Add inclusion probability and (curated) group indicators to results$arguments for bgmCompare

Author: MaartenMarsman

R/bgm.R

@@ -239,7 +239,7 @@
 #' @param target_accept Numeric between 0 and 1. Target acceptance rate for
 #'   the sampler. Defaults are set automatically if not supplied:
 #'   \code{0.44} for adaptive Metropolis, \code{0.65} for HMC,
-#'   and \code{0.60} for NUTS.
+#'   and \code{0.80} for NUTS.
 #'
 #' @param hmc_num_leapfrogs Integer. Number of leapfrog steps for Hamiltonian
 #'   Monte Carlo. Must be positive. Default: \code{100}.
@@ -418,7 +418,7 @@ bgm = function(
     } else if(update_method == "hamiltonian-mc") {
       target_accept = 0.65
     } else if(update_method == "nuts") {
-      target_accept = 0.60
+      target_accept = 0.80
     }
   }
 

R/bgmCompare.R

@@ -112,7 +112,7 @@
 #'   \code{"adaptive-metropolis"}, \code{"hamiltonian-mc"}, or \code{"nuts"}.
 #'   Default: \code{"nuts"}.
 #' @param target_accept Numeric between 0 and 1. Target acceptance rate.
-#'   Defaults: 0.44 (Metropolis), 0.65 (HMC), 0.60 (NUTS).
+#'   Defaults: 0.44 (Metropolis), 0.65 (HMC), 0.80 (NUTS).
 #' @param hmc_num_leapfrogs Integer. Leapfrog steps for HMC. Default: \code{100}.
 #' @param nuts_max_depth Integer. Maximum tree depth for NUTS. Default: \code{10}.
 #' @param learn_mass_matrix Logical. If \code{TRUE}, adapt the mass matrix
@@ -314,7 +314,7 @@ bgmCompare = function(
     } else if(update_method == "hamiltonian-mc") {
       target_accept = 0.65
     } else if(update_method == "nuts") {
-      target_accept = 0.60
+      target_accept = 0.80
     }
   }
 
@@ -524,6 +524,7 @@ bgmCompare = function(
         num_categories = num_categories,
         is_ordinal_variable = ordinal_variable,
         num_groups = num_groups,
+        group = sorted_group,
         iter = iter,
         warmup = warmup,
         main_effect_indices = main_effect_indices,
@@ -533,6 +534,7 @@ bgmCompare = function(
         difference_prior = difference_prior,
         difference_selection_alpha = beta_bernoulli_alpha,
         difference_selection_beta = beta_bernoulli_beta,
+        inclusion_probability = model$inclusion_probability_difference,
         pairwise_scale = pairwise_scale,
         difference_scale = difference_scale,
         update_method = update_method,
@@ -560,6 +562,7 @@ bgmCompare = function(
     num_categories = num_categories,
     is_ordinal_variable = ordinal_variable,
     num_groups = num_groups,
+    group = sorted_group,
     iter = iter,
     warmup = warmup,
     main_effect_indices = main_effect_indices,
@@ -569,6 +572,7 @@ bgmCompare = function(
     difference_prior = difference_prior,
     difference_selection_alpha = beta_bernoulli_alpha,
     difference_selection_beta = beta_bernoulli_beta,
+    inclusion_probability = model$inclusion_probability_difference,
     pairwise_scale = pairwise_scale,
     difference_scale = difference_scale,
     update_method = update_method,

R/output_utils.R

@@ -275,10 +275,11 @@ generate_param_names_bgmCompare = function(
 
 prepare_output_bgmCompare = function(
     out, observations, num_categories, is_ordinal_variable,
-    num_groups, iter, warmup,
+    num_groups, group, iter, warmup,
     main_effect_indices, pairwise_effect_indices,
     data_columnnames, difference_selection,
     difference_prior, difference_selection_alpha, difference_selection_beta,
+    inclusion_probability,
     pairwise_scale, difference_scale,
     update_method, target_accept, nuts_max_depth, hmc_num_leapfrogs,
     learn_mass_matrix, num_chains, projection
@@ -297,6 +298,7 @@ prepare_output_bgmCompare = function(
     difference_prior = difference_prior,
     difference_selection_alpha = difference_selection_alpha,
     difference_selection_beta = difference_selection_beta,
+    inclusion_probability = inclusion_probability,
     version = packageVersion("bgms"),
     update_method = update_method,
     target_accept = target_accept,
@@ -308,7 +310,8 @@ prepare_output_bgmCompare = function(
     data_columnnames = data_columnnames,
     projection = projection,
     num_categories = num_categories,
-    is_ordinal_variable = is_ordinal_variable
+    is_ordinal_variable = is_ordinal_variable,
+    group = group
   )
 
   # --- parameter names