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) dfop_sfo_pop <- attr(ds_dfop_sfo, "pop") 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_reduced, digits = 1), "print_sfo_saem_1_reduced.txt") skip_on_cran() # The following test is platform dependent and fails on # win-builder with current (18 Nov 2022) R-devel, on the Linux R-devel CRAN check systems # and also using R-devel locally expect_known_output(print(dfop_saem_1, digits = 1), "print_dfop_saem_1.txt") }) test_that("nlme results are reproducible to some degree", { skip_on_cran() 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]) ci_dfop_sfo_n <- summary(nlme_dfop_sfo)$confint_back expect_true(all(ci_dfop_sfo_n[dfop_no_k1_k2, "lower"] < dfop_sfo_pop_no_k1_k2)) expect_true(all(ci_dfop_sfo_n[, "upper"] > as.numeric(dfop_sfo_pop))) }) test_that("saemix results are reproducible for biphasic fits", { skip_on_cran() saem_dfop_sfo_s <- saem(mmkin_dfop_sfo, transformations = "saemix", quiet = TRUE) test_summary <- summary(saem_dfop_sfo_s) test_summary$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$time <- c(elapsed = "test time 0") expect_known_output(print(test_summary, digits = 1), "summary_saem_dfop_sfo_s.txt") dfop_sfo_pop <- as.numeric(dfop_sfo_pop) no_k1 <- c(1, 2, 3, 5, 6) no_k2 <- c(1, 2, 3, 4, 6) no_k1_k2 <- c(1, 2, 3, 6) ci_dfop_sfo_s_s <- summary(saem_dfop_sfo_s)$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)) # 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_dfop_sfo_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_dfop_sfo_2 <- saem(mmkin_dfop_sfo, solution_type = "deSolve", quiet = TRUE) # As with the analytical solution, k1 and k2 are not fitted well ci_dfop_sfo_s_d <- summary(saem_dfop_sfo_2)$confint_back expect_true(all(ci_dfop_sfo_s_d[no_k2, "lower"] < dfop_sfo_pop[no_k2])) expect_true(all(ci_dfop_sfo_s_d[no_k1, "upper"] > dfop_sfo_pop[no_k1])) }) test_that("Reading spreadsheets, finding ill-defined parameters and covariate modelling", { skip_on_cran() skip_on_travis() data_path <- system.file( "testdata", "lambda-cyhalothrin_soil_efsa_2014.xlsx", package = "mkin") ds_lambda <- read_spreadsheet(data_path, valid_datasets = c(1:4, 7:13)) covariates <- attr(ds_lambda, "covariates") lambda_sforb <- mmkin("SFORB", ds_lambda, quiet = TRUE, cores = n_cores, error_model = "const") lambda_sforb_saem_pH <- saem(lambda_sforb, covariates = covariates, covariate_models = list(log_k_lambda_bound_free ~ pH)) expect_equal( as.character(illparms(lambda_sforb_saem_pH)), c("sd(lambda_free_0)", "sd(log_k_lambda_free_bound)")) lambda_endpoints <- endpoints(lambda_sforb_saem_pH) expect_equal(lambda_endpoints$covariates$pH, 6.45) expect_equal( round(as.numeric(lambda_endpoints$distimes), 0), c(47, 422, 127, 7, 162)) }) test_that("SFO-SFO saemix specific analytical solution work", { skip_on_cran() skip_on_travis() SFO_SFO <- mkinmod(DMTA = mkinsub("SFO", "M23"), M23 = mkinsub("SFO"), quiet = TRUE) mmkin_sfo_sfo <- mmkin(list("SFO-SFO" = SFO_SFO), dmta_ds, quiet = TRUE, cores = n_cores, error_model = "const") saem_sfo_sfo_saemix_analytical <- saem(mmkin_sfo_sfo) expect_error(saem(mmkin_sfo_sfo, solution_type = "analytical"), "not supported") saem_sfo_sfo_mkin_desolve <- saem(mmkin_sfo_sfo, solution_type = "deSolve") expect_equal( endpoints(saem_sfo_sfo_saemix_analytical), endpoints(saem_sfo_sfo_mkin_desolve)) skip("This is seldom used, so save some time") saem_sfo_sfo_mkin_eigen<- saem(mmkin_sfo_sfo, solution_type = "eigen") expect_equal( endpoints(saem_sfo_sfo_saemix_analytical), endpoints(saem_sfo_sfo_mkin_eigen)) })