MAE: Mean Absolute Error (MAE)

In metrica: Prediction Performance Metrics

Mean Absolute Error (MAE)

Description

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.

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

Value

an object of class numeric within a list (if tidy = FALSE) or within a ⁠data frame⁠ (if tidy = TRUE).

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3354/cr030079")}

Examples

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)

metrica documentation built on June 30, 2024, 5:07 p.m.