| LCS {metrica} | R Documentation |
Lack of Correlation (LCS)
Description
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. |
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
Value
an object of class numeric within a list (if tidy = FALSE) or within a
data frame (if tidy = TRUE).
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
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)