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context("Nonlinear mixed-effects models")
# Round error model parameters as they are not rounded in print methods
dfop_nlme_1$modelStruct$varStruct$const <-
signif(dfop_nlme_1$modelStruct$varStruct$const, 3)
dfop_nlme_1$modelStruct$varStruct$prop <-
signif(dfop_nlme_1$modelStruct$varStruct$prop, 4)
test_that("Print methods work", {
expect_known_output(print(fits[, 2:3], digits = 2), "print_mmkin_parent.txt")
expect_known_output(print(mixed(mmkin_sfo_1), digits = 2), "print_mmkin_sfo_1_mixed.txt")
expect_known_output(print(dfop_nlme_1, digits = 1), "print_dfop_nlme_1.txt")
expect_known_output(print(sfo_saem_1, digits = 1), "print_sfo_saem_1.txt")
})
test_that("nlme results are reproducible to some degree", {
test_summary <- summary(dfop_nlme_1)
test_summary$nlmeversion <- "Dummy 0.0 for testing"
test_summary$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_dfop_nlme_1.txt")
# The biphasic example data illustrate that DFOP parameters are difficult to
# quantify with the usual design
# k1 and k2 just fail the first test (lower bound of the ci), so we need to exclude it
dfop_no_k1_k2 <- c("parent_0", "k_m1", "f_parent_to_m1", "g")
dfop_sfo_pop_no_k1_k2 <- as.numeric(dfop_sfo_pop[dfop_no_k1_k2])
dfop_sfo_pop <- as.numeric(dfop_sfo_pop) # to remove names
ci_dfop_sfo_n <- summary(nlme_biphasic)$confint_back
expect_true(all(ci_dfop_sfo_n[dfop_no_k1_k2, "lower"] < dfop_sfo_pop_no_k1_k2))
expect_true(all(ci_dfop_sfo_n[, "upper"] > dfop_sfo_pop))
})
test_that("saemix results are reproducible for biphasic fits", {
test_summary <- summary(saem_biphasic_s)
test_summary$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$time <- c(elapsed = "test time 0")
expect_known_output(print(test_summary, digits = 1), "summary_saem_biphasic_s.txt")
dfop_sfo_pop <- as.numeric(dfop_sfo_pop)
no_k1 <- c(1, 2, 3, 5, 6)
no_k2 <- c(1, 2, 3, 4, 6)
no_k1_k2 <- c(1, 2, 3, 6)
ci_dfop_sfo_s_s <- summary(saem_biphasic_s)$confint_back
expect_true(all(ci_dfop_sfo_s_s[, "lower"] < dfop_sfo_pop))
expect_true(all(ci_dfop_sfo_s_s[, "upper"] > dfop_sfo_pop))
# k2 is not fitted well
ci_dfop_sfo_s_m <- summary(saem_biphasic_m)$confint_back
expect_true(all(ci_dfop_sfo_s_m[no_k2, "lower"] < dfop_sfo_pop[no_k2]))
expect_true(all(ci_dfop_sfo_s_m[no_k1, "upper"] > dfop_sfo_pop[no_k1]))
# I tried to only do few iterations in routine tests as this is so slow
# but then deSolve fails at some point (presumably at the switch between
# the two types of iterations)
#saem_biphasic_2 <- saem(mmkin_biphasic, solution_type = "deSolve",
# control = list(nbiter.saemix = c(10, 5), nbiter.burn = 5), quiet = TRUE)
skip("Fitting with saemix takes around 10 minutes when using deSolve")
saem_biphasic_2 <- saem(mmkin_biphasic, solution_type = "deSolve", quiet = TRUE)
# As with the analytical solution, k1 and k2 are not fitted well
ci_dfop_sfo_s_d <- summary(saem_biphasic_2)$confint_back
expect_true(all(ci_dfop_sfo_s_d[no_k2, "lower"] < dfop_sfo_pop[no_k2]))
expect_true(all(ci_dfop_sfo_s_d[no_k1, "upper"] > dfop_sfo_pop[no_k1]))
})
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