SILFS {SILFS} | R Documentation |
This function employs SILFS method under L2 distance and uses the Coordinate Descent Algorithm for optimization to effectively identify subgroup structures and perform variable selection.
SILFS(Y, X_aug, r, lam_CAR, lam_lasso, alpha_init, K, epsilon)
Y |
The response vector of length |
X_aug |
The augmented design matrix created by row concatenation of common and idiosyncratic factor matrices, with a size of |
r |
The user supplied number of common factors. |
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 user-supplied group number. |
epsilon |
The user-supplied stopping tolerance. |
A vector containing the following components:
alpha_m |
The estimated intercept parameter vector of length |
gamma |
The estimated vector of subgroup centers of length |
theta_m |
The estimated regression coefficient vector, matched with common factor terms, with a dimension of |
beta_m |
The estimated regression coefficients matched with idiosyncratic factors, with a dimension of |
Yong He, Liu Dong, Fuxin Wang, Mingjuan Zhang, Wenxin Zhou.
He, Y., Liu, D., Wang, F., Zhang, M., Zhou, W., 2024. High-Dimensional Subgroup Identification under Latent Factor Structures.
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,F_1,0.1)
SILFS(Y,cbind(F_1,U),3,0.01,0.05,alpha_init,K,0.3)