Erel: Relative Model Efficiency (Erel)

In metrica: Prediction Performance Metrics

Relative Model Efficiency (Erel)

Description

It estimates the Erel model efficiency using differences to observations.

Usage

Erel(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 Erel model efficiency normalizes both residuals (numerator) and observed deviations (denominator) by observed values before squaring them. Compared to the NSE, the Erel is suggested as more sensitive to systematic over- or under-predictions. 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

Krause et al. (2005). Comparison of different efficiency criteria for hydrological model assessment. Adv. Geosci. 5, 89–97. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.5194/adgeo-5-89-2005")}

Examples

set.seed(1)
X <- rnorm(n = 100, mean = 0, sd = 10)
Y <- rnorm(n = 100, mean = 0, sd = 9)
Erel(obs = X, pred = Y)

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