jewel_inner: Estimate Gaussian graphical models from multiple datasets

In jewel: Graphical Models Estimation from Multiple Sources

Estimate Gaussian graphical models from multiple datasets

Description

This function estimates Gaussian graphical models (i.e. networks of conditional dependencies, direct connections between variables) given several datasets. We assume that datasets contain measurements of the same variables collected under different conditions (different equipment, locations, even sub-types of disease).

Usage

jewel_inner(
  X,
  lambda1,
  lambda2 = NULL,
  Theta = NULL,
  W = NULL,
  tol = 0.01,
  maxIter = 10000,
  verbose = TRUE
)

Arguments

X	a list of K numeric data matrices of n_k samples and p variables (n_k can be different for each matrix).
lambda1	a number, first regularization parameter (of the common penalty).
lambda2	an optional number, second regularization parameter (of the class-specific penalty). If NULL, set to lambda_2 = lambda_1 * 1.4
Theta	an optional list of K regression coefficient matrices of the size p by p. User-provided initialization can be used for warm-start procedures. If NULL, initialized as all zeros.
W	an optional list of K weights matrices of the size p by p. User-provided initialization can be used when some vertices are believed to be hubs. If NULL, initialized as all ones.
tol	an optional number, convergence threshold controlling the relative error between iterations. The default value is 0.01.
maxIter	an optional number, maximum allowed number of iterations. The default value is 10 000.
verbose	if verbose = FALSE, tracing information printing is disabled. The default value is TRUE.

Value

The following list is returned

• CommonG - an adjacency matrix of the common estimated graph (intersection of K estimated graphs).

• G_list - a list of K adjacency matrices for each estimated graph.

• Theta - a list of K estimated covariance matrices.

• BIC – a number, value of Bayesian information criterion for resulting graphs.

jewel documentation built on May 29, 2024, 2:54 a.m.