context("Dimethenamid data from 2018")
# Data
dmta_ds <- lapply(1:7, function(i) {
ds_i <- dimethenamid_2018$ds[[i]]$data
ds_i[ds_i$name == "DMTAP", "name"] <- "DMTA"
ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i]
ds_i
})
names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title)
dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]])
dmta_ds[["Elliot 1"]] <- dmta_ds[["Elliot 2"]] <- NULL
# mkin
dmta_dfop <- mmkin("DFOP", dmta_ds, quiet = TRUE)
dmta_dfop_tc <- mmkin("DFOP", dmta_ds, error_model = "tc", quiet = TRUE)
test_that("Different backends get consistent results for DFOP tc, dimethenamid data", {
# nlme
expect_warning(
nlme_dfop_tc <- nlme(dmta_dfop_tc),
"Iteration 3, .* false convergence")
ints_nlme <- intervals(nlme_dfop_tc)
# saemix
saem_saemix_dfop_tc <- saem(dmta_dfop_tc)
ints_saemix <- intervals(saem_saemix_dfop_tc)
# saemix mkin transformations
saem_saemix_dfop_tc_mkin <- saem(dmta_dfop_tc, transformations = "mkin")
ints_saemix_mkin <- intervals(saem_saemix_dfop_tc_mkin)
# nlmixr saem
saem_nlmixr_dfop_tc <- nlmixr(dmta_dfop_tc, est = "saem",
control = nlmixr::saemControl(nBurn = 300, nEm = 100, nmc = 9, print = 0))
ints_nlmixr_saem <- intervals(saem_nlmixr_dfop_tc)
# nlmixr focei
# We get three warnings about nudged etas, the initial optimization and
# gradient problems with initial estimate and covariance
# We need to capture output, otherwise it pops up in testthat output
expect_warning(tmp <- capture_output(focei_nlmixr_dfop_tc <- nlmixr(
dmta_dfop_tc, est = "focei",
control = nlmixr::foceiControl(print = 0), all = TRUE)))
ints_nlmixr_focei <- intervals(focei_nlmixr_dfop_tc)
# Fixed effects
## saemix vs. nlme
expect_true(all(ints_saemix$fixed[, "est."] >
backtransform_odeparms(ints_nlme$fixed[, "lower"], dmta_dfop$mkinmod)))
expect_true(all(ints_saemix$fixed[, "est."] <
backtransform_odeparms(ints_nlme$fixed[, "upper"], dmta_dfop$mkinmod)))
## saemix mkin vs. nlme
expect_true(all(ints_saemix_mkin$fixed[, "est."] >
backtransform_odeparms(ints_nlme$fixed[, "lower"], dmta_dfop$mkinmod)))
expect_true(all(ints_saemix_mkin$fixed[, "est."] <
backtransform_odeparms(ints_nlme$fixed[, "upper"], dmta_dfop$mkinmod)))
## nlmixr saem vs. nlme
expect_true(all(ints_nlmixr_saem$fixed[, "est."] >
backtransform_odeparms(ints_nlme$fixed[, "lower"], dmta_dfop$mkinmod)))
expect_true(all(ints_nlmixr_saem$fixed[, "est."] <
backtransform_odeparms(ints_nlme$fixed[, "upper"], dmta_dfop$mkinmod)))
## nlmixr focei vs. nlme
expect_true(all(ints_nlmixr_focei$fixed[, "est."] >
backtransform_odeparms(ints_nlme$fixed[, "lower"], dmta_dfop$mkinmod)))
expect_true(all(ints_nlmixr_focei$fixed[, "est."] <
backtransform_odeparms(ints_nlme$fixed[, "upper"], dmta_dfop$mkinmod)))
# Random effects
## for saemix with saemix transformations, the comparison would be complicated...
## saemix mkin vs. nlme
expect_true(all(ints_saemix$random[, "est."] >
backtransform_odeparms(ints_nlme$reStruct$ds[, "lower"], dmta_dfop$mkinmod)))
expect_true(all(ints_saemix$fixed[, "est."] <
backtransform_odeparms(ints_nlme$fixed[, "upper"], dmta_dfop$mkinmod)))
## nlmixr saem vs. nlme
expect_true(all(ints_nlmixr_saem$random[, "est."] >
backtransform_odeparms(ints_nlme$reStruct$ds[, "lower"], dmta_dfop$mkinmod)))
expect_true(all(ints_nlmixr_saem$random[, "est."] <
backtransform_odeparms(ints_nlme$reStruct$ds[, "upper"], dmta_dfop$mkinmod)))
## nlmixr focei vs. nlme
expect_true(all(ints_nlmixr_focei$random[, "est."] >
backtransform_odeparms(ints_nlme$reStruct$ds[, "lower"], dmta_dfop$mkinmod)))
expect_true(all(ints_nlmixr_focei$random[, "est."] <
backtransform_odeparms(ints_nlme$reStruct$ds[, "upper"], dmta_dfop$mkinmod)))
# Variance function
skip_on_travis() # For some reason this fails on Travis
# saemix vs. nlme
expect_true(all(ints_saemix[[3]][, "est."] >
ints_nlme$varStruct[, "lower"]))
expect_true(all(ints_saemix[[3]][, "est."] <
ints_nlme$varStruct[, "upper"]))
# saemix with mkin transformations vs. nlme
expect_true(all(ints_saemix_mkin[[3]][, "est."] >
ints_nlme$varStruct[, "lower"]))
expect_true(all(ints_saemix_mkin[[3]][, "est."] <
ints_nlme$varStruct[, "upper"]))
# nlmixr saem vs. nlme
expect_true(all(ints_nlmixr_saem[[3]][, "est."] >
ints_nlme$varStruct[, "lower"]))
expect_true(all(ints_nlmixr_saem[[3]][, "est."] <
ints_nlme$varStruct[, "upper"]))
# nlmixr focei vs. nlme
# We only test for the proportional part (rsd_high), as the
# constant part (sigma_low) obtained with nlmixr/FOCEI is below the lower
# bound of the confidence interval obtained with nlme
expect_true(ints_nlmixr_focei[[3]]["rsd_high", "est."] >
ints_nlme$varStruct["prop", "lower"])
expect_true(ints_nlmixr_focei[[3]]["rsd_high", "est."] <
ints_nlme$varStruct["prop", "upper"])
})
# Model definition from the 2020 paper https://doi.org/10.3390/environments8080071
# But note the data are different, as we did not pool the data from the same soils
# for the paper
# The model is called DFOP-SFO3+ in the paper
dfop_sfo3p <- mkinmod(
DMTA = mkinsub("DFOP", c("M23", "M27", "M31")),
M23 = mkinsub("SFO"),
M27 = mkinsub("SFO"),
M31 = mkinsub("SFO", "M27", sink = FALSE),
quiet = TRUE
)
dfop_sfo3 <- mkinmod(
DMTA = mkinsub("DFOP", c("M23", "M27", "M31")),
M23 = mkinsub("SFO"),
M27 = mkinsub("SFO"),
M31 = mkinsub("SFO"),
quiet = TRUE
)
# The following is work in progress
#dmta_dfop_sfo3p_tc <- mmkin(list("DFOP-SFO3+" = dfop_sfo3p),
# dmta_ds, error_model = "tc", quiet = TRUE)
# The Borstel dataset give false convergence
#dmta_dfop_sfo3_tc <- mmkin(list("DFOP-SFO3" = dfop_sfo3),
# dmta_ds, error_model = "tc", quiet = TRUE)
# We get convergence in all soils
#test_that("Different backends get consistent results for DFOP-SFO3+, dimethenamid data", {
# nlme needs some help to converge
# nlme_dfop_sfo3p_tc <- nlme(dmta_dfop_sfo3p_tc,
# control = list(pnlsMaxIter = 30, tolerance = 3e-3))
# ints_nlme_dfop_sfo3p_tc <- intervals(nlme_dfop_sfo3p_tc, which = "fixed")
# saemix does not succeed with these data, we get problems
# with the deSolve solutions, depending on the seed:
# Error in lsoda(y, times, func, parms, ...) :
# illegal input detected before taking any integration steps - see
# written message
# or:
# Error in out[available, var] : (subscript) logical subscript too long
#saem_saemix_dfop_sfo3p_tc <- saem(dmta_dfop_sfo3p_tc)
#})
