| RAE {metrica} | R Documentation |
Relative Absolute Error (RAE)
Description
It estimates the RAC for a continuous predicted-observed dataset.
Usage
RAE(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 RAE normalizes the Mean Absolute Error (MAE) with respect to the total absolute error. It is calculated as the ratio between the sum of absolute residuals (error of predictions with respect to observations) and the sum of absolute errors of observations with respect to its mean. It presents its lower bound at 0 (perfect fit), and has no upper bound. It can be used to compare models using different units. 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).
Examples
set.seed(1)
X <- rnorm(n = 100, mean = 0, sd = 10)
Y <- X + rnorm(n=100, mean = 0, sd = 3)
RAE(obs = X, pred = Y)