R/predict.prunedadaStump.R

In nivangio/adaStump: Fitting adaBoost model with Stumps as base learners

#' Predict from a \code{prunedadaStump} object 
#' 
#' 
#' @param stump.model object of class \code{prunedadaStump}
#' @param newdata Data to be fit
#' 
#' @return 
#' A vector of length \code{nrow(newdata)} expressing the probability of ocurrence of the event modelled.
#' 
#' @examples
#' 
#' 
#' #Load Iris
#' data(iris)
#' 
#' #Create Variable is Iris as numerical
#' iris$isSetosa <- as.numeric(iris$Species == "setosa")
#' 
#' #Split sample in 70 train - 30 test
#' train.ind <- sample(nrow(iris), nrow(iris) * 0.7)
#' 
#' #Train model. For obvious reasons, Species variable is not included in the fit
#' fit <- adaStump(isSetosa ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, iris[train.ind,], 
#'                 type = "discrete", iter = 10, nu = 0.05, bag.frac = 0.6)
#'                 
#' #Prune Tree and predict
#' 
#' fit_pruned <- pruneTree(fit)
#' predict(fit_pruned,iris[-train.ind,])
#' 
#' @export

predict.prunedadaStump <- function(stump.model, newdata){
  
  ret <- predict.adaStump(stump.model,newdata)
  
  return(ret)
  
}