| SB {metrica} | R Documentation |
Squared bias (SB)
Description
It estimates the SB component of the Mean Squared Error (MSE) proposed by Kobayashi & Salam (2000).
Usage
SB(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 SB represents the additive bias component of the prediction error following Kobayashi & Salam (2000). It is in square units of the variable of interest, so it does not have a direct interpretation. 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 <- rnorm(n = 100, mean = 0, sd = 9)
SB(obs = X, pred = Y)