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Kling-Gupta Model Efficiency (KGE).

Source: R/reg_KGE.R
KGE.Rd

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.

Value

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

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. doi:10.1016/j.jhydrol.2012.01.011

Examples

# \donttest{
set.seed(1)
X <- rnorm(n = 100, mean = 0, sd = 10)
Y <- rnorm(n = 100, mean = 0, sd = 9)
KGE(obs = X, pred = Y)
#> $KGE
#> [1] -3.393248
#> 
# }