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Percentage Bias Error (PBE).

Source: R/reg_PBE.R
PBE.Rd

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.

Value

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

References

Gupta et al. (1999). Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration. J. Hydrologic Eng. 4(2): 135-143. doi:10.1061/(ASCE)1084-0699(1999)4:2(135)

Examples

# \donttest{
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)
#> $PBE
#> [1] 10.41666
#> 
# }