From 5cf58feff3c19797bde7697331d349cb45bbe9f6 Mon Sep 17 00:00:00 2001
From: athowes <xwg3@cdc.gov>
Date: Tue, 6 Aug 2024 10:03:12 +0100
Subject: [PATCH] Fix to rebase

---
 tests/testthat/test-int-latent_individual.R | 18 +++++++++++-------
 tests/testthat/test-unit-postprocess.R      |  4 ++--
 2 files changed, 13 insertions(+), 9 deletions(-)

diff --git a/tests/testthat/test-int-latent_individual.R b/tests/testthat/test-int-latent_individual.R
index 89b13d3be..addd71bf2 100644
--- a/tests/testthat/test-int-latent_individual.R
+++ b/tests/testthat/test-int-latent_individual.R
@@ -33,7 +33,7 @@ test_that("epidist.epidist_latent_individual samples from the prior according to
   skip_on_cran()
   set.seed(1)
   prior_samples <- epidist(
-    data = prep_obs, fn = brms::brm, sample_prior = "only", seed = 1
+    data = prep_obs, fn = brms::brm, sample_prior = "only", seed = 1, silent = 2
   )
   pred <- predict_delay_parameters(prior_samples)
   family <- epidist_family(data = prep_obs, family = brms::lognormal())
@@ -53,7 +53,7 @@ test_that("epidist.epidist_latent_individual fits and the MCMC converges in the
   # Note: this test is stochastic. See note at the top of this script
   skip_on_cran()
   set.seed(1)
-  fit <- epidist(data = prep_obs, seed = 1)
+  fit <- epidist(data = prep_obs, seed = 1, silent = 2)
   expect_s3_class(fit, "brmsfit")
   expect_s3_class(fit, "epidist_fit")
   expect_convergence(fit)
@@ -63,7 +63,7 @@ test_that("epidist.epidist_latent_individual recovers the simulation settings fo
   # Note: this test is stochastic. See note at the top of this script
   skip_on_cran()
   set.seed(1)
-  fit <- epidist(data = prep_obs, seed = 1)
+  fit <- epidist(data = prep_obs, seed = 1, silent = 2)
   pred <- predict_delay_parameters(fit)
   # Unclear the extent to which we should expect parameter recovery here
   expect_equal(mean(pred$mu), meanlog, tolerance = 0.1)
@@ -93,7 +93,8 @@ test_that("epidist.epidist_latent_individual fits and the MCMC converges in the
     data = prep_obs_gamma,
     family = stats::Gamma(link = "log"),
     formula = brms::bf(mu ~ 1, shape ~ 1),
-    seed = 1
+    seed = 1,
+    silent = 2
   )
   expect_s3_class(fit_gamma, "brmsfit")
   expect_s3_class(fit_gamma, "epidist_fit")
@@ -108,7 +109,8 @@ test_that("epidist.epidist_latent_individual recovers the simulation settings fo
     data = prep_obs_gamma,
     family = stats::Gamma(link = "log"),
     formula = brms::bf(mu ~ 1, shape ~ 1),
-    seed = 1
+    seed = 1,
+    silent = 2
   )
   # Using the Stan parameterisation of the gamma distribution
   draws_gamma <- posterior::as_draws_df(fit_gamma$fit)
@@ -147,7 +149,8 @@ test_that("epidist.epidist_latent_individual recovers no sex effect when none is
   fit_sex <- epidist(
     data = prep_obs,
     formula = brms::bf(mu ~ 1 + sex, sigma ~ 1 + sex),
-    seed = 1
+    seed = 1,
+    silent = 2
   )
   draws <- posterior::as_draws_df(fit_sex$fit)
   expect_equal(mean(draws$b_sex), 0, tolerance = 0.2)
@@ -162,7 +165,8 @@ test_that("epidist.epidist_latent_individual fits and the MCMC converges for an
   fit_sex <- epidist(
     data = prep_obs,
     formula = brms::bf(mu ~ 1 + sex, sigma ~ 1 + sex),
-    seed = 1
+    seed = 1,
+    silent = 2
   )
   expect_s3_class(fit_sex, "brmsfit")
   expect_s3_class(fit_sex, "epidist_fit")
diff --git a/tests/testthat/test-unit-postprocess.R b/tests/testthat/test-unit-postprocess.R
index 64c5a6071..ea28fa280 100644
--- a/tests/testthat/test-unit-postprocess.R
+++ b/tests/testthat/test-unit-postprocess.R
@@ -2,7 +2,7 @@ test_that("predict_delay_parameters works with NULL newdata and the latent logno
   skip_on_cran()
   set.seed(1)
   prep_obs <- as_latent_individual(sim_obs)
-  fit <- epidist(data = prep_obs, seed = 1)
+  fit <- epidist(data = prep_obs, seed = 1, silent = 2)
   pred <- predict_delay_parameters(fit)
   expect_s3_class(pred, "data.table")
   expect_named(pred, c("index", "draw", "mu", "sigma", "mean", "sd"))
@@ -16,7 +16,7 @@ test_that("predict_delay_parameters accepts newdata arguments", { # nolint: line
   skip_on_cran()
   set.seed(1)
   prep_obs <- as_latent_individual(sim_obs)
-  fit <- epidist(data = prep_obs, seed = 1)
+  fit <- epidist(data = prep_obs, seed = 1, silent = 2)
   n <- 5
   pred <- predict_delay_parameters(fit, newdata = prep_obs[1:n, ])
   expect_s3_class(pred, "data.table")