Fix #342

Fix bug in `est_non_rf1` and `est_non_rf2`
columns when all the following conditions
were true:
  - predicting on new data
  - using a delta model
  - including IID random intercepts or
    time-varying coefficients

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: sdmTMB
 Title: Spatial and Spatiotemporal SPDE-Based GLMMs with 'TMB'
-Version: 0.5.0.9006
+Version: 0.5.0.9007
 Authors@R: c(
     person(c("Sean", "C."), "Anderson", , "sean@seananderson.ca",
            role = c("aut", "cre"),

NEWS.md

@@ -1,5 +1,12 @@
 # sdmTMB (development version)
 
+* Fix bug in `est_non_rf1` and `est_non_rf2` columns when all the following
+  conditions were true:
+  - predicting on new data
+  - using a delta model
+  - including IID random intercepts or time-varying coefficients
+  See #342. Thanks to @tom-peatman for the issue report.
+
 * Add suggestion to use an optimized BLAS library to README.
 
 * Add warning if it's detected that there were problems reloading (e.g., with

src/sdmTMB.cpp

@@ -1087,7 +1087,7 @@ Type objective_function<Type>::operator()()
             if (!exclude_RE(k)) proj_iid_re_i(i,m) += RE(proj_RE_indexes(i, k) + temp,m);
           }
         }
-        proj_fe(i) += proj_iid_re_i(i,m);
+        proj_fe(i,m) += proj_iid_re_i(i,m);
       }
     }
 
@@ -1099,7 +1099,7 @@ Type objective_function<Type>::operator()()
         for (int i = 0; i < proj_X_rw_ik.rows(); i++) {
           for (int k = 0; k < proj_X_rw_ik.cols(); k++) {
             proj_rw_i(i,m) += proj_X_rw_ik(i, k) * b_rw_t(proj_year(i), k, m);
-            proj_fe(i) += proj_rw_i(i,m);
+            proj_fe(i,m) += proj_rw_i(i,m);
           }
         }
       }

tests/testthat/test-random-intercepts.R

@@ -297,3 +297,30 @@ test_that("Random intercept classes in predict() are checked appropriately", {
   expect_s3_class(p11, "tbl_df")
 })
 
+test_that("Random intercepts are incorporated into est_non_rf1 and est_non_rf2 correctly #342", {
+  ## test based on example by @tom-peatman #342
+  skip_on_cran()
+  skip_on_ci()
+  pcod_2011$year <- factor(pcod_2011$year)
+  ## fit model with random intercepts for year in both model components
+  fit <- sdmTMB(
+    density ~ (1 | year),
+    spatial = "off",
+    data = pcod_2011, mesh = pcod_mesh_2011,
+    family = delta_gamma(link1 = "logit", link2 = "log")
+  )
+  ## fitted random intercepts
+  t1 <- tidy(fit, effects = "ran_vals", model = 1)
+  t2 <- tidy(fit, effects = "ran_vals", model = 2)
+  expect_equal(t1$estimate, c(-0.24066, 0.33083, 0.20459, -0.29288), tolerance = 0.001)
+  expect_equal(t2$estimate, c(0.17724, -0.14142, 0.11862, -0.16844), tolerance = 0.001)
+
+  ## data frame to get predictions for each year (at a specific location and depth)
+  new_dat <- expand.grid(
+    X = mean(pcod_2011$X), Y = mean(pcod_2011$Y),
+    depth = mean(pcod_2011$depth), year = unique(pcod_2011$year)
+  )
+  p <- predict(fit, newdata = new_dat)
+  expect_equal(p$est_non_rf1, c(-0.40011, 0.17137, 0.04514, -0.45234), tolerance = 0.001)
+  expect_equal(p$est_non_rf2, c(4.6455, 4.32684, 4.58688, 4.29982), tolerance = 0.001)
+})