RSR: Root Mean Standard Deviation Ratio (RSR)

In metrica: Prediction Performance Metrics

Root Mean Standard Deviation Ratio (RSR)

Description

It estimates the MSE normalized by the standard deviation of observed values following Moriasi et al. (2007).

Usage

RSR(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 RSR normalizes the Root Mean Squared Error (RMSE) using the standard deviation of observed values. It goes from an optimal value of 0 to infinity. Based on RSR, Moriasi et al. (2007) indicates performance ratings as: i) very-good (0-0.50), ii) good (0.50-0.60), iii) satisfactory (0.60-0.70), or iv) unsatisfactory (>0.70). 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

Moriasi et al. (2007). Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. Trans. ASABE 50, 885–900. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.13031/2013.23153")}

Examples

set.seed(1)
X <- rnorm(n = 100, mean = 0, sd = 10)
Y <- X + rnorm(n=100, mean = 0, sd = 3)
RSR(obs = X, pred = Y)

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