LIC {LIC}

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)