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Root Mean Standard Deviation Ratio (RSR)

Source: R/reg_RSR.R
RSR.Rd

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.

Value

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

References

Moriasi et al. (2007). Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. Trans. ASABE 50, 885–900. doi:10.13031/2013.23153

Examples

# \donttest{
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)
#> $RSR
#> [1] 0.03582699
#> 
# }