KGE: Kling-Gupta Model Efficiency (KGE).

In metrica: Prediction Performance Metrics

Kling-Gupta Model Efficiency (KGE).

Description

It estimates the KGE for a predicted-observed dataset.

Usage

KGE(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 KGE is a normalized, dimensionless, model efficiency that measures general agreement. It presents accuracy, precision, and consistency components. It is symmetric (invariant to predicted observed orientation). It is positively bounded up to 1. The closer to 1 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

Kling et al. (2012). Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios. Journal of Hydrology 424-425, 264-277. \Sexpr[results=rd]{tools:::Rd_expr_doi("doi:10.1016/j.jhydrol.2012.01.011")}

Examples

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

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