#' @title GINI Coefficient
#'
#' @description Calculates the GINI coefficient for a binary classifcation model
#'
#' @param actual A vector of the labels. Can be \code{numeric, character, or factor}
#' @param predicted A vector of predicted values
#' @param \dots additional parameters to be passed the the s3 methods
#' @param modelObject the model object. Currently supported \code{glm, randomForest, glmerMod, gbm}
#'
#' @examples
#' data(testDF)
#' glmModel <- glm(y ~ ., data = testDF, family="binomial")
#' Preds <- predict(glmModel, type = 'response')
#'
#' gini(testDF$y, Preds)
#' # using s3 method for glm
#' gini(glmModel)
#'
#' @export
gini <- function(...){
UseMethod("gini")
}
#' @rdname gini
#' @importFrom data.table fsort
#' @export
gini.default <- function(actual, predicted, ...){
df1 <- data.frame(actual = actual, predicted = predicted)
df2 <- data.frame(actual = actual, predicted = actual)
df1 <- df1[order(-df1$predicted),]
df2 <- df2[order(-df2$actual),]
gini <- gini_(df1$actual)/gini_(df2$actual)
return(gini)
}
#' @rdname gini
#' @export
gini.glm <- function(modelObject, ...){
family <- family(modelObject)[[1]]
if(any(family %in% c('binomial', 'poisson'))){
actual <- modelObject$y
predicted <- modelObject$fitted.values
} else {
stop(paste0("family: ", family, " is not currently supported"))
}
gini.default(actual, predicted)
}
#' @importFrom stats predict
#' @rdname gini
#' @export
gini.randomForest <- function(modelObject, ...){
actual <- as.numeric(modelObject$y) - 1
predicted <- predict(modelObject, type = 'prob')[,2]
gini.default(actual, predicted)
}
#' @rdname gini
#' @export
gini.glmerMod <- function(modelObject, ...){
actual <- modelObject@resp$y
predicted <- modelObject@resp$mu
gini.default(actual, predicted)
}
#' @rdname gini
#' @export
gini.gbm <- function(modelObject, ...){
actual <- modelObject$data$y
predicted <- modelObject$fit
gini.default(actual, predicted)
}
#' @rdname gini
#' @export
gini.rpart <- function(modelObject, ...){
actual <- modelObject$y
predicted <- predict(modelObject)
gini.default(actual, predicted)
}
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