| Theta.est {TransGraph} | R Documentation |
Sparse precision matrix estimation.
Description
The fast sparse precision matrix estimation in step 2(b).
Usage
Theta.est(S.hat.A, delta.hat, lam2=0.1, Omega.hat0=NULL,
n=100, max_iter=10, eps=1e-3, method = "cd")
Arguments
S.hat.A |
The sample covariance matrix. |
delta.hat |
The divergence matrix estimated in step 2(a). If the precision matrix is estimated in the common case (Liu and Luo, 2015, JMVA), it can be set to zero matrix. |
lam2 |
A float value, a tuning parameter. |
Omega.hat0 |
The initial values of the precision matrix, which can be unspecified. |
n |
The sample size. |
max_iter |
Int, maximum number of cycles of the algorithm. |
eps |
A float value, algorithm termination threshold. |
method |
The optimization algorithm, which can be selected as "admm" (ADMM algorithm) or "cd" (coordinate descent). |
Value
A result list including:
- Theta.hat.m
The optimal precision matrix.
- BIC.summary
The summary of BICs.
- Theta.hat.list.m
The precision matrices corresponding to a sequence of tuning parameters.
Author(s)
Mingyang Ren renmingyang17@mails.ucas.ac.cn.
References
Ren, M., Zhen Y., and Wang J. (2022). Transfer learning for tensor graphical models. Liu, W. and Luo X. (2015). Fast and adaptive sparse precision matrix estimation in high dimensions, Journal of Multivariate Analysis.
Examples
p = 20
n = 200
omega = diag(rep(1,p))
for (i in 1:p) {
for (j in 1:p) {
omega[i,j] = 0.3^(abs(i-j))*(abs(i-j) < 2)
}
}
Sigma = solve(omega)
X = MASS::mvrnorm(n, rep(0,p), Sigma)
S.hat.A = cov(X)
delta.hat = diag(rep(1,p)) - diag(rep(1,p))
omega.hat = Theta.est(S.hat.A, delta.hat, lam2=0.2)