LIC: The LIC criterion is to determine the most informative...

In LIC: The LIC Criterion for Optimal Subset Selection

The LIC criterion is to determine the most informative subsets so that the subset can retain most of the information contained in the complete data.

Description

The LIC criterion is to determine the most informative subsets so that the subset can retain most of the information contained in the complete data.

Usage

LIC(X, Y, alpha, K, nk)

Arguments

X	is a design matrix
Y	is a random response vector of observed values
alpha	is the significance level
K	is the number of subsets
nk	is the sample size of subsets

Value

MUopt,Bopt,MAEMUopt,MSEMUopt,opt,Yopt

Examples

set.seed(12)
X=matrix(data=sample(1:3,1200*5, replace = TRUE) ,nrow=1200,ncol=5)  
b=sample(1:3,5, replace = TRUE)         
e= rnorm(1200, 0, 1)    
Y=X%*%b+e
alpha=0.05	
K=10
nk=1200/K 
LIC(X,Y,alpha,K,nk)

LIC documentation built on March 28, 2022, 9:12 a.m.