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Merged
MaartenMarsman merged 6 commits into from
Nov 12, 2025
Merged

two sets of shape hyperparameters for the Beta hyperprior in the SBM #68

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c8af72d
two sets of shape hyperparameters for the Beta hyperprior in the SBM
sekulovskin Oct 14, 2025
727f8cd
Merge branch 'Bayesian-Graphical-Modelling-Lab:main' into SBM
sekulovskin Oct 27, 2025
bf3ec25
Update documentation for the extra shape parameters
sekulovskin Oct 27, 2025
8182afd
Merge branch 'SBM' of https://github.com/sekulovskin/bgms into SBM
sekulovskin Oct 27, 2025
a462165
update documentation
sekulovskin Oct 27, 2025
dc4de41
adress review comments
Nov 12, 2025
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4 changes: 2 additions & 2 deletions R/RcppExports.R
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Original file line number Diff line number Diff line change
Expand Up @@ -5,8 +5,8 @@ run_bgmCompare_parallel <- function(observations, num_groups, counts_per_categor
.Call(`_bgms_run_bgmCompare_parallel`, observations, num_groups, counts_per_category, blume_capel_stats, pairwise_stats, num_categories, main_alpha, main_beta, pairwise_scale, difference_scale, difference_selection_alpha, difference_selection_beta, difference_prior, iter, warmup, na_impute, missing_data_indices, is_ordinal_variable, baseline_category, difference_selection, main_effect_indices, pairwise_effect_indices, target_accept, nuts_max_depth, learn_mass_matrix, projection, group_membership, group_indices, interaction_index_matrix, inclusion_probability, num_chains, nThreads, seed, update_method, hmc_num_leapfrogs, progress_type)
}

run_bgm_parallel <- function(observations, num_categories, pairwise_scale, edge_prior, inclusion_probability, beta_bernoulli_alpha, beta_bernoulli_beta, dirichlet_alpha, lambda, interaction_index_matrix, iter, warmup, counts_per_category, blume_capel_stats, main_alpha, main_beta, na_impute, missing_index, is_ordinal_variable, baseline_category, edge_selection, update_method, pairwise_effect_indices, target_accept, pairwise_stats, hmc_num_leapfrogs, nuts_max_depth, learn_mass_matrix, num_chains, nThreads, seed, progress_type) {
.Call(`_bgms_run_bgm_parallel`, observations, num_categories, pairwise_scale, edge_prior, inclusion_probability, beta_bernoulli_alpha, beta_bernoulli_beta, dirichlet_alpha, lambda, interaction_index_matrix, iter, warmup, counts_per_category, blume_capel_stats, main_alpha, main_beta, na_impute, missing_index, is_ordinal_variable, baseline_category, edge_selection, update_method, pairwise_effect_indices, target_accept, pairwise_stats, hmc_num_leapfrogs, nuts_max_depth, learn_mass_matrix, num_chains, nThreads, seed, progress_type)
run_bgm_parallel <- function(observations, num_categories, pairwise_scale, edge_prior, inclusion_probability, beta_bernoulli_alpha, beta_bernoulli_beta, beta_bernoulli_alpha_between, beta_bernoulli_beta_between, dirichlet_alpha, lambda, interaction_index_matrix, iter, warmup, counts_per_category, blume_capel_stats, main_alpha, main_beta, na_impute, missing_index, is_ordinal_variable, baseline_category, edge_selection, update_method, pairwise_effect_indices, target_accept, pairwise_stats, hmc_num_leapfrogs, nuts_max_depth, learn_mass_matrix, num_chains, nThreads, seed, progress_type) {
.Call(`_bgms_run_bgm_parallel`, observations, num_categories, pairwise_scale, edge_prior, inclusion_probability, beta_bernoulli_alpha, beta_bernoulli_beta, beta_bernoulli_alpha_between, beta_bernoulli_beta_between, dirichlet_alpha, lambda, interaction_index_matrix, iter, warmup, counts_per_category, blume_capel_stats, main_alpha, main_beta, na_impute, missing_index, is_ordinal_variable, baseline_category, edge_selection, update_method, pairwise_effect_indices, target_accept, pairwise_stats, hmc_num_leapfrogs, nuts_max_depth, learn_mass_matrix, num_chains, nThreads, seed, progress_type)
}

get_explog_switch <- function() {
Expand Down
31 changes: 28 additions & 3 deletions R/bgm.R
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Original file line number Diff line number Diff line change
Expand Up @@ -207,8 +207,14 @@
#'
#' @param beta_bernoulli_alpha,beta_bernoulli_beta Double. Shape parameters
#' for the beta distribution in the Beta–Bernoulli and the Stochastic-Block
#' priors. Must be positive. Defaults: \code{beta_bernoulli_alpha = 1} and
#' \code{beta_bernoulli_beta = 1}.
#' priors. Must be positive. For the Stochastic-Block prior these are the shape
#' parameters for the within-cluster edge inclusion probabilities.
#' Defaults: \code{beta_bernoulli_alpha = 1} and \code{beta_bernoulli_beta = 1}.
#'
#' @param beta_bernoulli_alpha_between,beta_bernoulli_beta_between Double.
#' Shape parameters for the between-cluster edge inclusion probabilities in the
#' Stochastic-Block prior. Must be positive.
#' Default: \code{beta_bernoulli_alpha_between = 1} and \code{beta_bernoulli_beta_between = 1}
#'
#' @param dirichlet_alpha Double. Concentration parameter of the Dirichlet
#' prior on block assignments (used with the Stochastic Block model).
Expand Down Expand Up @@ -359,6 +365,8 @@ bgm = function(
inclusion_probability = 0.5,
beta_bernoulli_alpha = 1,
beta_bernoulli_beta = 1,
beta_bernoulli_alpha_between = 1,
beta_bernoulli_beta_between = 1,
dirichlet_alpha = 1,
lambda = 1,
na_action = c("listwise", "impute"),
Expand Down Expand Up @@ -418,7 +426,7 @@ bgm = function(
} else if(update_method == "hamiltonian-mc") {
target_accept = 0.65
} else if(update_method == "nuts") {
target_accept = 0.80
target_accept = 0.60
}
}

Expand All @@ -444,9 +452,21 @@ bgm = function(
inclusion_probability = inclusion_probability,
beta_bernoulli_alpha = beta_bernoulli_alpha,
beta_bernoulli_beta = beta_bernoulli_beta,
beta_bernoulli_alpha_between = beta_bernoulli_alpha_between,
beta_bernoulli_beta_between = beta_bernoulli_beta_between,
dirichlet_alpha = dirichlet_alpha,
lambda = lambda)

# check hyperparameters input
# If user left them NULL, pass -1 to C++ (means: ignore between prior)
if (is.null(beta_bernoulli_alpha_between) && is.null(beta_bernoulli_beta_between)) {
beta_bernoulli_alpha_between <- -1.0
beta_bernoulli_beta_between <- -1.0
} else if (is.null(beta_bernoulli_alpha_between) || is.null(beta_bernoulli_beta_between)) {
stop("If you wish to specify different between and within cluster probabilites,
provide both beta_bernoulli_alpha_between and beta_bernoulli_beta_between,
otherwise leave both NULL.")
}
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@MaartenMarsman MaartenMarsman Nov 12, 2025

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If I read this correctly, this will stop the analysis if the between parameters are left at their defaults. This means users cannot use bgms, except when they set other values for these defaults, irrespective if they use an SBM prior or not.

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@sekulovskin sekulovskin Nov 12, 2025

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No, this is in case the user specifies only one of the between parameters, but not both. Otherwise running the code below did work. But it doesn't matter now, since as you requested, both sets of hyperparameters are always provided.

fit <- bgm(Wenchuan[c(1:100), c(1:5)], edge_prior = "Stochastic-Block",
beta_bernoulli_alpha = 1,
beta_bernoulli_beta = 1,
beta_bernoulli_alpha_between = NULL,
beta_bernoulli_beta_between = NULL,
update_method = "adaptive-metropolis",
iter = 1000)

# ----------------------------------------------------------------------------
# The vector variable_type is now coded as boolean.
# Ordinal (variable_bool == TRUE) or Blume-Capel (variable_bool == FALSE)
Expand Down Expand Up @@ -572,6 +592,8 @@ bgm = function(
inclusion_probability = inclusion_probability,
beta_bernoulli_alpha = beta_bernoulli_alpha,
beta_bernoulli_beta = beta_bernoulli_beta,
beta_bernoulli_alpha_between = beta_bernoulli_alpha_between,
beta_bernoulli_beta_between = beta_bernoulli_beta_between,
dirichlet_alpha = dirichlet_alpha, lambda = lambda,
interaction_index_matrix = interaction_index_matrix, iter = iter,
warmup = warmup, counts_per_category = counts_per_category,
Expand Down Expand Up @@ -603,6 +625,7 @@ bgm = function(
na_action = na_action, na_impute = na_impute,
edge_selection = edge_selection, edge_prior = edge_prior, inclusion_probability = inclusion_probability,
beta_bernoulli_alpha = beta_bernoulli_alpha, beta_bernoulli_beta = beta_bernoulli_beta,
beta_bernoulli_alpha_between = beta_bernoulli_alpha_between, beta_bernoulli_beta_between = beta_bernoulli_beta_between,
dirichlet_alpha = dirichlet_alpha, lambda = lambda,
variable_type = variable_type,
update_method = update_method,
Expand Down Expand Up @@ -634,6 +657,8 @@ bgm = function(
edge_selection = edge_selection, edge_prior = edge_prior, inclusion_probability = inclusion_probability,
beta_bernoulli_alpha = beta_bernoulli_alpha,
beta_bernoulli_beta = beta_bernoulli_beta,
beta_bernoulli_alpha_between = beta_bernoulli_alpha_between,
beta_bernoulli_beta_between = beta_bernoulli_beta_between,
dirichlet_alpha = dirichlet_alpha, lambda = lambda,
variable_type = variable_type,
update_method = update_method,
Expand Down
44 changes: 35 additions & 9 deletions R/function_input_utils.R
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Original file line number Diff line number Diff line change
Expand Up @@ -31,6 +31,8 @@ check_model = function(x,
inclusion_probability = 0.5,
beta_bernoulli_alpha = 1,
beta_bernoulli_beta = 1,
beta_bernoulli_alpha_between = 1,
beta_bernoulli_beta_between = 1,
dirichlet_alpha = dirichlet_alpha,
lambda = lambda) {

Expand Down Expand Up @@ -204,18 +206,42 @@ check_model = function(x,
is.null(beta_bernoulli_alpha) || is.null(beta_bernoulli_beta))
stop("Values for both scale parameters of the beta distribution need to be specified.")
}

if(edge_prior == "Stochastic-Block") {
theta = matrix(0.5, nrow = ncol(x), ncol = ncol(x))
if(beta_bernoulli_alpha <= 0 || beta_bernoulli_beta <= 0 || dirichlet_alpha <= 0 || lambda <= 0)
stop("The scale parameters of the beta and Dirichlet distribution need to be positive.")
if(!is.finite(beta_bernoulli_alpha) || !is.finite(beta_bernoulli_beta) || !is.finite(dirichlet_alpha) || !is.finite(lambda))
stop("The scale parameters of the beta distribution, the concentration parameter of the Dirichlet distribution, and the rate parameter of the Poisson distribution need to be finite.")
if(is.na(beta_bernoulli_alpha) || is.na(beta_bernoulli_beta) ||
is.null(beta_bernoulli_alpha) || is.null(beta_bernoulli_beta) ||
is.null(dirichlet_alpha) || is.null(dirichlet_alpha) || is.null(lambda) || is.null(lambda))
stop("Values for both scale parameters of the beta distribution, the concentration parameter of the Dirichlet distribution, and the rate parameter of the Poisson distribution need to be specified.")

# Check that all beta parameters are provided
if (is.null(beta_bernoulli_alpha) || is.null(beta_bernoulli_beta) ||
is.null(beta_bernoulli_alpha_between) || is.null(beta_bernoulli_beta_between)) {
stop("The Stochastic-Block prior requires all four beta parameters: ",
"beta_bernoulli_alpha, beta_bernoulli_beta, ",
"beta_bernoulli_alpha_between, and beta_bernoulli_beta_between.")
}

# Check that all beta parameters are positive
if (beta_bernoulli_alpha <= 0 || beta_bernoulli_beta <= 0 ||
beta_bernoulli_alpha_between <= 0 || beta_bernoulli_beta_between <= 0 ||
dirichlet_alpha <= 0 || lambda <= 0) {
stop("The parameters of the beta and Dirichlet distributions need to be positive.")
}

# Check that all beta parameters are finite
if (!is.finite(beta_bernoulli_alpha) || !is.finite(beta_bernoulli_beta) ||
!is.finite(beta_bernoulli_alpha_between) || !is.finite(beta_bernoulli_beta_between) ||
!is.finite(dirichlet_alpha) || !is.finite(lambda)) {
stop("The shape parameters of the beta distribution, the concentration parameter of the Dirichlet distribution, ",
"and the rate parameter of the Poisson distribution need to be finite.")
}

# Check for NAs
if (is.na(beta_bernoulli_alpha) || is.na(beta_bernoulli_beta) ||
is.na(beta_bernoulli_alpha_between) || is.na(beta_bernoulli_beta_between) ||
is.na(dirichlet_alpha) || is.na(lambda)) {
stop("Values for all shape parameters of the beta distribution, the concentration parameter of the Dirichlet distribution, ",
"and the rate parameter of the Poisson distribution cannot be NA.")
}
}
} else {
}else {
theta = matrix(0.5, nrow = 1, ncol = 1)
edge_prior = "Not Applicable"
}
Expand Down
5 changes: 4 additions & 1 deletion R/output_utils.R
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,8 @@ prepare_output_bgm = function(
out, x, num_categories, iter, data_columnnames, is_ordinal_variable,
warmup, pairwise_scale, main_alpha, main_beta,
na_action, na_impute, edge_selection, edge_prior, inclusion_probability,
beta_bernoulli_alpha, beta_bernoulli_beta, dirichlet_alpha, lambda,
beta_bernoulli_alpha, beta_bernoulli_beta, beta_bernoulli_alpha_between,
beta_bernoulli_beta_between,dirichlet_alpha, lambda,
variable_type, update_method, target_accept, hmc_num_leapfrogs,
nuts_max_depth, learn_mass_matrix, num_chains
) {
Expand All @@ -22,6 +23,8 @@ prepare_output_bgm = function(
inclusion_probability = inclusion_probability,
beta_bernoulli_alpha = beta_bernoulli_alpha,
beta_bernoulli_beta = beta_bernoulli_beta,
beta_bernoulli_alpha_between = beta_bernoulli_alpha_between,
beta_bernoulli_beta_between = beta_bernoulli_beta_between,
dirichlet_alpha = dirichlet_alpha,
lambda = lambda,
na_action = na_action,
Expand Down
12 changes: 10 additions & 2 deletions man/bgm.Rd
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10 changes: 6 additions & 4 deletions src/RcppExports.cpp
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Expand Up @@ -58,8 +58,8 @@ BEGIN_RCPP
END_RCPP
}
// run_bgm_parallel
Rcpp::List run_bgm_parallel(const arma::imat& observations, const arma::ivec& num_categories, double pairwise_scale, const std::string& edge_prior, const arma::mat& inclusion_probability, double beta_bernoulli_alpha, double beta_bernoulli_beta, double dirichlet_alpha, double lambda, const arma::imat& interaction_index_matrix, int iter, int warmup, const arma::imat& counts_per_category, const arma::imat& blume_capel_stats, double main_alpha, double main_beta, bool na_impute, const arma::imat& missing_index, const arma::uvec& is_ordinal_variable, const arma::ivec& baseline_category, bool edge_selection, const std::string& update_method, const arma::imat& pairwise_effect_indices, double target_accept, const arma::imat& pairwise_stats, int hmc_num_leapfrogs, int nuts_max_depth, bool learn_mass_matrix, int num_chains, int nThreads, int seed, int progress_type);
RcppExport SEXP _bgms_run_bgm_parallel(SEXP observationsSEXP, SEXP num_categoriesSEXP, SEXP pairwise_scaleSEXP, SEXP edge_priorSEXP, SEXP inclusion_probabilitySEXP, SEXP beta_bernoulli_alphaSEXP, SEXP beta_bernoulli_betaSEXP, SEXP dirichlet_alphaSEXP, SEXP lambdaSEXP, SEXP interaction_index_matrixSEXP, SEXP iterSEXP, SEXP warmupSEXP, SEXP counts_per_categorySEXP, SEXP blume_capel_statsSEXP, SEXP main_alphaSEXP, SEXP main_betaSEXP, SEXP na_imputeSEXP, SEXP missing_indexSEXP, SEXP is_ordinal_variableSEXP, SEXP baseline_categorySEXP, SEXP edge_selectionSEXP, SEXP update_methodSEXP, SEXP pairwise_effect_indicesSEXP, SEXP target_acceptSEXP, SEXP pairwise_statsSEXP, SEXP hmc_num_leapfrogsSEXP, SEXP nuts_max_depthSEXP, SEXP learn_mass_matrixSEXP, SEXP num_chainsSEXP, SEXP nThreadsSEXP, SEXP seedSEXP, SEXP progress_typeSEXP) {
Rcpp::List run_bgm_parallel(const arma::imat& observations, const arma::ivec& num_categories, double pairwise_scale, const std::string& edge_prior, const arma::mat& inclusion_probability, double beta_bernoulli_alpha, double beta_bernoulli_beta, double beta_bernoulli_alpha_between, double beta_bernoulli_beta_between, double dirichlet_alpha, double lambda, const arma::imat& interaction_index_matrix, int iter, int warmup, const arma::imat& counts_per_category, const arma::imat& blume_capel_stats, double main_alpha, double main_beta, bool na_impute, const arma::imat& missing_index, const arma::uvec& is_ordinal_variable, const arma::ivec& baseline_category, bool edge_selection, const std::string& update_method, const arma::imat& pairwise_effect_indices, double target_accept, const arma::imat& pairwise_stats, int hmc_num_leapfrogs, int nuts_max_depth, bool learn_mass_matrix, int num_chains, int nThreads, int seed, int progress_type);
RcppExport SEXP _bgms_run_bgm_parallel(SEXP observationsSEXP, SEXP num_categoriesSEXP, SEXP pairwise_scaleSEXP, SEXP edge_priorSEXP, SEXP inclusion_probabilitySEXP, SEXP beta_bernoulli_alphaSEXP, SEXP beta_bernoulli_betaSEXP, SEXP beta_bernoulli_alpha_betweenSEXP, SEXP beta_bernoulli_beta_betweenSEXP, SEXP dirichlet_alphaSEXP, SEXP lambdaSEXP, SEXP interaction_index_matrixSEXP, SEXP iterSEXP, SEXP warmupSEXP, SEXP counts_per_categorySEXP, SEXP blume_capel_statsSEXP, SEXP main_alphaSEXP, SEXP main_betaSEXP, SEXP na_imputeSEXP, SEXP missing_indexSEXP, SEXP is_ordinal_variableSEXP, SEXP baseline_categorySEXP, SEXP edge_selectionSEXP, SEXP update_methodSEXP, SEXP pairwise_effect_indicesSEXP, SEXP target_acceptSEXP, SEXP pairwise_statsSEXP, SEXP hmc_num_leapfrogsSEXP, SEXP nuts_max_depthSEXP, SEXP learn_mass_matrixSEXP, SEXP num_chainsSEXP, SEXP nThreadsSEXP, SEXP seedSEXP, SEXP progress_typeSEXP) {
BEGIN_RCPP
Rcpp::RObject rcpp_result_gen;
Rcpp::RNGScope rcpp_rngScope_gen;
Expand All @@ -70,6 +70,8 @@ BEGIN_RCPP
Rcpp::traits::input_parameter< const arma::mat& >::type inclusion_probability(inclusion_probabilitySEXP);
Rcpp::traits::input_parameter< double >::type beta_bernoulli_alpha(beta_bernoulli_alphaSEXP);
Rcpp::traits::input_parameter< double >::type beta_bernoulli_beta(beta_bernoulli_betaSEXP);
Rcpp::traits::input_parameter< double >::type beta_bernoulli_alpha_between(beta_bernoulli_alpha_betweenSEXP);
Rcpp::traits::input_parameter< double >::type beta_bernoulli_beta_between(beta_bernoulli_beta_betweenSEXP);
Rcpp::traits::input_parameter< double >::type dirichlet_alpha(dirichlet_alphaSEXP);
Rcpp::traits::input_parameter< double >::type lambda(lambdaSEXP);
Rcpp::traits::input_parameter< const arma::imat& >::type interaction_index_matrix(interaction_index_matrixSEXP);
Expand All @@ -95,7 +97,7 @@ BEGIN_RCPP
Rcpp::traits::input_parameter< int >::type nThreads(nThreadsSEXP);
Rcpp::traits::input_parameter< int >::type seed(seedSEXP);
Rcpp::traits::input_parameter< int >::type progress_type(progress_typeSEXP);
rcpp_result_gen = Rcpp::wrap(run_bgm_parallel(observations, num_categories, pairwise_scale, edge_prior, inclusion_probability, beta_bernoulli_alpha, beta_bernoulli_beta, dirichlet_alpha, lambda, interaction_index_matrix, iter, warmup, counts_per_category, blume_capel_stats, main_alpha, main_beta, na_impute, missing_index, is_ordinal_variable, baseline_category, edge_selection, update_method, pairwise_effect_indices, target_accept, pairwise_stats, hmc_num_leapfrogs, nuts_max_depth, learn_mass_matrix, num_chains, nThreads, seed, progress_type));
rcpp_result_gen = Rcpp::wrap(run_bgm_parallel(observations, num_categories, pairwise_scale, edge_prior, inclusion_probability, beta_bernoulli_alpha, beta_bernoulli_beta, beta_bernoulli_alpha_between, beta_bernoulli_beta_between, dirichlet_alpha, lambda, interaction_index_matrix, iter, warmup, counts_per_category, blume_capel_stats, main_alpha, main_beta, na_impute, missing_index, is_ordinal_variable, baseline_category, edge_selection, update_method, pairwise_effect_indices, target_accept, pairwise_stats, hmc_num_leapfrogs, nuts_max_depth, learn_mass_matrix, num_chains, nThreads, seed, progress_type));
return rcpp_result_gen;
END_RCPP
}
Expand Down Expand Up @@ -182,7 +184,7 @@ END_RCPP

static const R_CallMethodDef CallEntries[] = {
{"_bgms_run_bgmCompare_parallel", (DL_FUNC) &_bgms_run_bgmCompare_parallel, 36},
{"_bgms_run_bgm_parallel", (DL_FUNC) &_bgms_run_bgm_parallel, 32},
{"_bgms_run_bgm_parallel", (DL_FUNC) &_bgms_run_bgm_parallel, 34},
{"_bgms_get_explog_switch", (DL_FUNC) &_bgms_get_explog_switch, 0},
{"_bgms_rcpp_ieee754_exp", (DL_FUNC) &_bgms_rcpp_ieee754_exp, 1},
{"_bgms_rcpp_ieee754_log", (DL_FUNC) &_bgms_rcpp_ieee754_log, 1},
Expand Down
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