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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)
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", {
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)$confint_back
expect_true(all(ci_dfop_sfo_s_d[no_k2, "lower"] < dfop_sfo_pop[no_k2]))
expect_true(all(ci_dfop_sfo_s_d[no_k1, "upper"] > dfop_sfo_pop[no_k1]))
})
test_that("Reading spreadsheets, finding ill-defined parameters and covariate modelling", {
skip_on_cran()
data_path <- system.file(
"testdata", "lambda-cyhalothrin_soil_efsa_2014.xlsx",
package = "mkin")
ds_lambda <- read_spreadsheet(data_path, valid_datasets = c(1:4, 7:13))
covariates <- attr(ds_lambda, "covariates")
lambda_sforb <- mmkin("SFORB", ds_lambda, quiet = TRUE,
cores = n_cores,
error_model = "const")
lambda_sforb_saem_pH <- saem(lambda_sforb, covariates = covariates,
covariate_models = list(log_k_lambda_bound_free ~ pH))
expect_equal(
as.character(illparms(lambda_sforb_saem_pH)),
c("sd(lambda_free_0)", "sd(log_k_lambda_free_bound)"))
lambda_endpoints <- endpoints(lambda_sforb_saem_pH)
expect_equal(lambda_endpoints$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()
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_eigen<- saem(mmkin_sfo_sfo, solution_type = "eigen")
expect_equal(
endpoints(saem_sfo_sfo_saemix_analytical),
endpoints(saem_sfo_sfo_mkin_eigen))
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))
})
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