NSE: Nash-Sutcliffe Model Efficiency (NSE)

In metrica: Prediction Performance Metrics

Nash-Sutcliffe Model Efficiency (NSE)

Description

It estimates the model efficiency suggested by Nash & Sutcliffe (1970) for a continuous predicted-observed dataset.

Usage

NSE(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 NSE measures general agreement. It is normalized (by the variance of the observations) and dimensionless. It is calculated using the absolute squared differences between the predictions and observations, which has been suggested as an issue due to over-sensitivity to outliers. It goes form -infinity to 1. The closer to 1 the better the prediction performance. 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

Nash & Sutcliffe (1970). River flow forecasting through conceptual models part I - A discussion of principles. J. Hydrol. 10(3), 292-290. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/0022-1694(70)90255-6")}

Examples

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

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