Possibility to manually specify no_random_effects in mhmkin

1 files changed, 22 insertions, 12 deletions

diff --git a/tests/testthat/test_mhmkin.R b/tests/testthat/test_mhmkin.R
index e2339f28..da063326 100644
--- a/tests/testthat/test_mhmkin.R
+++ b/tests/testthat/test_mhmkin.R
@@ -3,8 +3,11 @@ context("Batch fitting and diagnosing hierarchical kinetic models")
 test_that("Multiple hierarchical kinetic models can be fitted and diagnosed", {
 
   skip_on_cran()
-  fits_synth_const <- suppressWarnings(
-    mmkin(c("SFO", "FOMC"), ds_sfo[1:6], cores = n_cores, quiet = TRUE))
+  fits_synth_const <- mmkin(c("SFO", "FOMC"), ds_fomc[1:6], cores = n_cores, quiet = TRUE)
+
+  expect_known_output(
+    print(fits_synth_const),
+    "print_fits_synth_const.txt")
 
   fits_synth_tc <- suppressWarnings(
     update(fits_synth_const, error_model = "tc"))
@@ -19,8 +22,8 @@ test_that("Multiple hierarchical kinetic models can be fitted and diagnosed", {
     print(illparms(hfits)),
     "illparms_hfits_synth.txt")
 
-  expect_equal(which.min(AIC(hfits)), 3)
-  expect_equal(which.min(BIC(hfits)), 3)
+  expect_equal(which.min(AIC(hfits)), 4)
+  expect_equal(which.min(BIC(hfits)), 4)
 
   hfit_sfo_tc <- update(hfits[["SFO", "tc"]],
     covariance.model = diag(c(0, 1)))
@@ -38,12 +41,19 @@ test_that("Multiple hierarchical kinetic models can be fitted and diagnosed", {
   expect_known_output(print(test_summary, digits = 1),
     "summary_hfit_sfo_tc.txt")
 
-  # It depends on the platform exactly which of the datasets fail to converge
-  # with FOMC, because they were generated to be SFO
-  skip_on_travis()
-
-  expect_known_output(
-    print(fits_synth_const),
-    "print_fits_synth_const.txt")
-
+  hfits_sfo_reduced <- update(hfits,
+    no_random_effect = illparms(hfits))
+  expect_equal(
+    as.character(illparms(hfits_sfo_reduced)),
+    rep("", 4))
+
+  # We can also manually set up an object specifying random effects to be
+  # excluded. Entries in the inital list have to be by column
+  no_ranef <- list("parent_0", "log_beta", "parent_0", c("parent_0", "log_beta"))
+  dim(no_ranef) <- c(2, 2)
+
+  hfits_sfo_reduced_2 <- update(hfits,
+    no_random_effect = no_ranef)
+  expect_equivalent(round(anova(hfits_sfo_reduced), 0),
+    round(anova(hfits_sfo_reduced_2), 0))
 })