PPB {metrica} | R Documentation |
Percentage Proportional Bias (PPB)
Description
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. |
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
Value
an object of class numeric
within a list
(if tidy = FALSE) or within a
data frame
(if tidy = TRUE).
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
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)