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#' Predict method for DECORATE objects
#'
#' Prediction of new data using DECORATE
#'
#' @param object an object of the class \code{DECORATE}, as created by the function \code{DECORATE}.
#' @param newdata a data frame containing the same predictors as in the training phase.
#' @param type character specifying whether to return the probabilites ('prob') or class ('class'). Default: prob.
#' @param all Return the predictions per tree instead of the average (default = FALSE).
#' @param ... Not used currently.
#'
#' @return vector containing the response probabilities.
#'
#'
#' @examples
#'
#' data(iris)
#' y <- as.factor(ifelse(iris$Species[1:100]=="setosa",0,1))
#' x <- iris[1:100,-5]
#' dec <- DECORATE(x = x, y = y)
#' predict(object=dec,newdata=x)
#'
#'
predict.DECORATE <- function (object, newdata, type = 'prob',
all =FALSE, ...) {
final <- newdata
if (!is.data.frame(final)) stop("newdata must be a data frame")
predicted <- matrix(NA,nrow=nrow(final),ncol=length(object))
if(type == 'prob') {
for (i in 1:length(object)){
predicted[,i] <-predict(object[[i]], final, 'prob')[,2]
}
if(all) {
return(predicted)
} else {
finalpredicted <- rowMeans(predicted)
return(finalpredicted)
}
}
if(type == 'class') {
Mode <- function(x) {
ux <- unique(x)
ux[which.max(tabulate(match(x, ux)))]
}
for (i in 1:length(object)){
predicted[,i] <- as.numeric(as.character(predict(object[[i]], final, 'class')))
}
if(all) {
return(predicted)
} else {
finalpredicted <- as.factor(apply(predicted, 1, Mode))
return(finalpredicted)
}
}
}
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