Lack of Correlation (LCS) — LCS • metrica

It estimates the lack of correlation (LCS) component of the Mean Squared Error (MSE) proposed by Kobayashi & Salam (2000).

Usage

LCS(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 LCS represents the random component of the prediction error following Kobayashi & Salam (2000). The lower the value the less contribution to the MSE. However, it needs to be compared to MSE as its benchmark. For the formula and more details, see online-documentation

References

Kobayashi & Salam (2000). Comparing simulated and measured values using mean squared deviation and its components. Agron. J. 92, 345–352. doi:10.2134/agronj2000.922345x

Examples

# \donttest{
set.seed(1)
X <- rnorm(n = 100, mean = 0, sd = 10)
Y <- X + rnorm(n=100, mean = 0, sd = 3)
LCS(obs = X, pred = Y)
#> $LCS
#> [1] 7.978492
#> 
# }