Type:	Package
Title:	Bayesian Analysis of Networks of Binary and/or Ordinal Variables
Version:	0.1.6.0
Date:	2025-09-26
Maintainer:	Maarten Marsman <m.marsman@uva.nl>
Description:	Bayesian variable selection methods for analyzing the structure of a Markov random field model for a network of binary and/or ordinal variables.
Copyright:	Includes datasets 'ADHD' and 'Boredom', which are licensed under CC-BY 4. See individual data documentation for license and citation.
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://Bayesian-Graphical-Modelling-Lab.github.io/bgms/
BugReports:	https://github.com/Bayesian-Graphical-Modelling-Lab/bgms/issues
Imports:	Rcpp (≥ 1.0.7), RcppParallel, Rdpack, methods, coda, lifecycle
RdMacros:	Rdpack
LinkingTo:	Rcpp, RcppArmadillo, RcppParallel, dqrng, BH
RoxygenNote:	7.3.3
Depends:	R (≥ 3.5)
LazyData:	true
Encoding:	UTF-8
Suggests:	ggplot2, knitr, parallel, qgraph, rmarkdown, testthat (≥ 3.0.0)
VignetteBuilder:	knitr
Config/testthat/edition:	3
Config/Needs/website:	tidyverse/tidytemplate
NeedsCompilation:	yes
Packaged:	2025-09-27 18:24:39 UTC; maartenmarsman
Author:	Maarten Marsman [aut, cre], Giuseppe Arena [ctb], Karoline Huth [ctb], Nikola Sekulovski [ctb], Don van den Bergh [ctb]
Repository:	CRAN
Date/Publication:	2025-09-27 19:10:02 UTC

bgms: Bayesian Analysis of Networks of Binary and/or Ordinal Variables

Description

The R package bgms provides tools for Bayesian analysis of the ordinal Markov random field (MRF), a graphical model describing networks of binary and/or ordinal variables (Marsman et al. 2025). The likelihood is approximated via a pseudolikelihood, and Markov chain Monte Carlo (MCMC) methods are used to sample from the corresponding pseudoposterior distribution of model parameters.

The main entry points are:

• bgm: estimation in a one-sample design.

• bgmCompare: estimation and group comparison in an independent-sample design.

Both functions support Bayesian effect selection with spike-and-slab priors.

• In one-sample designs, bgm models the presence or absence of edges between variables. Posterior inclusion probabilities quantify the plausibility of each edge and can be converted into Bayes factors for conditional independence tests.

• bgm can also model communities (clusters) of variables. The posterior distribution of the number of clusters provides evidence for or against clustering (Sekulovski et al. 2025).

• In independent-sample designs, bgmCompare estimates group differences in edge weights and category thresholds. Posterior inclusion probabilities quantify the evidence for differences and can be converted into Bayes factors for parameter equivalence tests (Marsman et al. 2024).

Tools

The package also provides:

1. Simulation of response data from MRFs with a Gibbs sampler (mrfSampler).

2. Posterior estimation and edge selection in one-sample designs (bgm).

3. Posterior estimation and group-difference selection in independent-sample designs (bgmCompare).

Vignettes

For tutorials and worked examples, see:

• vignette("intro", package = "bgms") — Getting started.

• vignette("comparison", package = "bgms") — Model comparison.

• vignette("diagnostics", package = "bgms") — Diagnostics and spike-and-slab summaries.

Author(s)

Maintainer: Maarten Marsman m.marsman@uva.nl (ORCID)

Other contributors:

• Giuseppe Arena (ORCID) [contributor]

• Karoline Huth (ORCID) [contributor]

• Nikola Sekulovski (ORCID) [contributor]

• Don van den Bergh (ORCID) [contributor]

References

Marsman M, Waldorp LJ, Sekulovski N, Haslbeck JMB (2024). “Bayes factor tests for group differences in ordinal and binary graphical models.” Retrieved from https://osf.io/preprints/osf/f4pk9. OSF preprint.

Marsman M, van den Bergh D, Haslbeck JMB (2025). “Bayesian analysis of the ordinal Markov random field.” Psychometrika, 90, 146–-182.

Sekulovski N, Arena G, Haslbeck JMB, Huth KBS, Friel N, Marsman M (2025). “A Stochastic Block Prior for Clustering in Graphical Models.” Retrieved from https://osf.io/preprints/psyarxiv/29p3m_v1. OSF preprint.

See Also

Useful links:

• https://Bayesian-Graphical-Modelling-Lab.github.io/bgms/

• Report bugs at https://github.com/Bayesian-Graphical-Modelling-Lab/bgms/issues

ADHD Symptom Checklist for Children Aged 6–8 Years

Description

This dataset includes ADHD symptom ratings for 355 children aged 6 to 8 years from the Children’s Attention Project (CAP) cohort (Silk et al. 2019). The sample consists of 146 children diagnosed with ADHD and 209 without a diagnosis. Symptoms were assessed through structured interviews with parents using the NIMH Diagnostic Interview Schedule for Children IV (DISC-IV) (Shaffer et al. 2000). The checklist includes 18 items: 9 Inattentive (I) and 9 Hyperactive/Impulsive (HI). Each item is binary (1 = present, 0 = absent).

Usage

data("ADHD")

Format

A matrix with 355 rows and 19 columns.

group

ADHD diagnosis: 1 = diagnosed, 0 = not diagnosed

avoid

Often avoids, dislikes, or is reluctant to engage in tasks that require sustained mental effort (I)

closeatt

Often fails to give close attention to details or makes careless mistakes in schoolwork, work, or other activities (I)

distract

Is often easily distracted by extraneous stimuli (I)

forget

Is often forgetful in daily activities (I)

instruct

Often does not follow through on instructions and fails to finish schoolwork, chores, or duties in the workplace (I)

listen

Often does not seem to listen when spoken to directly (I)

loses

Often loses things necessary for tasks or activities (I)

org

Often has difficulty organizing tasks and activities (I)

susatt

Often has difficulty sustaining attention in tasks or play activities (I)

blurts

Often blurts out answers before questions have been completed (HI)

fidget

Often fidgets with hands or feet or squirms in seat (HI)

interrupt

Often interrupts or intrudes on others (HI)

motor

Is often "on the go" or often acts as if "driven by a motor" (HI)

quiet

Often has difficulty playing or engaging in leisure activities quietly (HI)

runs

Often runs about or climbs excessively in situations in which it is inappropriate (HI)

seat

Often leaves seat in classroom or in other situations in which remaining seated is expected (HI)

talks

Often talks excessively (HI)

turn

Often has difficulty awaiting turn (HI)

Source

Silk et al. (2019). Data retrieved from doi:10.1371/journal.pone.0211053.s004. Licensed under the CC-BY 4.0: https://creativecommons.org/licenses/by/4.0/

References

Shaffer D, Fisher P, Lucas CP, Dulcan MK, Schwab-Stone ME (2000). “NIMH Diagnostic Interview Schedule for Children Version IV (NIMH DISC-IV): description, differences from previous versions, and reliability of some common diagnoses.” Journal of the American Academy of Child & Adolescent Psychiatry, 39, 28–38. doi:10.1097/00004583-200001000-00014, PMID: 10638065.

Silk TJ, Malpas CB, Beare R, Efron D, Anderson V, Hazell P, Jongeling B, Nicholson JM, Sciberras E (2019). “A network analysis approach to ADHD symptoms: More than the sum of its parts.” PLOS ONE, 14(1), e0211053. doi:10.1371/journal.pone.0211053.

Short Boredom Proneness Scale Responses

Description

This dataset includes responses to the 8-item Short Boredom Proneness Scale (SBPS), a self-report measure of an individual's susceptibility to boredom (Martarelli et al. 2023). Items were rated on a 7-point Likert scale ranging from 1 ("strongly disagree") to 7 ("strongly agree"). The scale was administered in either English (Struk et al. 2015) or French (translated by (Martarelli et al. 2023)).

Usage

data("Boredom")

Format

A matrix with 986 rows and 9 columns. Each row corresponds to a respondent.

language

Language in which the SBPS was administered: "en" = English, "fr" = French

loose_ends

I often find myself at “loose ends,” not knowing what to do.

entertain

I find it hard to entertain myself.

repetitive

Many things I have to do are repetitive and monotonous.

stimulation

It takes more stimulation to get me going than most people.

motivated

I don't feel motivated by most things that I do.

keep_interest

In most situations, it is hard for me to find something to do or see to keep me interested.

sit_around

Much of the time, I just sit around doing nothing.

half_dead_dull

Unless I am doing something exciting, even dangerous, I feel half-dead and dull.

Source

Martarelli et al. (2023). Data retrieved from https://osf.io/qhux8. Licensed under the CC-BY 4.0: https://creativecommons.org/licenses/by/4.0/

References

Martarelli CS, Baillifard A, Audrin C (2023). “A Trait-Based Network Perspective on the Validation of the French Short Boredom Proneness Scale.” European Journal of Psychological Assessment, 39(6), 390–399. doi:10.1027/1015-5759/a000718.

Struk AA, Carriere JSA, Cheyne JA, Danckert J (2015). “A Short Boredom Proneness Scale: Development and Psychometric Properties.” Assessment, 24(3), 346–359. doi:10.1177/1073191115609996.

PTSD Symptoms in Wenchuan Earthquake Survivors Who Lost a Child

Description

This dataset contains responses to 17 items assessing symptoms of post-traumatic stress disorder (PTSD) in Chinese adults who survived the 2008 Wenchuan earthquake and lost at least one child in the disaster (McNally et al. 2015). Participants completed the civilian version of the Posttraumatic Checklist, with each item corresponding to a DSM-IV PTSD symptom. Items were rated on a 5-point Likert scale from "not at all" to "extremely," indicating the degree to which the symptom bothered the respondent in the past month.

Usage

data("Wenchuan")

Format

A matrix with 362 rows and 17 columns. Each row represents a participant.

intrusion

Repeated, disturbing memories, thoughts, or images of a stressful experience from the past?

dreams

Repeated, disturbing dreams of a stressful experience from the past?

flash

Suddenly acting or feeling as if a stressful experience were happening again (as if you were reliving it)?

upset

Feeling very upset when something reminded you of a stressful experience from the past?

physior

Having physical reactions (e.g., heart pounding, trouble breathing, sweating) when something reminded you of a stressful experience from the past?

avoidth

Avoiding thinking about or talking about a stressful experience from the past or avoiding having feelings related to it?

avoidact

Avoiding activities or situations because they reminded you of a stressful experience from the past?

amnesia

Trouble remembering important parts of a stressful experience from the past?

lossint

Loss of interest in activities that you used to enjoy?

distant

Feeling distant or cut off from other people?

numb

Feeling emotionally numb or being unable to have loving feelings for those close to you?

future

Feeling as if your future will somehow be cut short?

sleep

Trouble falling or staying asleep?

anger

Feeling irritable or having angry outbursts?

concen

Having difficulty concentrating?

hyper

Being "super-alert" or watchful or on guard?

startle

Feeling jumpy or easily startled?

Source

https://psychosystems.org/wp-content/uploads/2014/10/Wenchuan.csv

References

McNally RJ, Robinaugh DJ, Wu GWY, Wang L, Deserno MK, Borsboom D (2015). “Mental disorders as causal systems: A network approach to posttraumatic stress disorder.” Clinical Psychological Science, 6, 836–849. doi:10.1177/2167702614553230.

Bayesian Estimation or Edge Selection for Markov Random Fields

Description

The bgm function estimates the pseudoposterior distribution of category thresholds (main effects) and pairwise interaction parameters of a Markov Random Field (MRF) model for binary and/or ordinal variables. Optionally, it performs Bayesian edge selection using spike-and-slab priors to infer the network structure.

Usage

bgm(
  x,
  variable_type = "ordinal",
  baseline_category,
  iter = 1000,
  warmup = 1000,
  pairwise_scale = 2.5,
  main_alpha = 0.5,
  main_beta = 0.5,
  edge_selection = TRUE,
  edge_prior = c("Bernoulli", "Beta-Bernoulli", "Stochastic-Block"),
  inclusion_probability = 0.5,
  beta_bernoulli_alpha = 1,
  beta_bernoulli_beta = 1,
  dirichlet_alpha = 1,
  lambda = 1,
  na_action = c("listwise", "impute"),
  update_method = c("nuts", "adaptive-metropolis", "hamiltonian-mc"),
  target_accept,
  hmc_num_leapfrogs = 100,
  nuts_max_depth = 10,
  learn_mass_matrix = FALSE,
  chains = 4,
  cores = parallel::detectCores(),
  display_progress = c("per-chain", "total", "none"),
  seed = NULL,
  interaction_scale,
  burnin,
  save,
  threshold_alpha,
  threshold_beta
)

Arguments

Details

This function models the joint distribution of binary and ordinal variables using a Markov Random Field, with support for edge selection through Bayesian variable selection. The statistical foundation of the model is described in Marsman et al. (2025), where the ordinal MRF model and its Bayesian estimation procedure were first introduced. While the implementation in bgms has since been extended and updated (e.g., alternative priors, parallel chains, HMC/NUTS warmup), it builds on that original framework.

Key components of the model are described in the sections below.

Value

A list of class "bgms" with posterior summaries, posterior mean matrices, and access to raw MCMC draws. The object can be passed to print(), summary(), coef(), and as_draws() methods for inspection and analysis.

Main components include:

• posterior_summary_main: Data frame with posterior summaries (mean, sd, MCSE, ESS, Rhat) for category threshold parameters.

• posterior_summary_pairwise: Data frame with posterior summaries for pairwise interaction parameters.

• posterior_summary_indicator: Data frame with posterior summaries for edge inclusion indicators (if edge_selection = TRUE).

• posterior_mean_main: Matrix of posterior mean thresholds (rows = variables, cols = categories or parameters).

• posterior_mean_pairwise: Symmetric matrix of posterior mean pairwise interaction strengths.

• posterior_mean_indicator: Symmetric matrix of posterior mean inclusion probabilities (if edge selection was enabled).

• Additional summaries returned when edge_prior = "Stochastic-Block". For more details about this prior see Sekulovski et al. (2025).

  • posterior_summary_pairwise_allocations: Data frame with posterior summaries (mean, sd, MCSE, ESS, Rhat) for the pairwise cluster co-occurrence of the nodes. This serves to indicate whether the estimated posterior allocations,co-clustering matrix and posterior cluster probabilities (see blow) have converged.

  • posterior_coclustering_matrix: a symmetric matrix of pairwise proportions of occurrence of every variable. This matrix can be plotted to visually inspect the estimated number of clusters and visually inspect nodes that tend to switch clusters.

  • posterior_mean_allocations: A vector with the posterior mean of the cluster allocations of the nodes. This is calculated using the method proposed in Dahl (2009).

  • posterior_mode_allocations: A vector with the posterior mode of the cluster allocations of the nodes.

  • posterior_num_blocks: A data frame with the estimated posterior inclusion probabilities for all the possible number of clusters.

• raw_samples: A list of raw MCMC draws per chain:

  main

  List of main effect samples.

  pairwise

  List of pairwise effect samples.

  indicator

  List of indicator samples (if edge selection enabled).

  allocations

  List of cluster allocations (if SBM prior used).

  nchains

  Number of chains.

  niter

  Number of post–warmup iterations per chain.

  parameter_names

  Named lists of parameter labels.

• arguments: A list of function call arguments and metadata (e.g., number of variables, warmup, sampler settings, package version).

The summary() method prints formatted posterior summaries, coef() extracts posterior mean matrices, and as_draws() converts the raw samples into a posterior::draws_df object for use with the posterior package.

NUTS diagnostics (tree depth, divergences, energy, E-BFMI) are included in fit$nuts_diag if update_method = "nuts".

Ordinal Variables

The function supports two types of ordinal variables:

Regular ordinal variables: Assigns a category threshold parameter to each response category except the lowest. The model imposes no additional constraints on the distribution of category responses.

Blume-Capel ordinal variables: Assume a baseline category (e.g., a “neutral” response) and score responses by distance from this baseline. Category thresholds are modeled as:

\mu_{c} = \alpha \cdot c + \beta \cdot (c - b)^2

where:

• \mu_{c}: category threshold for category c

• \alpha: linear trend across categories

• \beta: preference toward or away from the baseline

  • If \beta < 0, the model favors responses near the baseline category;

  • if \beta > 0, it favors responses farther away (i.e., extremes).

• b: baseline category

Edge Selection

When edge_selection = TRUE, the function performs Bayesian variable selection on the pairwise interactions (edges) in the MRF using spike-and-slab priors.

Supported priors for edge inclusion:

• Bernoulli: Fixed inclusion probability across edges.

• Beta-Bernoulli: Inclusion probability is assigned a Beta prior distribution.

• Stochastic-Block: Cluster-based edge priors with Beta, Dirichlet, and Poisson hyperpriors.

All priors operate via binary indicator variables controlling the inclusion or exclusion of each edge in the MRF.

Prior Distributions

• Pairwise effects: Modeled with a Cauchy (slab) prior.

• Main effects: Modeled using a beta-prime distribution.

• Edge indicators: Use either a Bernoulli, Beta-Bernoulli, or Stochastic-Block prior (as above).

Sampling Algorithms and Warmup

Parameters are updated within a Gibbs framework, but the conditional updates can be carried out using different algorithms:

• Adaptive Metropolis–Hastings: Componentwise random–walk updates for main effects and pairwise effects. Proposal standard deviations are adapted during burn–in via Robbins–Monro updates toward a target acceptance rate.

• Hamiltonian Monte Carlo (HMC): Joint updates of all parameters using fixed–length leapfrog trajectories. Step size is tuned during warmup via dual–averaging; the diagonal mass matrix can also be adapted if learn_mass_matrix = TRUE.

• No–U–Turn Sampler (NUTS): An adaptive extension of HMC that dynamically chooses trajectory lengths. Warmup uses a staged adaptation schedule (fast–slow–fast) to stabilize step size and, if enabled, the mass matrix.

When edge_selection = TRUE, updates of edge–inclusion indicators are carried out with Metropolis–Hastings steps. These are switched on after the core warmup phase, ensuring that graph updates occur only once the samplers’ tuning parameters (step size, mass matrix, proposal SDs) have stabilized.

After warmup, adaptation is disabled. Step size and mass matrix are fixed at their learned values, and proposal SDs remain constant.

Warmup and Adaptation

The warmup procedure in bgm is based on the multi–stage adaptation schedule used in Stan (Stan Development Team 2023). Warmup iterations are split into several phases:

• Stage 1 (fast adaptation): A short initial interval where only step size (for HMC/NUTS) is adapted, allowing the chain to move quickly toward the typical set.

• Stage 2 (slow windows): A sequence of expanding, memoryless windows where both step size and, if learn_mass_matrix = TRUE, the diagonal mass matrix are adapted. Each window ends with a reset of the dual–averaging scheme for improved stability.

• Stage 3a (final fast interval): A short interval at the end of the core warmup where the step size is adapted one final time.

• Stage 3b (proposal–SD tuning): Only active when edge_selection = TRUE under HMC/NUTS. In this phase, Robbins–Monro adaptation of proposal standard deviations is performed for the Metropolis steps used in edge–selection moves.

• Stage 3c (graph selection warmup): Also only relevant when edge_selection = TRUE. At the start of this phase, a random graph structure is initialized, and Metropolis–Hastings updates for edge inclusion indicators are switched on.

When edge_selection = FALSE, the total number of warmup iterations equals the user–specified burnin. When edge_selection = TRUE and update_method is "nuts" or "hamiltonian-mc", the schedule automatically appends additional Stage-3b and Stage-3c intervals, so the total warmup is strictly greater than the requested burnin.

After all warmup phases, the sampler transitions to the sampling phase with adaptation disabled. Step size and mass matrix (for HMC/NUTS) are fixed at their learned values, and proposal SDs remain constant.

This staged design improves stability of proposals and ensures that both local parameters (step size) and global parameters (mass matrix, proposal SDs) are tuned before collecting posterior samples.

For adaptive Metropolis–Hastings runs, step size and mass matrix adaptation are not relevant. Proposal SDs are tuned continuously during burn–in using Robbins–Monro updates, without staged fast/slow intervals.

Missing Data

If na_action = "listwise", observations with missing values are removed. If na_action = "impute", missing values are imputed during Gibbs sampling.

References

Dahl DB (2009). “Modal clustering in a class of product partition models.” Bayesian Analysis, 4(2), 243–264. doi:10.1214/09-BA409.

Marsman M, van den Bergh D, Haslbeck JMB (2025). “Bayesian analysis of the ordinal Markov random field.” Psychometrika, 90, 146–-182.

Sekulovski N, Arena G, Haslbeck JMB, Huth KBS, Friel N, Marsman M (2025). “A Stochastic Block Prior for Clustering in Graphical Models.” Retrieved from https://osf.io/preprints/psyarxiv/29p3m_v1. OSF preprint.

Stan Development Team (2023). Stan Modeling Language Users Guide and Reference Manual. Version 2.33, https://mc-stan.org/docs/.

See Also

vignette("intro", package = "bgms") for a worked example.

Examples

# Run bgm on subset of the Wenchuan dataset
fit = bgm(x = Wenchuan[, 1:5])

# Posterior inclusion probabilities
summary(fit)$indicator

# Posterior pairwise effects
summary(fit)$pairwise

Bayesian Estimation and Variable Selection for Group Differences in Markov Random Fields

Description

The bgmCompare function estimates group differences in category threshold parameters (main effects) and pairwise interactions (pairwise effects) of a Markov Random Field (MRF) for binary and ordinal variables. Groups can be defined either by supplying two separate datasets (x and y) or by a group membership vector. Optionally, Bayesian variable selection can be applied to identify differences across groups.

Usage

bgmCompare(
  x,
  y,
  group_indicator,
  difference_selection = TRUE,
  variable_type = "ordinal",
  baseline_category,
  difference_scale = 1,
  difference_prior = c("Bernoulli", "Beta-Bernoulli"),
  difference_probability = 0.5,
  beta_bernoulli_alpha = 1,
  beta_bernoulli_beta = 1,
  pairwise_scale = 2.5,
  main_alpha = 0.5,
  main_beta = 0.5,
  iter = 1000,
  warmup = 1000,
  na_action = c("listwise", "impute"),
  update_method = c("nuts", "adaptive-metropolis", "hamiltonian-mc"),
  target_accept,
  hmc_num_leapfrogs = 100,
  nuts_max_depth = 10,
  learn_mass_matrix = FALSE,
  chains = 4,
  cores = parallel::detectCores(),
  display_progress = c("per-chain", "total", "none"),
  seed = NULL,
  main_difference_model,
  reference_category,
  main_difference_scale,
  pairwise_difference_scale,
  pairwise_difference_prior,
  main_difference_prior,
  pairwise_difference_probability,
  main_difference_probability,
  pairwise_beta_bernoulli_alpha,
  pairwise_beta_bernoulli_beta,
  main_beta_bernoulli_alpha,
  main_beta_bernoulli_beta,
  interaction_scale,
  threshold_alpha,
  threshold_beta,
  burnin,
  save
)

Arguments

Details

This function extends the ordinal MRF framework Marsman et al. (2025) to multiple groups. The basic idea of modeling, analyzing, and testing group differences in MRFs was introduced in Marsman et al. (2024), where two–group comparisons were conducted using adaptive Metropolis sampling. The present implementation generalizes that approach to more than two groups and supports additional samplers (HMC and NUTS) with staged warmup adaptation.

Key components of the model:

Value

A list of class "bgmCompare" containing posterior summaries, posterior mean matrices, and raw MCMC samples:

• posterior_summary_main_baseline, posterior_summary_pairwise_baseline: summaries of baseline thresholds and pairwise interactions.

• posterior_summary_main_differences, posterior_summary_pairwise_differences: summaries of group differences in thresholds and pairwise interactions.

• posterior_summary_indicator: summaries of inclusion indicators (if difference_selection = TRUE).

• posterior_mean_main_baseline, posterior_mean_pairwise_baseline: posterior mean matrices (legacy style).

• raw_samples: list of raw draws per chain for main, pairwise, and indicator parameters.

• arguments: list of function call arguments and metadata.

The summary() method prints formatted summaries, coef() extracts posterior means, and as_draws() converts raw samples to a posterior draws_df.

NUTS diagnostics (tree depth, divergences, energy, E-BFMI) are included in fit$nuts_diag if update_method = "nuts".

Pairwise Interactions

For variables i and j, the group-specific interaction is represented as:

\theta_{ij}^{(g)} = \phi_{ij} + \delta_{ij}^{(g)},

where \phi_{ij} is the baseline effect and \delta_{ij}^{(g)} are group differences constrained to sum to zero.

Ordinal Variables

Regular ordinal variables: category thresholds are decomposed into a baseline plus group differences for each category.

Blume–Capel variables: category thresholds are quadratic in the category index, with both the linear and quadratic terms split into a baseline plus group differences.

Variable Selection

When difference_selection = TRUE, spike-and-slab priors are applied to difference parameters:

• Bernoulli: fixed prior inclusion probability.

• Beta–Bernoulli: inclusion probability given a Beta prior.

Sampling Algorithms and Warmup

Parameters are updated within a Gibbs framework, using the same sampling algorithms and staged warmup scheme described in bgm:

• Adaptive Metropolis–Hastings: componentwise random–walk proposals with Robbins–Monro adaptation of proposal SDs.

• Hamiltonian Monte Carlo (HMC): joint updates with fixed leapfrog trajectories; step size and optionally the mass matrix are adapted during warmup.

• No–U–Turn Sampler (NUTS): an adaptive HMC variant with dynamic trajectory lengths; warmup uses the same staged adaptation schedule as HMC.

For details on the staged adaptation schedule (fast–slow–fast phases), see bgm. In addition, when difference_selection = TRUE, updates of inclusion indicators are delayed until late warmup. In HMC/NUTS, this appends two extra phases (Stage-3b and Stage-3c), so that the total number of warmup iterations exceeds the user-specified warmup.

After warmup, adaptation is disabled: step size and mass matrix are fixed at their learned values, and proposal SDs remain constant.

References

Marsman M, Waldorp LJ, Sekulovski N, Haslbeck JMB (2024). “Bayes factor tests for group differences in ordinal and binary graphical models.” Retrieved from https://osf.io/preprints/osf/f4pk9. OSF preprint.

Marsman M, van den Bergh D, Haslbeck JMB (2025). “Bayesian analysis of the ordinal Markov random field.” Psychometrika, 90, 146–-182.

See Also

vignette("comparison", package = "bgms") for a worked example.

Examples

## Not run: 
# Run bgmCompare on subset of the Boredom dataset
x = Boredom[Boredom$language == "fr", 2:6]
y = Boredom[Boredom$language != "fr", 2:6]

fit <- bgmCompare(x, y)

# Posterior inclusion probabilities
summary(fit)$indicator

# Bayesian model averaged main effects for the groups
coef(fit)$main_effects_groups

# Bayesian model averaged pairwise effects for the groups
coef(fit)$pairwise_effects_groups

## End(Not run)

Extract Coefficients from a bgmCompare Object

Description

Returns posterior means for raw parameters (baseline + differences) and group-specific effects from a bgmCompare fit, as well as inclusion indicators.

Usage

## S3 method for class 'bgmCompare'
coef(object, ...)

Arguments

Value

A list with components:

main_effects_raw

Posterior means of the raw main-effect parameters (variables x [baseline + differences]).

pairwise_effects_raw

Posterior means of the raw pairwise-effect parameters (pairs x [baseline + differences]).

main_effects_groups

Posterior means of group-specific main effects (variables x groups), computed as baseline plus projected differences.

pairwise_effects_groups

Posterior means of group-specific pairwise effects (pairs x groups), computed as baseline plus projected differences.

indicators

Posterior mean inclusion probabilities as a symmetric matrix, with diagonals corresponding to main effects and off-diagonals to pairwise effects.

Extract Coefficients from a bgms Object

Description

Returns the posterior mean thresholds, pairwise effects, and edge inclusion indicators from a bgms model fit.

Usage

## S3 method for class 'bgms'
coef(object, ...)

Arguments

Value

A list with the following components:

main

Posterior mean of the category threshold parameters.

pairwise

Posterior mean of the pairwise interaction matrix.

indicator

Posterior mean of the edge inclusion indicators (if available).

Extractor Functions for bgms Objects

Description

Extractor Functions for bgms Objects

Usage

extract_arguments(bgms_object)

## S3 method for class 'bgms'
extract_arguments(bgms_object)

## S3 method for class 'bgmCompare'
extract_arguments(bgms_object)

extract_indicators(bgms_object)

## S3 method for class 'bgms'
extract_indicators(bgms_object)

## S3 method for class 'bgmCompare'
extract_indicators(bgms_object)

extract_posterior_inclusion_probabilities(bgms_object)

## S3 method for class 'bgms'
extract_posterior_inclusion_probabilities(bgms_object)

## S3 method for class 'bgmCompare'
extract_posterior_inclusion_probabilities(bgms_object)

extract_indicator_priors(bgms_object)

## S3 method for class 'bgms'
extract_indicator_priors(bgms_object)

## S3 method for class 'bgmCompare'
extract_indicator_priors(bgms_object)

extract_pairwise_interactions(bgms_object)

## S3 method for class 'bgms'
extract_pairwise_interactions(bgms_object)

## S3 method for class 'bgmCompare'
extract_pairwise_interactions(bgms_object)

extract_category_thresholds(bgms_object)

## S3 method for class 'bgms'
extract_category_thresholds(bgms_object)

## S3 method for class 'bgmCompare'
extract_category_thresholds(bgms_object)

extract_group_params(bgms_object)

## S3 method for class 'bgmCompare'
extract_group_params(bgms_object)

extract_edge_indicators(bgms_object)

extract_pairwise_thresholds(bgms_object)

Details

These functions extract various components from objects returned by the 'bgm()' function, such as edge indicators, posterior inclusion probabilities, and parameter summaries.

Internally, indicator samples were stored in '$gamma' (pre-0.1.4) and '$indicator' (0.1.4–0.1.5). As of **bgms 0.1.6.0**, they are stored in '$raw_samples$indicators'. Access via older names is supported but deprecated.

Posterior inclusion probabilities are computed from edge indicators.

Internally, indicator samples were stored in '$gamma' (pre-0.1.4) and '$indicator' (0.1.4–0.1.5). As of **bgms 0.1.6.0**, they are stored in '$raw_samples$indicator'. Access via older names is supported but deprecated.

Category thresholds were previously stored in '$main_effects' (pre-0.1.4) and '$posterior_mean_main' (0.1.4–0.1.5). As of **bgms 0.1.6.0**, they are stored in '$posterior_summary_main'. Access via older names is supported but deprecated.

Functions

- 'extract_arguments()' – Extract model arguments - 'extract_indicators()' – Get sampled edge indicators - 'extract_posterior_inclusion_probabilities()' – Posterior edge inclusion probabilities - 'extract_pairwise_interactions()' – Posterior mean of pairwise interactions - 'extract_category_thresholds()' – Posterior mean of category thresholds - 'extract_indicator_priors()' – Prior structure used for edge indicators

Sample observations from the ordinal MRF

Description

This function samples states from the ordinal MRF using a Gibbs sampler. The Gibbs sampler is initiated with random values from the response options, after which it proceeds by simulating states for each variable from a logistic model using the other variable states as predictor variables.

Usage

mrfSampler(
  no_states,
  no_variables,
  no_categories,
  interactions,
  thresholds,
  variable_type = "ordinal",
  reference_category,
  iter = 1000
)

Arguments

Details

There are two modeling options for the category thresholds. The default option assumes that the category thresholds are free, except that the first threshold is set to zero for identification. The user then only needs to specify the thresholds for the remaining response categories. This option is useful for any type of ordinal variable and gives the user the most freedom in specifying their model.

The Blume-Capel option is specifically designed for ordinal variables that have a special type of reference_category category, such as the neutral category in a Likert scale. The Blume-Capel model specifies the following quadratic model for the threshold parameters:

\mu_{\text{c}} = \alpha \times \text{c} + \beta \times (\text{c} - \text{r})^2,

where \mu_{\text{c}} is the threshold for category c (which now includes zero), \alpha offers a linear trend across categories (increasing threshold values if \alpha > 0 and decreasing threshold values if \alpha <0), if \beta < 0, it offers an increasing penalty for responding in a category further away from the reference_category category r, while \beta > 0 suggests a preference for responding in the reference_category category.

Value

A no_states by no_variables matrix of simulated states of the ordinal MRF.

Examples

# Generate responses from a network of five binary and ordinal variables.
no_variables = 5
no_categories = sample(1:5, size = no_variables, replace = TRUE)

Interactions = matrix(0, nrow = no_variables, ncol = no_variables)
Interactions[2, 1] = Interactions[4, 1] = Interactions[3, 2] =
  Interactions[5, 2] = Interactions[5, 4] = .25
Interactions = Interactions + t(Interactions)
Thresholds = matrix(0, nrow = no_variables, ncol = max(no_categories))

x = mrfSampler(no_states = 1e3,
               no_variables = no_variables,
               no_categories = no_categories,
               interactions = Interactions,
               thresholds = Thresholds)

# Generate responses from a network of 2 ordinal and 3 Blume-Capel variables.
no_variables = 5
no_categories = 4

Interactions = matrix(0, nrow = no_variables, ncol = no_variables)
Interactions[2, 1] = Interactions[4, 1] = Interactions[3, 2] =
  Interactions[5, 2] = Interactions[5, 4] = .25
Interactions = Interactions + t(Interactions)

Thresholds = matrix(NA, no_variables, no_categories)
Thresholds[, 1] = -1
Thresholds[, 2] = -1
Thresholds[3, ] = sort(-abs(rnorm(4)), decreasing = TRUE)
Thresholds[5, ] = sort(-abs(rnorm(4)), decreasing = TRUE)

x = mrfSampler(no_states = 1e3,
               no_variables = no_variables,
               no_categories = no_categories,
               interactions = Interactions,
               thresholds = Thresholds,
               variable_type = c("b","b","o","b","o"),
               reference_category = 2)

Print method for 'bgmCompare' objects

Description

Minimal console output for 'bgmCompare' fit objects.

Usage

## S3 method for class 'bgmCompare'
print(x, ...)

Arguments

Print method for 'bgms' objects

Description

Minimal console output for 'bgms' fit objects.

Usage

## S3 method for class 'bgms'
print(x, ...)

Arguments

Summary method for 'bgmCompare' objects

Description

Returns posterior summaries and diagnostics for a fitted 'bgmCompare' model.

Usage

## S3 method for class 'bgmCompare'
summary(object, ...)

Arguments

Value

An object of class 'summary.bgmCompare' with posterior summaries.

Summary method for 'bgms' objects

Description

Returns posterior summaries and diagnostics for a fitted 'bgms' model.

Usage

## S3 method for class 'bgms'
summary(object, ...)

Arguments

Value

An object of class 'summary.bgms' with posterior summaries.