fixed #404

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: BayesMallows
 Type: Package
 Title: Bayesian Preference Learning with the Mallows Rank Model
-Version: 2.1.1.9006
+Version: 2.1.1.9007
 Authors@R: c(person("Oystein", "Sorensen",
                     email = "oystein.sorensen.1985@gmail.com",
                     role = c("aut", "cre"),

NEWS.md

@@ -1,5 +1,7 @@
 # BayesMallows (development versions)
 
+* The SMC function now check for consistency with previous latent ranks for 
+  existing users also when data arrive in the form of pairwise preferences.
 * A function compute_exact_partition_function() is now added, which returns the
   logarithm of the exact partition function for Cayley, Hamming, and Kendall
   distance.

src/particles.cpp

@@ -105,7 +105,20 @@ std::vector<Particle> augment_particles(
       }
     } else if (dat.augpair) {
       for(auto index : dat.updated_match) {
-        pvec[i].consistent(index) = 0;
+        pvec[i].consistent(index) = 1;
+        vec augmented = pvec[i].augmented_data(span::all, span(index));
+        auto items_above_for_user = dat.items_above[index];
+        size_t j{};
+        while(pvec[i].consistent(index) == 1 && j < items_above_for_user.size()) {
+          size_t k{};
+          while(pvec[i].consistent(index) == 1 && k < items_above_for_user[j].size()) {
+            if(augmented(items_above_for_user[j][k] - 1) > augmented(j)) {
+              pvec[i].consistent(index) = 0;
+            }
+            k++;
+          }
+          j++;
+        }
       }
 
       pvec[i].augmented_data.resize(dat.n_items, dat.rankings.n_cols);

tests/testthat/test-smc_pairwise.R

@@ -76,7 +76,7 @@ test_that("update_mallows works with existing users updating their data", {
 
   expect_equal(
     mean(m2$alpha_samples),
-    2.82143649083478
+    2.79625839950885
   )
 
   expect_equal(

work-docs/pairwise_smc.R

@@ -1,24 +1,34 @@
 library(BayesMallows)
 m0 <- compute_mallows(
-  data = setup_rank_data(preferences = subset(beach_preferences, assessor == 2)),
+  data = setup_rank_data(preferences = subset(beach_preferences, assessor == 2 & bottom_item < 5 & top_item < 5),
+                         n_items = 5),
   compute_options = set_compute_options(nmc = 2000, burnin = 500)
 )
 
 m1 <- update_mallows(
   model = m0,
   new_data = setup_rank_data(
-    preferences = beach_preferences[1:10, ],
+    preferences = data.frame(
+      assessor = 1,
+      bottom_item = c(5, 4, 3, 2),
+      top_item = c(4, 3, 2, 1)
+    ),
     user_ids = 1,
-    n_items = 15
+    n_items = 5
   ),
   smc_options = set_smc_options(n_particles = 2)
 )
 
 m2 <- update_mallows(
   model = m1,
-  new_data = setup_rank_data(
-    preferences = beach_preferences[11:20, ],
-    user_ids = 1
+  setup_rank_data(
+    preferences = data.frame(
+      assessor = 1,
+      bottom_item = c(5, 4, 3, 2, 5),
+      top_item = c(4, 3, 2, 1, 3)
+    ),
+    user_ids = 1,
+    n_items = 5
   ),
   smc_options = set_smc_options(n_particles = 2)
 )