ggm_search: Perform Bayesian Graph Search and Optional Model Averaging

In BGGM: Bayesian Gaussian Graphical Models

Perform Bayesian Graph Search and Optional Model Averaging

Description

The 'ggm_search' function performs a Bayesian graph search to identify the most probable graph structure (MAP solution) using the Metropolis-Hastings algorithm. It also computes an optional Bayesian Model Averaged (BMA) solution across the graph structures sampled during the search.

Usage

ggm_search(
  x,
  n = NULL,
  method = "mc3",
  prior_prob = 0.3,
  iter = 5000,
  stop_early = 1000,
  bma_mean = TRUE,
  seed = NULL,
  progress = TRUE,
  ...
)

Arguments

x	Data, either raw data or covariance matrix
n	For x = covariance matrix, provide number of observations
method	mc3 defaults to MH sampling
prior_prob	Prior prbability of sparseness.
iter	Number of iterations #@param burn_in Burn in. Defaults to iter/2
stop_early	Default to 1000. Stop MH algorithm if proposals keep being rejected (stopping by default after 1000 rejections).
bma_mean	Compute Bayesian Model Averaged solution
seed	Set seed. Current default is to set R's random seed.
progress	Show progress bar, defaults to TRUE
...	Not currently in use

Details

This function is ideal for exploring the graph space and obtaining an initial estimate of the graph structure or adjacency matrix.

To refine the results or compute posterior distributions of graph parameters (e.g., partial correlations), use the bma_posterior function, which builds on the output of 'ggm_search' to account for parameter uncertainty.

Value

A list containing the MAP graph structure, BMA solution (if specified), and posterior probabilities of the sampled graphs.

Author(s)

Donny Williams and Philippe Rast

See Also

bma_posterior

BGGM documentation built on April 4, 2025, 2:30 a.m.