# Commits

committed 2a414e2

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• Parent commits a215840

# File R/autoKrige.cv.r

 	out = list()
 	# mean error, ideally 0:
 	out$mean_error = mean(obj$residual)
+    # mean error divided by the mean of the observed values, measure for how large the mean_error is in contrast to the mean of the dataset
+    out$me_mean = out$mean_error / mean(obj$observed)  # mean absolute error, ideally 0, less vulnerable to outliers  out$MAE = mean(abs(obj$residual))  # MSE, ideally small  out$cor_predres = cor(obj$observed - obj$residual, obj$residual)  # RMSE, ideally small  out$RMSE = sqrt(sum(obj$residual^2) / length(obj$residual))
-	# RMSE, ideally zero
+    # RMSE / sd(observed), measure for how much the residuals vary to the total variation in the dataset
+    out$RMSE_sd = out$RMSE / sd(obj$observed) + # URMSE, ideally zero  out$URMSE = sqrt((sum(obj$residual^2) / length(obj$residual)) - mean(obj$residual)^2)  # Inter quartile range, ideally small  out$iqr = IQR(obj\$residual)

# File man/compare.cv.rd

 \value{A data.frame with for each cross-validation result a number of diagnostics:
 % \describe{
 	\item{mean_error}{The mean of the cross-validation residual. Ideally small.}
+    \item{me_mean}{mean error divided by the mean of the observed values,
+                   measure for how large the mean_error is in contrast to the mean of the dataset}
 	\item{MSE}{Mean Squared error.}
 	\item{MSNE}{Mean Squared Normalized Error, mean of the squared z-scores. Ideally small.}
 	\item{cor_obspred}{Correlation between the observed and predicted values. Ideally 1.}
 	\item{cor_predres}{Correlation between the predicted and the residual values. Ideally 0.}
 	\item{RMSE}{Root Mean Squared Error of the residual. Ideally small.}
+    \item{RMSE_sd}{RMSE divided by the standard deviation of the observed values. Provides a measure
+                   variation of the residuals vs the variation of the observed values.}
 	\item{URMSE}{Unbiased Root Mean Squared Error of the residual. Ideally small.}
 	\item{iqr}{Interquartile Range of the residuals. Ideally small.}
 % }