LLIC {LLIC} | R Documentation |
LLIC for Lre Model
Description
This function carries out an Laplace LIC analysis utilizing the Lre model.
Usage
LLIC(X, y, alpha, K)
Arguments
X |
Design matrix |
y |
Random response vector of observed values |
alpha |
Significance level |
K |
Number of subsets |
Value
A list containing the following components:
MUopt |
A vector of the means of the predictor variables in the optimal subset. |
Bopt |
A vector of the estimated regression coefficients from the final model fitted to the optimal subset. |
MAEMUopt |
The Mean Absolute Error (MAE) for the optimal subset. |
MSEMUopt |
The Mean Squared Error (MSE) for the optimal subset. |
opt |
Currently NULL, a placeholder for potential future use. |
Yopt |
A vector of the predicted values from the final model fitted to the optimal subset. |
Examples
set.seed(12)
library(VGAM)
X <- matrix(data = sample(1:3, 1200 * 5, replace = TRUE), nrow = 1200, ncol = 5)
b <- sample(1:3, 5, replace = TRUE)
e <- rlaplace(1200, 0, 1)
Y <- X %*% b + e
alpha <- 0.05
K <- 10
result <- LLIC(X, Y, alpha, K)
MUopt <- result$MUopt
Bopt <- result$Bopt
MAEMUopt <- result$MAEMUopt
MSEMUopt <- result$MSEMUopt
opt <- result$opt
Yopt <- result$Yopt
[Package LLIC version 3.0.0 Index]