, 2,2,3,3,2,3)
#' example_data <- data.frame(default_f,birth_year,job)
#' IV.calc.data(example_data, "default_f")


IV.calc.data <- function(data,default_flag){
  IV.calc <- function(data, default_flag,variable){
    number <- which( colnames(data)==default_flag )
    good = subset(data,data[,number] == "0")
    bad = subset(data, data[,number] == "1")
    x <- table(good[,variable])
    y <- as.data.frame(x)
    z <- table(bad[,variable])
    u <- as.data.frame(z)
    merged = merge(x = y, y = u, by="Var1", all = TRUE)
    merged$percentx = merged$Freq.x/(sum(merged$Freq.x))
    merged$percenty = merged$Freq.y/(sum(merged$Freq.y))
    merged$IV = (merged$percentx-merged$percenty)*(log(merged$percentx/merged$percenty))
    IV_RESULT <- sum(merged$IV)
    return(IV_RESULT)
  }
  iv_column_names <- matrix(data=NA,nrow=1,ncol=length(data))
  Information_Values <- matrix(data=NA,nrow=1,ncol=length(data))
  for(i in 1:length(data)){
    Information_Value <- IV.calc(data,default_flag,colnames(data)[i])
    Information_Values[,i] <- Information_Value
    iv_column_names[,i] <- colnames(data)[i]
  }
  iv_summary<-as.data.frame(cbind(t(iv_column_names), t(Information_Values)))
  colnames(iv_summary) <- c("Variable","IV")
  iv_summary <- iv_summary[iv_summary[,1] != default_flag,]
  rownames(iv_summary) <- c(1:length(iv_summary[,1]))
  return(iv_summary)
}