LIC {LIC} | R Documentation |
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.
LIC(X, Y, alpha, K, nk)
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 |
MUopt,Bopt,MAEMUopt,MSEMUopt,opt,Yopt
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)