reduce example time for nmixture

R/families.R

@@ -97,10 +97,9 @@ student_t = function(link = 'identity'){
 #'                # add a 'cap' variable that defines the maximum latent N to
 #'                # marginalize over when estimating latent abundance; in other words
 #'                # how large do we realistically think the true abundance could be?
-#'                cap = 100) %>%
+#'                cap = 80) %>%
 #'  dplyr::select(- replicate) -> testdat
 #'
-#'
 #'# Now add another species that has a different temporal trend and a smaller
 #'# detection probability (0.45 for this species)
 #'testdat = testdat %>%
@@ -161,12 +160,11 @@ student_t = function(link = 'identity'){
 #'             # priors can be set in the usual way
 #'             priors = c(prior(std_normal(), class = b),
 #'                        prior(normal(1, 1.5), class = Intercept_trend)),
-#'             samples = 1000)
+#'             burnin = 300,
+#'             samples = 300)
 #'
 #' # The usual diagnostics
 #' summary(mod)
-#' code(mod)
-#' loo(mod)
 #'
 #' # Plotting conditional effects
 #' library(ggplot2)

tests/testthat/test-mvgam.R

@@ -3,6 +3,14 @@ context("mvgam")
 # JAGS setup should work, whether installed or not
 test_that("JAGS setups work", {
   simdat <- sim_mvgam()
+  mod <- mvgam(y ~ s(season),
+               trend_model = 'RW',
+               data = simdat$data_train,
+               family = poisson(),
+               use_stan = FALSE,
+               run_model = FALSE)
+  expect_true(inherits(mod, 'mvgam_prefit'))
+
   mod <- mvgam(y ~ s(season),
                trend_model = 'AR1',
                data = simdat$data_train,