make long-running tests quicker

tests/testthat/test-non-numeric-pids.R

@@ -12,13 +12,13 @@ test_that("Using numeric and non-numeric pids gives the same answer", {
   expect_equal(stan_data, stan_data_new, ignore_attr = TRUE)
 
   fit <- mod$fit(parallel_chains = 4,
-          iter_warmup = 50,
-          iter_sampling = 100,
+          iter_warmup = 10,
+          iter_sampling = 40,
           seed = 100)
 
   fit_new <- mod_new$fit(parallel_chains = 4,
-          iter_warmup = 50,
-          iter_sampling = 100,
+          iter_warmup = 10,
+          iter_sampling = 40,
           seed = 100)
 
   expect_equal(fit$draws(), fit_new$draws(), ignore_attr = TRUE)

tests/testthat/test-plots.R

@@ -28,7 +28,6 @@ test_that("Can plot prior predictions from model", {
 })
 
 test_that("Prior predictions from model are the same", {
-  skip_on_ci()
   data <- data.table::fread(system.file("delta_full.rds", package = "epikinetics"))
   priors <- biokinetics_priors(mu_values = c(4.1, 11, 65, 0.2, -0.01, 0.01),
                                sigma_values = c(2.0, 2.0, 3.0, 0.01, 0.01, 0.001))
@@ -41,7 +40,6 @@ test_that("Prior predictions from model are the same", {
 })
 
 test_that("Can plot input data", {
-  skip_on_ci()
   data <- data.table::fread(system.file("delta_full.rds", package = "epikinetics"))
   mod <- biokinetics$new(data = data)
   plot <- mod$plot_model_inputs()
@@ -57,7 +55,6 @@ mock_model <- function(name, package) {
 }
 
 test_that("Summarised and un-summarised population trajectories give same plots", {
-  skip_on_ci()
   # note that this is using a pre-fitted model with very few iterations, so the
   # fits won't look very good
   local_mocked_bindings(
@@ -69,11 +66,10 @@ test_that("Summarised and un-summarised population trajectories give same plots"
   trajectories <- mod$simulate_population_trajectories(summarise = TRUE)
   unsummarised_trajectories <- mod$simulate_population_trajectories(summarise = FALSE)
   vdiffr::expect_doppelganger("populationtrajectories", plot(trajectories))
- # vdiffr::expect_doppelganger("populationtrajectories", plot(unsummarised_trajectories))
+  vdiffr::expect_doppelganger("populationtrajectories", plot(unsummarised_trajectories))
 })
 
 test_that("Can plot population trajectories with data", {
-  skip_on_ci()
   local_mocked_bindings(
     stan_package_model = mock_model, .package = "instantiate"
   )

tests/testthat/test-relative-dates.R

@@ -4,26 +4,27 @@ test_that("Using relative and absolute dates gives the same answer", {
   dat_absolute <- data.table::fread(system.file("delta_full.rds", package = "epikinetics"))
   mod_absolute <- biokinetics$new(data = dat_absolute, covariate_formula = ~0 + infection_history)
   delta_absolute <- mod_absolute$fit(parallel_chains = 4,
-                   iter_warmup = 50,
+                   iter_warmup = 10,
                    iter_sampling = 100,
                    seed = 100)
 
   set.seed(1)
-  trajectories_absolute <- mod_absolute$simulate_individual_trajectories()
+  trajectories_absolute <- mod_absolute$simulate_individual_trajectories(summarise = FALSE)
 
   dat_relative <- data.table::fread(test_path("testdata", "delta_full_relative.rds"))
   mod_relative <- biokinetics$new(data = dat_relative, covariate_formula = ~0 + infection_history)
   delta_relative <- mod_relative$fit(parallel_chains = 4,
-                   iter_warmup = 50,
+                   iter_warmup = 10,
                    iter_sampling = 100,
                    seed = 100)
 
   set.seed(1)
-  trajectories_relative <- mod_relative$simulate_individual_trajectories()
+  trajectories_relative <- mod_relative$simulate_individual_trajectories(summarise = FALSE)
 
   # convert relative days to absolute
   min_date <- min(dat_absolute$day)
   trajectories_relative$calendar_day <- min_date + trajectories_relative$calendar_day
+  trajectories_relative$exposure_day <- min_date + trajectories_relative$exposure_day
 
   expect_equal(trajectories_relative, trajectories_absolute)