From 2890a954d8443e1aab04a5c5fa128574fa6cde87 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Wed, 10 Aug 2022 14:21:26 +0200 Subject: Naming of random effects for reduced parameter models --- tests/testthat/summary_hfit_sfo_tc.txt | 58 ++++++++++++++++++++++++++++++++++ tests/testthat/test_mhmkin.R | 11 +++++++ 2 files changed, 69 insertions(+) create mode 100644 tests/testthat/summary_hfit_sfo_tc.txt (limited to 'tests') diff --git a/tests/testthat/summary_hfit_sfo_tc.txt b/tests/testthat/summary_hfit_sfo_tc.txt new file mode 100644 index 00000000..ad701cee --- /dev/null +++ b/tests/testthat/summary_hfit_sfo_tc.txt @@ -0,0 +1,58 @@ +saemix version used for fitting: Dummy 0.0 for testing +mkin version used for pre-fitting: Dummy 0.0 for testing +R version used for fitting: Dummy R version for testing +Date of fit: Dummy date for testing +Date of summary: Dummy date for testing + +Equations: +d_parent/dt = - k_parent * parent + +Data: +106 observations of 1 variable(s) grouped in 6 datasets + +Model predictions using solution type analytical + +Fitted in test time 0 s +Using 300, 100 iterations and 9 chains + +Variance model: Two-component variance function + +Mean of starting values for individual parameters: + parent_0 log_k_parent + 101 -3 + +Fixed degradation parameter values: +None + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 533 531 -261 + +Optimised parameters: + est. lower upper +parent_0 101 100 102 +log_k_parent -3 -4 -3 + +Correlation: + pr_0 +log_k_parent 0.1 + +Random effects: + est. lower upper +SD.log_k_parent 0.3 0.1 0.4 + +Variance model: + est. lower upper +a.1 0.91 0.64 1.17 +b.1 0.05 0.04 0.06 + +Backtransformed parameters: + est. lower upper +parent_0 1e+02 1e+02 1e+02 +k_parent 4e-02 3e-02 4e-02 + +Estimated disappearance times: + DT50 DT90 +parent 19 64 diff --git a/tests/testthat/test_mhmkin.R b/tests/testthat/test_mhmkin.R index e059948a..eced5583 100644 --- a/tests/testthat/test_mhmkin.R +++ b/tests/testthat/test_mhmkin.R @@ -29,4 +29,15 @@ test_that("Multiple hierarchical kinetic models can be fitted and diagnosed", { hfit_sfo_tc <- update(hfits[["SFO", "tc"]], covariance.model = diag(c(0, 1))) expect_equal(illparms(hfit_sfo_tc), character(0)) + + test_summary <- summary(hfit_sfo_tc) + test_summary$saemixversion <- "Dummy 0.0 for testing" + test_summary$mkinversion <- "Dummy 0.0 for testing" + test_summary$Rversion <- "Dummy R version for testing" + test_summary$date.fit <- "Dummy date for testing" + test_summary$date.summary <- "Dummy date for testing" + test_summary$time <- c(elapsed = "test time 0") + + expect_known_output(print(test_summary, digits = 1), + "summary_hfit_sfo_tc.txt") }) -- cgit v1.2.1