PLA: Percentage Lack of Accuracy (PLA)

In metrica: Prediction Performance Metrics

Percentage Lack of Accuracy (PLA)

Description

It estimates the PLA, the contribution of the systematic error to the Mean Squared Error (MSE) for a continuous predicted-observed dataset following Correndo et al. (2021).

Usage

PLA(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 PLA (%, 0-100) represents the contribution of the Mean Lack of Accuracy (MLA), the systematic (bias) component of the MSE. It is obtained via a symmetric decomposition of the MSE (invariant to predicted-observed orientation). The PLA can be further segregated into percentage additive bias (PAB) and percentage proportional bias (PPB). The greater the value the greater the contribution of systematic error to the MSE. 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. \Sexpr[results=rd]{tools:::Rd_expr_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)
PLA(obs = X, pred = Y)

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