diff options
Diffstat (limited to 'tests/testthat/test_saemix_parent.R')
| -rw-r--r-- | tests/testthat/test_saemix_parent.R | 135 |
1 files changed, 94 insertions, 41 deletions
diff --git a/tests/testthat/test_saemix_parent.R b/tests/testthat/test_saemix_parent.R index 731228d9..39efa18f 100644 --- a/tests/testthat/test_saemix_parent.R +++ b/tests/testthat/test_saemix_parent.R @@ -4,37 +4,81 @@ test_that("Parent fits using saemix are correctly implemented", { skip_on_cran() expect_error(saem(fits), "Only row objects") - # Some fits were done in the setup script + + # SFO + # mmkin_sfo_1 was generated in the setup script + # We did not introduce variance of parent_0 in the data generation + # This is correctly detected + expect_equal(illparms(sfo_saem_1), "sd(parent_0)") + # So we have also done a fit without this variance + expect_equal(illparms(sfo_saem_1_reduced), character(0)) + + # We cannot currently do the fit with completely fixed initial values mmkin_sfo_2 <- update(mmkin_sfo_1, fixed_initials = c(parent = 100)) + sfo_saem_3 <- expect_error(saem(mmkin_sfo_2, quiet = TRUE), "at least two parameters") + + # We get an error if we do not supply a suitable model specification 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_saem_1_mkin <- saem(mmkin_sfo_1, quiet = TRUE, transformations = "mkin") + expect_equal(illparms(sfo_saem_1_mkin), "sd(parent_0)") + sfo_saem_1_reduced_mkin <- update(sfo_saem_1_mkin, no_random_effect = "parent_0") + + # The endpoints obtained do not depend on the transformation + expect_equal(endpoints(sfo_saem_1), endpoints(sfo_saem_1_mkin), tol = 0.01) + expect_equal(endpoints(sfo_saem_1_reduced), endpoints(sfo_saem_1_reduced_mkin), tol = 0.01) + + s_sfo_saem_1 <- summary(sfo_saem_1) + s_sfo_saem_1_reduced <- summary(sfo_saem_1_reduced) + s_sfo_saem_1_mkin <- summary(sfo_saem_1_mkin) + s_sfo_saem_1_reduced_mkin <- summary(sfo_saem_1_reduced_mkin) sfo_nlme_1 <- expect_warning(nlme(mmkin_sfo_1), "not converge") - s_sfo_n <- summary(sfo_nlme_1) + s_sfo_nlme_1 <- summary(sfo_nlme_1) # Compare with input - expect_equal(round(s_sfo_s2$confint_ranef["SD.log_k_parent", "est."], 1), 0.3) + expect_equal(round(s_sfo_saem_1$confint_ranef["SD.k_parent", "est."], 1), 0.3) + expect_equal(round(s_sfo_saem_1_mkin$confint_ranef["SD.log_k_parent", "est."], 1), 0.3) # k_parent is a bit different from input 0.03 here - expect_equal(round(s_sfo_s1$confint_back["k_parent", "est."], 3), 0.035) - expect_equal(round(s_sfo_s2$confint_back["k_parent", "est."], 3), 0.035) + expect_equal(round(s_sfo_saem_1$confint_back["k_parent", "est."], 3), 0.035) + expect_equal(round(s_sfo_saem_1_mkin$confint_back["k_parent", "est."], 3), 0.035) # But the result is pretty unanimous between methods - expect_equal(round(s_sfo_s1$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$confint_back["k_parent", "est."], 3)) - + expect_equal(round(s_sfo_saem_1_reduced$confint_back["k_parent", "est."], 3), + round(s_sfo_saem_1$confint_back["k_parent", "est."], 3)) + expect_equal(round(s_sfo_saem_1_mkin$confint_back["k_parent", "est."], 3), + round(s_sfo_saem_1$confint_back["k_parent", "est."], 3)) + expect_equal(round(s_sfo_saem_1_reduced_mkin$confint_back["k_parent", "est."], 3), + round(s_sfo_saem_1$confint_back["k_parent", "est."], 3)) + expect_equal(round(s_sfo_nlme_1$confint_back["k_parent", "est."], 3), + round(s_sfo_saem_1$confint_back["k_parent", "est."], 3)) + + # Compare fits with heavy rounding to avoid platform dependent results + anova_sfo <- anova( + sfo_saem_1, sfo_saem_1_reduced, + sfo_saem_1_mkin, sfo_saem_1_reduced_mkin, + test = TRUE) + anova_sfo_rounded <- round(anova_sfo, 0) + expect_known_output(print(anova_sfo_rounded), file = "anova_sfo_saem.txt") + + # Check the influence of an invented covariate + set.seed(123456) # In my first attempt I hit a false positive by chance... + pH <- data.frame(pH = runif(15, 5, 8), row.names = as.character(1:15)) + sfo_saem_pH <- update(sfo_saem_1_reduced_mkin, covariates = pH, + covariate_models = list(log_k_parent ~ pH)) + # We expect that this is not significantly better, as the covariate values were completely random + expect_true(anova(sfo_saem_1_reduced_mkin, sfo_saem_pH, test = TRUE)[2, "Pr(>Chisq)"] > 0.05) + + # FOMC mmkin_fomc_1 <- mmkin("FOMC", ds_fomc, quiet = TRUE, error_model = "tc", cores = n_cores) - fomc_saem_1 <- saem(mmkin_fomc_1, quiet = TRUE) + fomc_saem_1 <- saem(mmkin_fomc_1, quiet = TRUE, transformations = "saemix", no_random_effect = "parent_0") + fomc_saem_2 <- update(fomc_saem_1, transformations = "mkin") ci_fomc_s1 <- summary(fomc_saem_1)$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)) + expect_equal(endpoints(fomc_saem_1), endpoints(fomc_saem_2), tol = 0.01) mmkin_fomc_2 <- update(mmkin_fomc_1, state.ini = 100, fixed_initials = "parent") fomc_saem_2 <- saem(mmkin_fomc_2, quiet = TRUE, transformations = "mkin") @@ -43,62 +87,71 @@ test_that("Parent fits using saemix are correctly implemented", { expect_true(all(ci_fomc_s2[, "lower"] < fomc_pop[2:3])) expect_true(all(ci_fomc_s2[, "upper"] > fomc_pop[2:3])) + # DFOP + dfop_saemix_2 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "saemix", + no_random_effect = "parent_0") + 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)) + + # When using DFOP with mkin transformations, k1 and k2 are sometimes swapped + swap_k1_k2 <- function(p) c(p[1], p[3], p[2], 1 - p[4]) + expect_true(all(s_dfop_s1$confint_back[, "lower"] < swap_k1_k2(dfop_pop))) + expect_true(all(s_dfop_s1$confint_back[, "upper"] > swap_k1_k2(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 < 20% deviations with transformations made in mkin - rel_diff_1 <- (s_dfop_s1$confint_back[, "est."] - dfop_pop) / dfop_pop + # We get < 20% deviations with transformations made in mkin (need to swap k1 and k2) + rel_diff_1 <- (swap_k1_k2(s_dfop_s1$confint_back[, "est."]) - dfop_pop) / dfop_pop expect_true(all(rel_diff_1 < 0.20)) # We get < 20% deviations with transformations made in saemix rel_diff_2 <- (s_dfop_s2$confint_back[, "est."] - dfop_pop) / dfop_pop expect_true(all(rel_diff_2 < 0.2)) + # SFORB + mmkin_sforb_1 <- mmkin("SFORB", ds_dfop, quiet = TRUE, cores = n_cores) + sforb_saemix_1 <- saem(mmkin_sforb_1, quiet = TRUE, + no_random_effect = c("parent_free_0"), + transformations = "mkin") + sforb_saemix_2 <- saem(mmkin_sforb_1, quiet = TRUE, + no_random_effect = c("parent_free_0"), + transformations = "saemix") + expect_equal( + log(endpoints(dfop_saemix_1)$distimes[1:2]), + log(endpoints(sforb_saemix_1)$distimes[1:2]), tolerance = 0.03) + expect_equal( + log(endpoints(sforb_saemix_1)$distimes[1:2]), + log(endpoints(sforb_saemix_2)$distimes[1:2]), tolerance = 0.01) + mmkin_hs_1 <- mmkin("HS", ds_hs, quiet = TRUE, error_model = "const", cores = n_cores) hs_saem_1 <- saem(mmkin_hs_1, quiet = TRUE) + hs_saem_2 <- saem(mmkin_hs_1, quiet = TRUE, transformations = "mkin") + expect_equal(endpoints(hs_saem_1), endpoints(hs_saem_2), tol = 0.01) ci_hs_s1 <- summary(hs_saem_1)$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[, "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)$confint_back - - #expect_true(all(ci_hs_s2[, "lower"] < hs_pop[2:4])) # k1 again overestimated - expect_true(all(ci_hs_s2[, "upper"] > hs_pop[2:4])) + hs_saem_3 <- saem(mmkin_hs_2, quiet = TRUE) + ci_hs_s3 <- summary(hs_saem_3)$confint_back - # HS would likely benefit from implemenation of transformations = "saemix" + #expect_true(all(ci_hs_s3[, "lower"] < hs_pop[2:4])) # k1 again overestimated + expect_true(all(ci_hs_s3[, "upper"] > hs_pop[2:4])) }) test_that("We can also use mkin solution methods for saem", { expect_error(saem(mmkin_dfop_1, quiet = TRUE, transformations = "saemix", solution_type = "analytical"), "saemix transformations is only supported if an analytical solution is implemented" ) - skip_on_cran() # This still takes almost 2.5 minutes although we do not solve ODEs + skip("This still takes almost 2.5 minutes although we do not solve ODEs") dfop_saemix_3 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "mkin", - solution_type = "analytical") + solution_type = "analytical", no_random_effect = "parent_0") distimes_dfop <- endpoints(dfop_saemix_1)$distimes distimes_dfop_analytical <- endpoints(dfop_saemix_3)$distimes rel_diff <- abs(distimes_dfop_analytical - distimes_dfop) / distimes_dfop |