PLP: Percentage Lack of Precision (PLP)

In metrica: Prediction Performance Metrics

Percentage Lack of Precision (PLP)

Description

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

Usage

PLP(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 PLP (%, 0-100) represents the contribution of the Mean Lack of Precision (MLP), the unsystematic (random) component of the MSE. It is obtained via a symmetric decomposition of the MSE (invariant to predicted-observed orientation). The greater the value the greater the contribution of unsystematic 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)
PLP(obs = X, pred = Y)

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