update documention, fix package to compete with 'method Bruno'

[talweg.git] / pkg / R / computeError.R

diff --git a/pkg/R/computeError.R b/pkg/R/computeError.R
index af4109f..db5783e 100644 (file)
--- a/pkg/R/computeError.R
+++ b/pkg/R/computeError.R
@@ -1,36 +1,42 @@
-#' @title Get error
+#' Compute error
 #'
-#' @description 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 predict_from First time step to consider (>= predict_from used in
+#'   \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$getStdHorizon())
+computeError = function(data, pred, predict_from, horizon=length(data$getSerie(1)))
 {
-       L = forecast$getSize()
-       mape_day = rep(0, horizon)
-       abs_day = rep(0, horizon)
+       L = pred$getSize()
+       mape_day = rep(0, horizon-predict_from+1)
+       abs_day = rep(0, horizon-predict_from+1)
        mape_indices = rep(NA, L)
        abs_indices = rep(NA, L)
 
        nb_no_NA_data = 0
        for (i in seq_len(L))
        {
-               index = forecast$getIndexInData(i)
-               serie = data$getSerie(index+1)[1:horizon]
-               pred = forecast$getSerie(i)[1:horizon]
-               if (!any(is.na(serie)) && !any(is.na(pred)))
+               index = pred$getIndexInData(i)
+               serie = data$getSerie(index)[predict_from:horizon]
+               forecast = pred$getForecast(i)[predict_from: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)