SCAR {SILFS} | R Documentation |
Standard Center Augmented Regularization (S-CAR) Method for Subgroup Identification and Variable Selection
Description
This function employs the S-CAR method under L2 distance and uses the Coordinate Descent Algorithm for optimization to identify subgroup structures and execute variable selection.
Usage
SCAR(Y, X, lam_CAR, lam_lasso, alpha_init, K, epsilon)
Arguments
Y |
The response vector of length |
X |
The design matrix of size |
lam_CAR |
The tuning parameter for Center-Augmented Regularization. |
lam_lasso |
The tuning parameter for lasso. |
alpha_init |
The initialization of intercept parameter. |
K |
The estimated group number. |
epsilon |
The user-supplied stopping tolerance. |
Value
A list with the following components:
alpha_m |
The estimated intercept parameter vector of length |
gamma |
The estimated vector of subgroup centers of length |
beta_m |
The estimated regression coefficient vector of dimension |
Author(s)
Yong He, Liu Dong, Fuxin Wang, Mingjuan Zhang, Wenxin Zhou.
Examples
n <- 50
p <- 50
r <- 3
K <- 2
alpha <- sample(c(-3,3),n,replace=TRUE,prob=c(1/2,1/2))
beta <- c(rep(1,2),rep(0,48))
B <- matrix((rnorm(p*r,1,1)),p,r)
F_1 <- matrix((rnorm(n*r,0,1)),n,r)
U <- matrix(rnorm(p*n,0,0.1),n,p)
X <- F_1%*%t(B)+U
Y <- alpha + X%*%beta + rnorm(n,0,0.5)
alpha_init <- INIT(Y,X,0.1)
SCAR(Y,X,0.01,0.05,alpha_init,K,0.3)
[Package SILFS version 0.1.0 Index]