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#' @title Mean absolute error
#' @description Calculates the mean absolute error
#'
#' @param actual A vector of the labels
#' @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}
#'
#' @export
mae <- function(...){
UseMethod("mae")
}
#' @rdname mae
#' @export
mae.default <- function(actual, predicted, ...){
mae_(actual, predicted)
}
#' @rdname mae
#' @export
mae.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"))
}
mae.default(actual, predicted)
}
#' @rdname mae
#' @export
mae.randomForest <- function(modelObject, ...){
actual <- as.numeric(modelObject$y) - 1
predicted <- predict(modelObject, type = 'prob')[,2]
mae.default(actual, predicted)
}
#' @rdname mae
#' @export
mae.glmerMod <- function(modelObject, ...){
actual <- modelObject@resp$y
predicted <- modelObject@resp$mu
mae.default(actual, predicted)
}
#' @rdname mae
#' @export
mae.gbm <- function(modelObject, ...){
actual <- modelObject$data$y
predicted <- modelObject$fit
mae.default(actual, predicted)
}
#' @rdname mae
#' @export
mae.rpart <- function(modelObject, ...){
actual <- modelObject$y
predicted <- predict(modelObject)
mae.default(actual, predicted)
}
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