Exp be gone

.Rbuildignore

@@ -12,3 +12,4 @@
 ^doc$
 ^Meta$
 ^\.vscode$
+^dev/

NEWS.md

@@ -7,10 +7,11 @@
 ## Other changes
 
 * reparameterized the Blume-capel model to use (score-baseline) instead of score.
+* implemented a new way to compute the denominators and probabilities. This made their computation both faster and more stable.
 
 ## Bug fixes
 
-* Fixed numerical problems with Blume-Capel variables using HMC and NUTS for bgm().
+* fixed numerical problems with Blume-Capel variables using HMC and NUTS.
 
 # bgms 0.1.6.1
 
@@ -22,9 +23,9 @@
 
 ## Bug fixes
 
-* Fixed a problem with warmup scheduling for adaptive-metropolis in bgmCompare()
-* Fixed stability problems with parallel sampling for bgm()
-* Fixed spurious output errors printing to console after user interrupt. 
+* fixed a problem with warmup scheduling for adaptive-metropolis in bgmCompare()
+* fixed stability problems with parallel sampling for bgm()
+* fixed spurious output errors printing to console after user interrupt. 
 
 # bgms 0.1.6.0
 

R/RcppExports.R

@@ -25,8 +25,8 @@ sample_omrf_gibbs <- function(no_states, no_variables, no_categories, interactio
     .Call(`_bgms_sample_omrf_gibbs`, no_states, no_variables, no_categories, interactions, thresholds, iter)
 }
 
-sample_bcomrf_gibbs <- function(no_states, no_variables, no_categories, interactions, thresholds, variable_type, reference_category, iter) {
-    .Call(`_bgms_sample_bcomrf_gibbs`, no_states, no_variables, no_categories, interactions, thresholds, variable_type, reference_category, iter)
+sample_bcomrf_gibbs <- function(no_states, no_variables, no_categories, interactions, thresholds, variable_type, baseline_category, iter) {
+    .Call(`_bgms_sample_bcomrf_gibbs`, no_states, no_variables, no_categories, interactions, thresholds, variable_type, baseline_category, iter)
 }
 
 compute_Vn_mfm_sbm <- function(no_variables, dirichlet_alpha, t_max, lambda) {

R/bgm.R

@@ -560,8 +560,9 @@ bgm = function(
     # Ordinal (variable_bool == TRUE) or Blume-Capel (variable_bool == FALSE)
     bc_vars = which(!variable_bool)
     for(i in bc_vars) {
-      blume_capel_stats[1, i] = sum(x[, i])
-      blume_capel_stats[2, i] = sum((x[, i] - baseline_category[i])^2)
+      blume_capel_stats[1, i] = sum(x[, i] - baseline_category[i])
+      blume_capel_stats[2, i] = sum((x[, i] - baseline_category[i]) ^ 2)
+      x[, i] = x[, i] - baseline_category[i]
     }
   }
   pairwise_stats = t(x) %*% x
@@ -627,7 +628,6 @@ bgm = function(
     nThreads = cores, seed = seed, progress_type = progress_type
   )
 
-
   userInterrupt = any(vapply(out, FUN = `[[`, FUN.VALUE = logical(1L), "userInterrupt"))
   if(userInterrupt) {
     warning("Stopped sampling after user interrupt, results are likely uninterpretable.")

R/bgmCompare.R

@@ -321,7 +321,7 @@ bgmCompare = function(
     } else if(update_method == "hamiltonian-mc") {
       target_accept = 0.65
     } else if(update_method == "nuts") {
-      target_accept = 0.80
+      target_accept = 0.65
     }
   }
 
@@ -414,13 +414,15 @@ bgmCompare = function(
   blume_capel_stats = compute_blume_capel_stats(
     x, baseline_category, ordinal_variable, group
   )
+  for (i in which(!ordinal_variable)) {
+    x[, i] = x[, i] - baseline_category[i]
+  }
 
   # Compute sufficient statistics for pairwise interactions
   pairwise_stats = compute_pairwise_stats(
     x, group
   )
 
-
   # Index vector used to sample interactions in a random order -----------------
   Index = matrix(0, nrow = num_interactions, ncol = 3)
   counter = 0
@@ -490,7 +492,6 @@ bgmCompare = function(
 
   seed <- as.integer(seed)
 
-
   # Call the Rcpp function
   out = run_bgmCompare_parallel(
     observations = observations,

R/data_utils.R

@@ -243,7 +243,7 @@ compute_counts_per_category = function(x, num_categories, group = NULL) {
         counts_per_category_gr[category, variable] = sum(x[group == g, variable] == category)
       }
     }
-    counts_per_category[[g]] = counts_per_category_gr
+    counts_per_category[[length(counts_per_category) + 1]] = counts_per_category_gr
   }
   return(counts_per_category)
 }
@@ -253,34 +253,34 @@ compute_blume_capel_stats = function(x, baseline_category, ordinal_variable, gro
   if(is.null(group)) { # One-group design
     sufficient_stats = matrix(0, nrow = 2, ncol = ncol(x))
     bc_vars = which(!ordinal_variable)
-    for(i in bc_vars) {
-      sufficient_stats[1, i] = sum(x[, i])
-      sufficient_stats[2, i] = sum((x[, i] - baseline_category[i])^2)
+    for (i in bc_vars) {
+      sufficient_stats[1, i] = sum(x[, i] - baseline_category[i])
+      sufficient_stats[2, i] = sum((x[, i] - baseline_category[i]) ^ 2)
     }
     return(sufficient_stats)
   } else { # Multi-group design
     sufficient_stats = list()
     for(g in unique(group)) {
       sufficient_stats_gr = matrix(0, nrow = 2, ncol = ncol(x))
       bc_vars = which(!ordinal_variable)
-      for(i in bc_vars) {
-        sufficient_stats_gr[1, i] = sum(x[group == g, i])
-        sufficient_stats_gr[2, i] = sum((x[group == g, i] - baseline_category[i])^2)
+      for (i in bc_vars) {
+        sufficient_stats_gr[1, i] = sum(x[group == g, i] - baseline_category[i])
+        sufficient_stats_gr[2, i] = sum((x[group == g, i] - baseline_category[i]) ^ 2)
       }
-      sufficient_stats[[g]] = sufficient_stats_gr
+      sufficient_stats[[length(sufficient_stats) + 1]] = sufficient_stats_gr
     }
     return(sufficient_stats)
   }
 }
 
 # Helper function for computing sufficient statistics for pairwise interactions
 compute_pairwise_stats <- function(x, group) {
-  result <- vector("list", length(unique(group)))
+  result <- list()
 
   for(g in unique(group)) {
     obs <- x[group == g, , drop = FALSE]
     # cross-product: gives number of co-occurrences of categories
-    result[[g]] <- t(obs) %*% obs
+    result[[length(result) + 1]] <- t(obs) %*% obs
   }
 
   result

R/nuts_diagnostics.R

@@ -42,18 +42,16 @@ summarize_nuts_diagnostics <- function(out, nuts_max_depth = 10, verbose = TRUE)
         100 * divergence_rate,
         total_divergences,
         nrow(divergent_mat) * ncol(divergent_mat)
-      ), "Consider increasing the target acceptance rate.")
-    } else if(divergence_rate > 0) {
-      message(
-        sprintf(
-          "Note: %.3f%% of transitions ended with a divergence (%d of %d).\n",
-          100 * divergence_rate,
-          total_divergences,
-          nrow(divergent_mat) * ncol(divergent_mat)
-        ),
-        "Check R-hat and effective sample size (ESS) to ensure the chains are\n",
-        "mixing well."
-      )
+      ), "Consider increasing the target acceptance rate or change to update_method = ``adaptive-metropolis''.")
+    } else if (divergence_rate > 0) {
+      message(sprintf(
+        "Note: %.3f%% of transitions ended with a divergence (%d of %d).\n",
+        100 * divergence_rate,
+        total_divergences,
+        nrow(divergent_mat) * ncol(divergent_mat)
+      ),
+      "Check R-hat and effective sample size (ESS) to ensure the chains are\n",
+      "mixing well.")
     }
 
     depth_hit_rate <- max_tree_depth_hits / (nrow(treedepth_mat) * ncol(treedepth_mat))
@@ -84,16 +82,14 @@ summarize_nuts_diagnostics <- function(out, nuts_max_depth = 10, verbose = TRUE)
     low_ebfmi_chains <- which(ebfmi_per_chain < 0.3)
     min_ebfmi <- min(ebfmi_per_chain)
 
-    if(length(low_ebfmi_chains) > 0) {
-      warning(
-        sprintf(
-          "E-BFMI below 0.3 detected in %d chain(s): %s.\n",
-          length(low_ebfmi_chains),
-          paste(low_ebfmi_chains, collapse = ", ")
-        ),
-        "This suggests inefficient momentum resampling in those chains.\n",
-        "Sampling efficiency may be reduced. Consider longer chains or checking convergence diagnostics."
-      )
+    if (length(low_ebfmi_chains) > 0) {
+      warning(sprintf(
+        "E-BFMI below 0.3 detected in %d chain(s): %s.\n",
+        length(low_ebfmi_chains),
+        paste(low_ebfmi_chains, collapse = ", ")
+      ),
+      "This suggests inefficient momentum resampling in those chains.\n",
+      "Sampling efficiency may be reduced. Consider longer chains and check convergence diagnostics.")
     }
   }
 

R/sampleMRF.R

@@ -13,7 +13,7 @@
 #' 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
+#' have a special type of baseline_category category, such as the neutral
 #' category in a Likert scale. The Blume-Capel model specifies the following
 #' quadratic model for the threshold parameters:
 #' \deqn{\mu_{\text{c}} = \alpha \times \text{c} + \beta \times (\text{c} - \text{r})^2,}{{\mu_{\text{c}} = \alpha \times \text{c} + \beta \times (\text{c} - \text{r})^2,}}
@@ -23,8 +23,8 @@
 #' \eqn{\alpha > 0}{\alpha > 0} and decreasing threshold values if
 #' \eqn{\alpha <0}{\alpha <0}), if \eqn{\beta < 0}{\beta < 0}, it offers an
 #' increasing penalty for responding in a category further away from the
-#' reference_category category r, while \eqn{\beta > 0}{\beta > 0} suggests a
-#' preference for responding in the reference_category category.
+#' baseline_category category r, while \eqn{\beta > 0}{\beta > 0} suggests a
+#' preference for responding in the baseline_category category.
 #'
 #' @param no_states The number of states of the ordinal MRF to be generated.
 #'
@@ -53,8 +53,8 @@
 #' ``blume-capel''. Binary variables are automatically treated as ``ordinal’’.
 #' Defaults to \code{variable_type = "ordinal"}.
 #'
-#' @param reference_category An integer vector of length \code{no_variables} specifying the
-#' reference_category category that is used for the Blume-Capel model (details below).
+#' @param baseline_category An integer vector of length \code{no_variables} specifying the
+#' baseline_category category that is used for the Blume-Capel model (details below).
 #' Can be any integer value between \code{0} and \code{no_categories} (or
 #' \code{no_categories[i]}).
 #'
@@ -106,7 +106,7 @@
 #'   interactions = Interactions,
 #'   thresholds = Thresholds,
 #'   variable_type = c("b", "b", "o", "b", "o"),
-#'   reference_category = 2
+#'   baseline_category = 2
 #' )
 #'
 #' @export
@@ -116,7 +116,7 @@ mrfSampler = function(no_states,
                       interactions,
                       thresholds,
                       variable_type = "ordinal",
-                      reference_category,
+                      baseline_category,
                       iter = 1e3) {
   # Check no_states, no_variables, iter --------------------------------------------
   if(no_states <= 0 ||
@@ -187,24 +187,20 @@ mrfSampler = function(no_states,
     }
   }
 
-  # Check the reference_category for Blume-Capel variables ---------------------
+  # Check the baseline_category for Blume-Capel variables ---------------------
   if(any(variable_type == "blume-capel")) {
-    if(length(reference_category) == 1) {
-      reference_category = rep(reference_category, no_variables)
+    if(length(baseline_category) == 1) {
+      baseline_category = rep(baseline_category, no_variables)
     }
-    if(any(reference_category < 0) || any(abs(reference_category - round(reference_category)) > .Machine$double.eps)) {
-      stop(paste0(
-        "For variables ",
-        which(reference_category < 0),
-        " ``reference_category'' was either negative or not integer."
-      ))
+    if(any(baseline_category < 0) || any(abs(baseline_category - round(baseline_category)) > .Machine$double.eps)) {
+      stop(paste0("For variables ",
+                  which(baseline_category < 0),
+                  " ``baseline_category'' was either negative or not integer."))
     }
-    if(any(reference_category - no_categories > 0)) {
-      stop(paste0(
-        "For variables ",
-        which(reference_category - no_categories > 0),
-        " the ``reference_category'' category was larger than the maximum category value."
-      ))
+    if(any(baseline_category - no_categories > 0)) {
+      stop(paste0("For variables ",
+                  which(baseline_category - no_categories > 0),
+                  " the ``baseline_category'' category was larger than the maximum category value."))
     }
   }
 
@@ -347,7 +343,7 @@ mrfSampler = function(no_states,
       interactions = interactions,
       thresholds = thresholds,
       variable_type = variable_type,
-      reference_category = reference_category,
+      baseline_category = baseline_category,
       iter = iter
     )
   }