It estimates the MAE for a continuous predicted-observed dataset.
Arguments
- data
(Optional) argument to call an existing data frame containing the data.
- obs
Vector with observed values (numeric).
- pred
Vector with predicted values (numeric).
- tidy
Logical operator (TRUE/FALSE) to decide the type of return. TRUE returns a data.frame, FALSE returns a list; Default : FALSE.
- na.rm
Logic argument to remove rows with missing values (NA). Default is na.rm = TRUE.
Value
an object of class numeric
within a list
(if tidy = FALSE) or within a
data frame
(if tidy = TRUE).
Details
The MAE measures both lack of accuracy and precision in absolute scale. It keeps the same units than the response variable. It is less sensitive to outliers than the MSE or RMSE. The lower the better. For the formula and more details, see online-documentation
References
Willmott & Matsuura (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim. Res. 30, 79–82. doi:10.3354/cr030079