diff options
Diffstat (limited to 'R/partition.R')
| -rw-r--r-- | R/partition.R | 32 |
1 files changed, 16 insertions, 16 deletions
diff --git a/R/partition.R b/R/partition.R index e64e1fb..7c0af2b 100644 --- a/R/partition.R +++ b/R/partition.R @@ -1,19 +1,19 @@ -#' Subset an idframe object +#' Subset an idframe data #' -#' Subsetting method for objects of class \code{idframe} +#' Subsetting method for datas of class \code{idframe} #' -#' @param object an object of class \code{idframe} +#' @param data an object of class \code{idframe} #' @param indices the indices that need to be subsetted #' @export -dataSlice <- function(object,indices){ +dataSlice <- function(data,indices){ # check if the class is correct - if(class(object)!='idframe') - stop("Not an idframe object") + if(class(data)!='idframe') + stop("Not an idframe data") - if(!all(indices %in% seq(to=dim(object$output)[1],by=1))) + if(!all(indices %in% seq(to=dim(data$output)[1],by=1))) stop("Invalid indices") - trim <- object + trim <- data trim$output <- trim$output[indices,,drop=F] trim$input <- trim$input[indices,,drop=F] @@ -32,9 +32,9 @@ dataSlice <- function(object,indices){ #' The function splits the data into training and validation sets and returns them bundled #' as a list. The size of the sets are determined by the parameter \code{p}. #' -#' @param object an object of class \code{idframe} +#' @param data an object of class \code{idframe} #' @param p the percentage of the data that goes to training (Default : \code{0.6}) -#' @return list containing estimation and validation idframe objects +#' @return list containing estimation and validation idframe datas #' #' @examples #' data(cstr) @@ -43,18 +43,18 @@ dataSlice <- function(object,indices){ #' test <- splitList$validation # testing set #' #' @export -dataPartition <- function(object,p=0.6){ +dataPartition <- function(data,p=0.6){ # check if the class is correct - if(class(object)!='idframe') - stop("Not an idframe object") + if(class(data)!='idframe') + stop("Not an idframe data") - index <- seq_along(object$output[,1]) + index <- seq_along(data$output[,1]) trainIndex <- index[1:round(p*length(index))] testIndex <- index[!(index %in% trainIndex)] - train <- dataSlice(object,trainIndex) - test <- dataSlice(object,testIndex) + train <- dataSlice(data,trainIndex) + test <- dataSlice(data,testIndex) return(list(estimation=train,validation=test)) }
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