Skip to contents

Relative Mean Absolute Error (RMAE)

Source: R/reg_RMAE.R
RMAE.Rd

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

Usage

RMAE(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 RMAE normalizes the Mean Absolute Error (MAE) by the mean of observations. The closer to zero the lower the prediction error. For the formula and more details, see online-documentation

Examples

# \donttest{
set.seed(1)
X <- rnorm(n = 100, mean = 0, sd = 10)
Y <- X + rnorm(n=100, mean = 0, sd = 3)
RMAE(obs = X, pred = Y)
#> $RMAE
#> [1] 2.097195
#> 
# }