ADSIHT (version 0.2.1)

MIGHT: MIGHT: Milti-task iterative graphical hard thresholding

Description

An implementation of the sparse group selection in joint graphical model.

Usage

MIGHT(
  X,
  ic.coef = 3,
  ic.scale = 3,
  L = 15,
  coef1 = 1,
  coef2 = 0.1,
  kappa = 0.9,
  eta = 0.8,
  center = TRUE,
  scale = 1,
  parallel = FALSE,
  ncpus = 4
)

Value

A list object containing the estimated precision matrices for each dataset.

Arguments

X

The list of input observation matrices.

ic.coef

A non-negative value used for multiplying the penalty term for choosing the optimal stopping time. Default: ic.coef = 3.

ic.scale

A non-negative value used for multiplying the penalty term in information criterion. Default: ic.scale = 3.

L

The length of the sequence of s0. Default: L = 15.

coef1

A positive value to control the sub-optimal stopping time.

coef2

A positive value to control the overall stopping time. A small value leads to larger search range.

kappa

A parameter that controls the rapid of the decrease of threshold. Default is 0.9.

eta

A parameter controls the step size in the gradient descent step. Default: eta = 0.8.

center

A boolean value indicating whether centralization is required. Default: center = TRUE.

scale

A positive value to control the column-wise L2 norm of each observation matrix. Default: scale=1.

parallel

A boolean value indicating whether parallel operation is required. Default: parallel = FALSE.

ncpus

A positive value that controls the numer of cpus. Default: ncpus = 4.

Author

Yanhang Zhang, Zhifan Li, Shixiang Liu, Jianxin Yin.

Examples

library(mvnfast)
set.seed(1)
n = 50; p = 10; K = 4
x_list <- lapply(1:K, function(x) rmvn(n, mu=rep(1, p),
                                       sigma = toeplitz( (x/2/K)^(1:p-1) ) ) )
fit = MIGHT(X=x_list, scale = 10)
solve( toeplitz( 0.5^(0:9) ) )
fit[[4]]