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Percentage Proportional Bias (PPB)

Source: R/reg_PPB.R
PPB.Rd

It estimates the contribution of the proportional bias to the Mean Squared Error (MSE) following Correndo et al. (2021).

Usage

PPB(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 PPB (%) measures the contribution of proportional bias to the MSE. The PPB = 100*(((sO - sP)^2)/MSE), where sO, and sP are the sample variances of observations and predictions, respectively. The greater the value the greater the contribution of proportional bias to the prediction error. For the formula and more details, see online-documentation

References

Correndo et al. (2021). Revisiting linear regression to test agreement in continuous predicted-observed datasets. Agric. Syst. 192, 103194. doi:10.1016/j.agsy.2021.103194

Examples

# \donttest{
set.seed(1)
X <- rnorm(n = 100, mean = 0, sd = 10)
Y <- X + rnorm(n=100, mean = 0, sd = 3)
PPB(obs = X, pred = Y)
#> $PPB
#> [1] 2.402532
#> 
# }