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Percentage Lack of Precision (PLP)

Source: R/reg_PLP.R
PLP.Rd

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.

Value

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

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)
PLP(obs = X, pred = Y)
#> $PLP
#> [1] 97.44035
#> 
# }