X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2FcomputeError.R;h=1fbb2a8cd007c80368878da8849f0643da31f012;hb=102bcfda4afbb5cfee885cbee0f55545624168fd;hp=b57e60732e6ad42b7241bf165354eb27ab142fde;hpb=af3b84f4cacade7d83221ca0249b546c50ddf340;p=talweg.git

diff --git a/pkg/R/computeError.R b/pkg/R/computeError.R
index b57e607..1fbb2a8 100644
--- a/pkg/R/computeError.R
+++ b/pkg/R/computeError.R
@@ -1,15 +1,19 @@
 #' Compute error
 #'
-#' Obtain the errors between forecast and data
+#' Compute the errors between forecasted and measured series.
 #'
-#' @param data Dataset, object of class \code{Data} output of \code{getData}
-#' @param forecast Forecast object, class \code{Forecast} output of \code{computeForecast}
-#' @param horizon Horizon where to compute the error (<= horizon used in \code{computeForecast})
+#' @param data Object of class \code{Data} output of \code{getData}
+#' @param pred Object of class \code{Forecast} output of \code{computeForecast}
+#' @param horizon Horizon where to compute the error
+#'   (<= horizon used in \code{computeForecast})
 #'
-#' @return A list (abs,MAPE) of lists (day,indices)
+#' @return A list (abs,MAPE) of lists (day,indices). The "indices" slots contain series
+#'   of size L where L is the number of predicted days; i-th value is the averaged error
+#'   (absolute or MAPE) on day i. The "day" slots contain curves of errors, for each time
+#'   step, averaged on the L forecasting days.
 #'
 #' @export
-computeError = function(data, forecast, horizon=data$getStdHorizon())
+computeError = function(data, pred, horizon=data$getStdHorizon())
 {
 	L = forecast$getSize()
 	mape_day = rep(0, horizon)
@@ -22,15 +26,15 @@ computeError = function(data, forecast, horizon=data$getStdHorizon())
 	{
 		index = forecast$getIndexInData(i)
 		serie = data$getSerie(index+1)[1:horizon]
-		pred = forecast$getSerie(i)[1:horizon]
-		if (!any(is.na(serie)) && !any(is.na(pred)))
+		forecast = pred$getForecast(i)[1:horizon]
+		if (!any(is.na(serie)) && !any(is.na(forecast)))
 		{
 			nb_no_NA_data = nb_no_NA_data + 1
-			mape_increment = abs(serie - pred) / serie
+			mape_increment = abs(serie - forecast) / serie
 			mape_increment[is.nan(mape_increment)] = 0. # 0 / 0
 			mape_increment[!is.finite(mape_increment)] = 1. # >0 / 0
 			mape_day = mape_day + mape_increment
-			abs_increment = abs(serie - pred)
+			abs_increment = abs(serie - forecast)
 			abs_day = abs_day + abs_increment
 			mape_indices[i] = mean(mape_increment)
 			abs_indices[i] = mean(abs_increment)