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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 <- 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"))
hfits <- mhmkin(list(fits_synth_const, fits_synth_tc))
expect_known_output(
print(hfits),
"print_hfits_synth.txt")
expect_known_output(
print(illparms(hfits)),
"illparms_hfits_synth.txt")
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)))
expect_equal(as.character(illparms(hfit_sfo_tc)), character(0))
expect_silent(print(illparms(hfit_sfo_tc)))
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")
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))
})
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