fix nmix test

nicholasjclark · Mar 26, 2024 · 272ad99 · 272ad99
1 parent 61f4067
commit 272ad99
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Showing 6 changed files with 49 additions and 29 deletions.
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: mvgam
 Title: Multivariate (Dynamic) Generalized Additive Models
-Version: 1.0.91
-Date: 2024-01-12
+Version: 1.1.0
+Date: 2024-03-26
 Authors@R:
     person("Nicholas J", "Clark", , "nicholas.j.clark1214@gmail.com", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0001-7131-3301"))

diff --git a/R/conditional_effects.R b/R/conditional_effects.R
@@ -87,6 +87,17 @@ conditional_effects.mvgam = function(x,
                             'detection', 'latent_N',
                             'expected'))
 
+  if(type == 'response'){
+    if(points){
+      points <- 0.5
+    } else {
+      points <- 0
+    }
+  } else {
+    points <- 0
+    rug <- FALSE
+  }
+
   # Can't plot points or rugs with binomial models due to the
   # cbind syntax
   if(rug){
@@ -103,17 +114,6 @@ conditional_effects.mvgam = function(x,
     }
   }
 
-  if(type == 'response'){
-    if(points){
-      points <- 0.5
-    } else {
-      points <- 0
-    }
-  } else {
-    points <- 0
-    rug <- FALSE
-  }
-
   if(use_def_effects){
     # Get all term labels in the model
     termlabs <- attr(terms(formula(x),

diff --git a/R/update.mvgam.R b/R/update.mvgam.R
@@ -13,19 +13,27 @@
 #' @examples
 #' \dontrun{
 #' # Simulate some data and fit a Poisson AR1 model
-#' simdat <- sim_mvgam(n_series = 1, trend_model = 'AR1')
+#' simdat <- sim_mvgam(n_series = 1, trend_model = AR())
 #' mod <- mvgam(y ~ s(season, bs = 'cc'),
-#'             trend_model = 'AR1',
-#'             data = simdat$data_train)
+#'              trend_model = AR(),
+#'              data = simdat$data_train)
 #' summary(mod)
+#' conditional_effects(mod, type = 'link')
 #'
 #' # Update to an AR2 model
-#' updated_mod <- update(mod, trend_model = 'AR2')
+#' updated_mod <- update(mod, trend_model = AR(p = 2))
 #' summary(updated_mod)
+#' conditional_effects(updated_mod, type = 'link')
 #'
-#' # Now update to a Negative Binomial AR1
-#' updated_mod <- update(mod, family = nb())
+#' # Now update to a Binomial AR1 by adding information on trials
+#' # requires that we supply newdata that contains the 'trials' variable
+#' simdat$data_train$trials <- max(simdat$data_train$y) + 15
+#' updated_mod <- update(mod,
+#'                       formula = cbind(y, trials) ~ s(season, bs = 'cc'),
+#'                       data = simdat$data_train,
+#'                       family = binomial())
 #' summary(updated_mod)
+#' conditional_effects(updated_mod, type = 'link')
 #'}
 #'@export
 update.mvgam = function(object,

diff --git a/man/update.mvgam.Rd b/man/update.mvgam.Rd
diff --git a/src/mvgam.dll b/src/mvgam.dll
diff --git a/tests/testthat/test-nmixture.R b/tests/testthat/test-nmixture.R
@@ -22,7 +22,9 @@ test_that("cap must be supplied in data", {
                'Max abundances must be supplied as a variable named "cap" for N-mixture models',
                fixed = TRUE)
 
-  poisdat$data_train$cap <- 100
+  poisdat$data_train$cap <- rpois(NROW(poisdat$data_train),
+                                  lambda = 5) +
+    max(poisdat$data_train$y, na.rm = TRUE)
   expect_error(mvgam(formula = y ~ s(season),
                      trend_formula = ~ s(season) +
                        trend,
@@ -45,10 +47,12 @@ test_that("cap must be supplied in data", {
                fixed = TRUE)
 })
 
-poisdat$data_train$cap <- 100
+poisdat$data_train$cap <- rpois(NROW(poisdat$data_train),
+                               lambda = 5) +
+  max(poisdat$data_train$y, na.rm = TRUE)
 poisdat$data_test$cap <- rpois(NROW(poisdat$data_test),
                                lambda = 5) +
-  max(poisdat$data_test$y)
+  max(poisdat$data_test$y, na.rm = TRUE)
 
 test_that("latent process intercept is allowed in nmixtures", {
   prior_df <- get_mvgam_priors(formula = y ~ s(season),