PBE: Percentage Bias Error (PBE).

In metrica: Prediction Performance Metrics

Percentage Bias Error (PBE).

Description

It estimates the PBE for a continuous predicted-observed dataset following Gupta et al. (1999).

Usage

PBE(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 PBE (%) is useful to identify systematic over or under predictions. It is an unbounded metric. The closer to zero the bias of predictions. Negative values indicate overestimation, while positive values indicate underestimation. 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

Gupta et al. (1999). Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration. J. Hydrologic Eng. 4(2): 135-143. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1061/(ASCE)1084-0699(1999)4:2(135)")}

Examples

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

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