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#' Mean-Center the data
#'
#' Mean Centers the input and output matrices.
#'
#' @param data an object of class \code{idframe}
#'
#' @return
#' A list containing the following elements
#'
#' \tabular{ll}{
#' \code{fitted.values} \tab \code{idframe} object with mean-centered variables \cr
#' \code{output.mean} \tab \code{vector} containing means for each output variable \cr
#' \code{input.mean} \tab \code{vector} containing means for each input variable
#' }
#'
#' @examples
#' data(cstr)
#' fit.mean <- demean(cstr)
#' cstr_demean <- predict(fit.mean)
#'
#' @seealso \code{\link{predict.demean}}, \code{\link[stats]{colMeans}}
#' @export
demean <- function(data){
data_demean <- data
output.mean <- colMeans(data$output)
input.mean <- colMeans(data$input)
data_demean$output <- data$output - output.mean
data_demean$input <- data$input - input.mean
est <- list(fitted.values=data_demean,output.mean = output.mean,
input.mean = input.mean)
class(est) <- "demean"
return(est)
}
#' Predict the centered values
#'
#' Center an \code{idframe} object based on the training center means
#'
#' @param object an object of class \code{idframe}
#' @param newdata An optional idframe object in which to look for variables with which
#' to predict. If ommited, the original idframe object is used
#'
#' @return an \code{idframe} object
#'
#' @examples
#' ## Examples for train and test sets
#' data(cstr)
#' splitList <- dataPartition(cstr,p=0.6)
#' train <- splitList$estimation # training set
#' test <- splitList$validation # testing set
#' fit.mean <- demean(train)
#' train_demean <- predict(fit.mean)
#' test_demean <- predict(fit.mean,newdata=test)
#' @export
predict.demean <- function(object,newdata=NULL,...){
if(is.null(newdata)){
data <- fitted(object)
} else{
data <- newdata
data$output <- data$output - object$output.mean
data$input <- data$input - object$input.mean
}
return(data)
}
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