#47 adds new_expr_vec to model update

poissonconsulting · Jun 19, 2023 · 14862ab · 14862ab
1 parent 9febc4e
commit 14862ab
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Showing 3 changed files with 92 additions and 5 deletions.
diff --git a/R/update-model.R b/R/update-model.R
@@ -10,7 +10,7 @@
 update_model <- function(model, code = NULL, gen_inits = NULL,
                          random_effects = NULL, fixed = NULL, derived = NULL, select_data = NULL,
                          center = NULL, scale = NULL, modify_data = NULL,
-                         nthin = NULL, new_expr = NULL, modify_new_data = NULL,
+                         nthin = NULL, new_expr = NULL, new_expr_vec = NULL, modify_new_data = NULL,
                          drops = NULL, ...) {
   UseMethod("update_model")
 }
@@ -19,11 +19,9 @@ update_model <- function(model, code = NULL, gen_inits = NULL,
 update_model.mb_model <- function(model, code = NULL, gen_inits = NULL,
                                   random_effects = NULL, fixed = NULL, derived = NULL, select_data = NULL,
                                   center = NULL, scale = NULL, modify_data = NULL,
-                                  nthin = NULL, new_expr = NULL, modify_new_data = NULL,
+                                  nthin = NULL, new_expr = NULL, new_expr_vec = NULL, modify_new_data = NULL,
                                   drops = NULL, ...) {
 
-
-
   if (is.null(code)) code <- code(model)
   if (is.null(gen_inits)) gen_inits <- model$gen_inits
   if (is.null(random_effects)) random_effects <- model$random_effects
@@ -34,7 +32,7 @@ update_model.mb_model <- function(model, code = NULL, gen_inits = NULL,
   if (is.null(scale)) scale <- model$scale
   if (is.null(modify_data)) modify_data <- model$modify_data
   if (is.null(nthin)) nthin <- model$nthin
-  new_expr <- enexpr_new_expr({{ new_expr }}, default = model$new_expr)
+  new_expr <- enexpr_new_expr({{ new_expr }}, default = model$new_expr, vectorize = new_expr_vec)
   if (is.null(modify_new_data)) modify_new_data <- model$modify_new_data
   if (is.null(drops)) drops <- model$drops
 
@@ -50,6 +48,7 @@ update_model.mb_model <- function(model, code = NULL, gen_inits = NULL,
     modify_data = modify_data,
     nthin = nthin,
     new_expr = !!new_expr,
+    new_expr_vec =  new_expr_vec,
     modify_new_data = modify_new_data,
     drops = drops
   ))

diff --git a/tests/testthat/_snaps/new-expr-update.md b/tests/testthat/_snaps/new-expr-update.md
@@ -22,3 +22,15 @@
           residual <- res_lnorm(Density, fit, exp(log_sDensity))
       }
 
+# add new_expr_vec argument to update model
+
+    Code
+      new_expr(analysis)
+    Output
+      for (i in 1:length(Density)) {
+          fit[i] <- bIntercept + bYear * Year[i] + bHabitatQuality[HabitatQuality[i]] + 
+              bSiteYear[Site[i], YearFactor[i]]
+          log(prediction[i]) <- fit[i]
+          residual[i] <- res_lnorm(Density[i], fit[i], exp(log_sDensity))
+      }
+
diff --git a/tests/testthat/test-new-expr-update.R b/tests/testthat/test-new-expr-update.R
@@ -142,3 +142,79 @@ random_effects = list(bSiteYear = c("Site", "YearFactor")))
   local_edition(3)
   expect_snapshot(new_expr(analysis))
 })
+
+test_that("add new_expr_vec argument to update model", {
+  skip_if_not_installed("jmbr")
+
+  set_analysis_mode("quick")
+
+  data <- embr::density99
+  data$YearFactor <- factor(data$Year)
+
+  model <- model("model{
+
+  bIntercept ~ dnorm(0, 5^-2)
+  bYear ~ dnorm(0, .5^-2) # bYear2 ~ dnorm(0, .5^-2)
+
+  bHabitatQuality[1] <- 0
+  for(i in 2:nHabitatQuality) {
+    bHabitatQuality[i] ~ dnorm(0, 5.^-2) T(0,)
+  }
+
+  log_sSiteYear ~ dlnorm(0, 5^-2)
+  log_sDensity ~ dt(0, 5^-2, 4.5)
+
+  log(sSiteYear) <- log_sSiteYear
+  log(sDensity) <- log_sDensity
+
+  for(i in 1:nSite) {
+    for(j in 1:nYearFactor) {
+      bSiteYear[i,j] ~ dnorm(0, sSiteYear^-2)
+    }
+  }
+
+  for(i in 1:length(Density)) {
+    eDensity[i] <- bIntercept + bYear * Year[i] + bHabitatQuality[HabitatQuality[i]] + bSiteYear[Site[i], YearFactor[i]]
+    Density[i] ~ dlnorm(eDensity[i], sDensity^-2)
+  }
+  }",
+  new_expr = "
+  for(i in 1:length(Density)) {
+    fit[i] <- bIntercept + bYear * Year[i] + bHabitatQuality[HabitatQuality[i]] + bSiteYear[Site[i], YearFactor[i]]
+    log(prediction[i]) <- fit[i]
+    residual[i] <- res_lnorm(Density[i], fit[i], exp(log_sDensity))
+  }",
+  new_expr_vec = TRUE,
+  select_data = list("Year+" = numeric(), YearFactor = factor(),
+                   Site = factor(), Density = numeric(),
+                   HabitatQuality = factor()),
+  fixed = "^(b|l)", derived = "eDensity",
+  random_effects = list(bSiteYear = c("Site", "YearFactor")))
+
+  model <- update_model(
+    model,
+    new_expr =
+      "
+      for(i in 1:length(Density)) {
+        fit[i] <- bIntercept + bYear * Year[i] + bHabitatQuality[HabitatQuality[i]] + bSiteYear[Site[i], YearFactor[i]]
+        log(prediction[i]) <- fit[i]
+        residual[i] <- res_lnorm(Density[i], fit[i], exp(log_sDensity))
+      }",
+    new_expr_vec = FALSE
+    )
+  expect_output(expect_warning(analysis <- analyse(model, data = data)))
+
+  year <- predict(analysis, new_data = "Year")
+
+  expect_is(year, "tbl")
+  expect_identical(colnames(year), c("Site", "HabitatQuality", "Year", "Visit",
+                                     "Density", "YearFactor",
+                                     "estimate", "lower", "upper", "svalue"))
+  expect_true(all(year$lower < year$estimate))
+  expect_false(is.unsorted(year$estimate))
+
+  local_edition(3)
+  expect_snapshot(new_expr(analysis))
+})
+
+