Functions to extract specific components from fitted bgms and bgmCompare objects. Executed examples on this page use a bgm() fit of the Wenchuan data (fit = bgm(Wenchuan, chains = 2, seed = 1234)).
Unless noted otherwise, extractor functions use one argument:
bgms_object: a fitted bgms or bgmCompare object.Retrieve the arguments used when fitting a model with bgm() or bgmCompare().
Returns a named list containing all arguments passed to the fitting function, including data dimensions, prior settings, and MCMC configuration.
Retrieve posterior mean main-effect parameters.
The structure depends on the model type:
bgms): NULL. GGM models have no main effects.bgms): A numeric matrix (p x max_categories) of posterior mean category thresholds. Columns beyond the number of categories for a variable are NA.bgms): A list with $discrete (threshold matrix) and $continuous (means matrix).bgmCompare: A matrix with one row per post-warmup iteration, containing posterior samples of baseline main-effect parameters.Retrieve posterior samples of partial association parameters.
Returns a matrix with one row per post-warmup iteration and one column per edge. For bgmCompare, columns correspond to baseline partial association parameters.
Retrieve posterior samples of inclusion indicators.
Returns a matrix with one row per post-warmup iteration and one column per indicator, containing binary (0/1) samples:
bgms: One column per edge. Requires edge_selection = TRUE.bgmCompare: Columns for main-effect and pairwise difference indicators. Requires difference_selection = TRUE.Compute posterior inclusion probabilities.
Returns a symmetric p x p matrix of posterior inclusion probabilities:
bgms: Off-diagonal entries are edge inclusion probabilities. Requires edge_selection = TRUE. intrusion dreams flash upset
intrusion 0.0 1.00 1.00 0.60
dreams 1.0 0.00 1.00 0.77
flash 1.0 1.00 0.00 0.02
upset 0.6 0.77 0.02 0.00Here the intrusion–dreams edge is included in every posterior draw (probability 1), while intrusion–upset is included in about 60% of draws — the data do not clearly decide that edge. See Edge Selection for turning these into Bayes factors. - bgmCompare: Diagonal entries are main-effect inclusion probabilities; off-diagonal entries are pairwise difference inclusion probabilities. Requires difference_selection = TRUE.
Retrieve the prior specification used for inclusion indicators.
Returns a named list describing the prior structure, including the prior type and hyperparameters:
bgms: Requires edge_selection = TRUE. Returns the prior type ("Bernoulli", "Beta-Bernoulli", or "Stochastic-Block") and associated hyperparameters.bgmCompare: Requires difference_selection = TRUE. Returns the difference prior specification.Compute group-specific parameter estimates by combining baseline parameters and group differences.
bgms_object must be a fitted bgmCompare object.
Returns a list with main_effects_groups (main effects per group) and pairwise_effects_groups (pairwise effects per group).
Retrieve posterior summaries from a model fitted with the Stochastic Block prior.
Works on both bgms and bgmCompare fits.
bgms: requires edge_selection = TRUE and edge_prior = sbm_prior(...).bgmCompare: requires difference_selection = TRUE and difference_prior = sbm_prior(...). The clustering applies to the off-diagonal (pairwise) difference inclusions.Returns a list with:
posterior_num_blocks — Posterior probabilities for each possible number of clusters.posterior_mean_allocations — Posterior mean cluster allocations.posterior_mode_allocations — Posterior mode cluster allocations.posterior_mean_coclustering_matrix — Co-clustering proportion matrix.Retrieve effective sample size estimates for all parameters.
Returns a named list with ESS values for each parameter type present in the model (e.g., main, pairwise, indicator). ESS values match coda::effectiveSize. For implementation details, see the Technical Manual.
intrusion-dreams intrusion-flash intrusion-upset intrusion-physior
848 624 47 152
intrusion-avoidth intrusion-avoidact
84 147 Low values relative to the total number of retained draws signal slow mixing for that parameter; see MCMC Diagnostics for guidance.
Retrieve R-hat convergence diagnostics for all parameters.
Returns a named list with R-hat values for each parameter type present in the model (e.g., main, pairwise, indicator). R-hat follows the Gelman-Rubin formula, matching coda::gelman.diag. For implementation details, see diagnostics in the Technical Manual.
Retrieve the posterior mean precision matrix.
bgms_object must be a fitted bgms object (GGM or mixed MRF).
Returns a symmetric p x p matrix (or the continuous block submatrix for mixed MRFs) containing the posterior mean precision matrix \(\boldsymbol{\Theta}\).
Retrieve the posterior mean partial correlation matrix.
bgms_object must be a fitted bgms object (GGM or mixed MRF).
Returns a symmetric matrix of partial correlations, computed by standardizing the precision matrix: \(\rho_{ij} = -\Theta_{ij} / \sqrt{\Theta_{ii} \Theta_{jj}}\).
Retrieve the posterior mean log-odds matrix.
bgms_object must be a fitted bgms object (OMRF or mixed MRF).
Returns a symmetric matrix of log adjacent-category odds ratios. For the ordinal MRF, \(\text{log-odds}_{ij} = 2 \omega_{ij}\).
extract_category_thresholds() — Renamed to extract_main_effects().extract_edge_indicators() — Renamed to extract_indicators().extract_pairwise_thresholds() — Renamed to extract_main_effects().