Skip to contents

Mean Absolute Error (MAE)

Source: R/reg_MAE.R
MAE.Rd

It estimates the MAE for a continuous predicted-observed dataset.

Usage

MAE(data = NULL, obs, pred, tidy = FALSE, na.rm = TRUE)

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

Examples

# \donttest{
set.seed(1)
X <- rnorm(n = 100, mean = 0, sd = 10)
Y <- X + rnorm(n=100, mean = 0, sd = 3)
MAE(obs = X, pred = Y)
#> $MAE
#> [1] 2.283581
#> 
# }