context("Nonlinear mixed-effects models")
test_that("Parent fits using saemix are correctly implemented", {
skip_if(!saemix_available)
expect_error(saem(fits), "Only row objects")
# Some fits were done in the setup script
mmkin_sfo_2 <- update(mmkin_sfo_1, fixed_initials = c(parent = 100))
expect_error(update(mmkin_sfo_1, models = c("SFOOO")), "Please supply models.*")
sfo_saem_2 <- saem(mmkin_sfo_1, quiet = TRUE, transformations = "mkin")
sfo_saem_3 <- expect_error(saem(mmkin_sfo_2, quiet = TRUE), "at least two parameters")
s_sfo_s1 <- summary(sfo_saem_1)
s_sfo_s2 <- summary(sfo_saem_2)
sfo_nlme_1 <- expect_warning(nlme(mmkin_sfo_1), "not converge")
s_sfo_n <- summary(sfo_nlme_1)
# Compare with input
expect_equal(round(s_sfo_s2$confint_ranef["SD.log_k_parent", "est."], 1), 0.3)
# k_parent is a bit different from input 0.03 here
expect_equal(round(s_sfo_s1$confint_back["k_parent", "est."], 3), 0.035)
expect_equal(round(s_sfo_s2$confint_back["k_parent", "est."], 3), 0.035)
# But the result is pretty unanimous between methods
expect_equal(round(s_sfo_s1$confint_back["k_parent", "est."], 3),
round(s_sfo_s2$confint_back["k_parent", "est."], 3))
expect_equal(round(s_sfo_s1$confint_back["k_parent", "est."], 3),
round(s_sfo_n$confint_back["k_parent", "est."], 3))
mmkin_fomc_1 <- mmkin("FOMC", ds_fomc, quiet = TRUE, error_model = "tc", cores = n_cores)
fomc_saem_1 <- saem(mmkin_fomc_1, quiet = TRUE)
ci_fomc_s1 <- summary(fomc_saem_1)$confint_back
fomc_pop <- as.numeric(fomc_pop)
expect_true(all(ci_fomc_s1[, "lower"] < fomc_pop))
expect_true(all(ci_fomc_s1[, "upper"] > fomc_pop))
mmkin_fomc_2 <- update(mmkin_fomc_1, state.ini = 100, fixed_initials = "parent")
fomc_saem_2 <- saem(mmkin_fomc_2, quiet = TRUE, transformations = "mkin")
ci_fomc_s2 <- summary(fomc_saem_2)$confint_back
expect_true(all(ci_fomc_s2[, "lower"] < fomc_pop[2:3]))
expect_true(all(ci_fomc_s2[, "upper"] > fomc_pop[2:3]))
s_dfop_s1 <- summary(dfop_saemix_1)
s_dfop_s2 <- summary(dfop_saemix_2)
s_dfop_n <- summary(dfop_nlme_1)
dfop_pop <- as.numeric(dfop_pop)
expect_true(all(s_dfop_s1$confint_back[, "lower"] < dfop_pop))
expect_true(all(s_dfop_s1$confint_back[, "upper"] > dfop_pop))
expect_true(all(s_dfop_s2$confint_back[, "lower"] < dfop_pop))
expect_true(all(s_dfop_s2$confint_back[, "upper"] > dfop_pop))
dfop_mmkin_means_trans_tested <- mean_degparms(mmkin_dfop_1, test_log_parms = TRUE)
dfop_mmkin_means_trans <- apply(parms(mmkin_dfop_1, transformed = TRUE), 1, mean)
dfop_mmkin_means_tested <- backtransform_odeparms(dfop_mmkin_means_trans_tested, mmkin_dfop_1$mkinmod)
dfop_mmkin_means <- backtransform_odeparms(dfop_mmkin_means_trans, mmkin_dfop_1$mkinmod)
# We get < 20% deviations for parent_0 and k1 by averaging the transformed parameters
# If we average only parameters passing the t-test, the deviation for k2 is also < 20%
rel_diff_mmkin <- (dfop_mmkin_means - dfop_pop) / dfop_pop
rel_diff_mmkin_tested <- (dfop_mmkin_means_tested - dfop_pop) / dfop_pop
expect_true(all(rel_diff_mmkin[c("parent_0", "k1")] < 0.20))
expect_true(all(rel_diff_mmkin_tested[c("parent_0", "k1", "k2")] < 0.20))
# We get < 30% deviations with transformations made in mkin
rel_diff_1 <- (s_dfop_s1$confint_back[, "est."] - dfop_pop) / dfop_pop
expect_true(all(rel_diff_1 < 0.5))
# We get < 20% deviations with transformations made in saemix
rel_diff_2 <- (s_dfop_s2$confint_back[, "est."] - dfop_pop) / dfop_pop
expect_true(all(rel_diff_2 < 0.2))
mmkin_hs_1 <- mmkin("HS", ds_hs, quiet = TRUE, error_model = "const", cores = n_cores)
hs_saem_1 <- saem(mmkin_hs_1, quiet = TRUE)
ci_hs_s1 <- summary(hs_saem_1)$confint_back
hs_pop <- as.numeric(hs_pop)
# expect_true(all(ci_hs_s1[, "lower"] < hs_pop)) # k1 is overestimated
expect_true(all(ci_hs_s1[, "upper"] > hs_pop))
mmkin_hs_2 <- update(mmkin_hs_1, state.ini = 100, fixed_initials = "parent")
hs_saem_2 <- saem(mmkin_hs_2, quiet = TRUE)
ci_hs_s2 <- summary(hs_saem_2)$confint_back
#expect_true(all(ci_hs_s2[, "lower"] < hs_pop[2:4])) # k1 again overestimated
expect_true(all(ci_hs_s2[, "upper"] > hs_pop[2:4]))
# HS would likely benefit from implemenation of transformations = "saemix"
})
test_that("Print methods work", {
expect_known_output(print(fits[, 2:3], digits = 2), "print_mmkin_parent.txt")
expect_known_output(print(mmkin_biphasic_mixed, digits = 2), "print_mmkin_biphasic_mixed.txt")
expect_known_output(print(nlme_biphasic, digits = 1), "print_nlme_biphasic.txt")
skip_if(!saemix_available)
expect_known_output(print(sfo_saem_1, digits = 1), "print_sfo_saem_1.txt")
})
test_that("nlme results are reproducible to some degree", {
test_summary <- summary(nlme_biphasic)
test_summary$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$time <- c(elapsed = "test time 0")
expect_known_output(print(test_summary, digits = 1), "summary_nlme_biphasic_s.txt")
# k1 just fails the first test (lower bound of the ci), so we need to excluded it
dfop_no_k1 <- c("parent_0", "k_m1", "f_parent_to_m1", "k2", "g")
dfop_sfo_pop_no_k1 <- as.numeric(dfop_sfo_pop[dfop_no_k1])
dfop_sfo_pop <- as.numeric(dfop_sfo_pop)
ci_dfop_sfo_n <- summary(nlme_biphasic)$confint_back
expect_true(all(ci_dfop_sfo_n[dfop_no_k1, "lower"] < dfop_sfo_pop_no_k1))
expect_true(all(ci_dfop_sfo_n[, "upper"] > dfop_sfo_pop))
})
test_that("saem results are reproducible for biphasic fits", {
skip_if(!saemix_available)
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 = 2), "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)$confint_back
# k1 and k2 are overestimated
expect_true(all(ci_dfop_sfo_s_s[no_k1_k2, "lower"] < dfop_sfo_pop[no_k1_k2]))
expect_true(all(ci_dfop_sfo_s_s[, "upper"] > dfop_sfo_pop))
# k1 and k2 are not fitted well
ci_dfop_sfo_s_m <- summary(saem_biphasic_m)$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]))
})
