Rudimentary support for setting up nlmixr models

- All degradation models are specified as ODE models. This appears to be
fast enough
- Error models are being translated to nlmixr as close to the mkin error
model as possible. When using the 'saem' backend, it appears not to be
possible to use the same error model for more than one observed variable
- No support yet for models with parallel formation of metabolites, where
the ilr transformation is used in mkin per default
- There is a bug in nlmixr which appears to be triggered if the data are
not balanced, see nlmixrdevelopment/nlmixr#530
- There is a print and a plot method, the summary method is not finished

1 files changed, 188 insertions, 0 deletions

diff --git a/man/nlmixr.mmkin.Rd b/man/nlmixr.mmkin.Rd
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+++ b/man/nlmixr.mmkin.Rd
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+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nlmixr.R
+\name{nlmixr.mmkin}
+\alias{nlmixr.mmkin}
+\alias{print.nlmixr.mmkin}
+\alias{nlmixr_model}
+\alias{nlmixr_data}
+\title{Fit nonlinear mixed models using nlmixr}
+\usage{
+\method{nlmixr}{mmkin}(
+  object,
+  data = NULL,
+  est = NULL,
+  control = list(),
+  table = tableControl(),
+  error_model = object[[1]]$err_mod,
+  test_log_parms = TRUE,
+  conf.level = 0.6,
+  ...,
+  save = NULL,
+  envir = parent.frame()
+)
+
+\method{print}{nlmixr.mmkin}(x, digits = max(3, getOption("digits") - 3), ...)
+
+nlmixr_model(
+  object,
+  est = c("saem", "focei"),
+  degparms_start = "auto",
+  test_log_parms = FALSE,
+  conf.level = 0.6,
+  error_model = object[[1]]$err_mod
+)
+
+nlmixr_data(object, ...)
+}
+\arguments{
+\item{object}{An \link{mmkin} row object containing several fits of the same
+\link{mkinmod} model to different datasets}
+
+\item{est}{Estimation method passed to \link[nlmixr:nlmixr]{nlmixr::nlmixr}}
+
+\item{control}{Passed to \link[nlmixr:nlmixr]{nlmixr::nlmixr}.}
+
+\item{test_log_parms}{If TRUE, an attempt is made to use more robust starting
+values for population parameters fitted as log parameters in mkin (like
+rate constants) by only considering rate constants that pass the t-test
+when calculating mean degradation parameters using \link{mean_degparms}.}
+
+\item{conf.level}{Possibility to adjust the required confidence level
+for parameter that are tested if requested by 'test_log_parms'.}
+
+\item{\dots}{Passed to \link{nlmixr_model}}
+
+\item{x}{An nlmixr.mmkin object to print}
+
+\item{digits}{Number of digits to use for printing}
+
+\item{degparms_start}{Parameter values given as a named numeric vector will
+be used to override the starting values obtained from the 'mmkin' object.}
+
+\item{solution_type}{Possibility to specify the solution type in case the
+automatic choice is not desired}
+}
+\value{
+An S3 object of class 'nlmixr.mmkin', containing the fitted
+\link[nlmixr:nlmixr]{nlmixr::nlmixr} object as a list component named 'nm'. The
+object also inherits from 'mixed.mmkin'.
+
+An function defining a model suitable for fitting with \link[nlmixr:nlmixr]{nlmixr::nlmixr}.
+
+An dataframe suitable for use with \link[nlmixr:nlmixr]{nlmixr::nlmixr}
+}
+\description{
+This function uses \code{\link[nlmixr:nlmixr]{nlmixr::nlmixr()}} as a backend for fitting nonlinear mixed
+effects models created from \link{mmkin} row objects using the Stochastic Approximation
+Expectation Maximisation algorithm (SAEM).
+}
+\details{
+An mmkin row object is essentially a list of mkinfit objects that have been
+obtained by fitting the same model to a list of datasets using \link{mkinfit}.
+}
+\examples{
+ds <- lapply(experimental_data_for_UBA_2019[6:10],
+ function(x) subset(x$data[c("name", "time", "value")]))
+names(ds) <- paste("Dataset", 6:10)
+f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP", "HS"), ds, quiet = TRUE, cores = 1)
+f_mmkin_parent_tc <- mmkin(c("SFO", "FOMC", "DFOP"), ds, error_model = "tc",
+  cores = 1, quiet = TRUE)
+
+f_nlmixr_sfo_saem <- nlmixr(f_mmkin_parent["SFO", ], est = "saem")
+f_nlmixr_sfo_focei <- nlmixr(f_mmkin_parent["SFO", ], est = "focei")
+
+f_nlmixr_fomc_saem <- nlmixr(f_mmkin_parent["FOMC", ], est = "saem")
+f_nlmixr_fomc_focei <- nlmixr(f_mmkin_parent["FOMC", ], est = "focei")
+
+f_nlmixr_dfop_saem <- nlmixr(f_mmkin_parent["DFOP", ], est = "saem")
+f_nlmixr_dfop_focei <- nlmixr(f_mmkin_parent["DFOP", ], est = "focei")
+
+f_nlmixr_hs_saem <- nlmixr(f_mmkin_parent["HS", ], est = "saem")
+f_nlmixr_hs_focei <- nlmixr(f_mmkin_parent["HS", ], est = "focei")
+
+f_nlmixr_fomc_saem_tc <- nlmixr(f_mmkin_parent_tc["FOMC", ], est = "saem")
+f_nlmixr_fomc_focei_tc <- nlmixr(f_mmkin_parent_tc["FOMC", ], est = "focei")
+
+AIC(
+  f_nlmixr_sfo_saem$nm, f_nlmixr_sfo_focei$nm,
+  f_nlmixr_fomc_saem$nm, f_nlmixr_fomc_focei$nm,
+  f_nlmixr_dfop_saem$nm, f_nlmixr_dfop_focei$nm,
+  f_nlmixr_hs_saem$nm, f_nlmixr_hs_focei$nm,
+  f_nlmixr_fomc_saem_tc$nm, f_nlmixr_fomc_focei_tc$nm)
+
+AIC(nlme(f_mmkin_parent["FOMC", ]))
+AIC(nlme(f_mmkin_parent["HS", ]))
+
+# nlme is comparable to nlmixr with focei, saem finds a better
+# solution, the two-component error model does not improve it
+plot(f_nlmixr_fomc_saem)
+
+\dontrun{
+sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"),
+  A1 = mkinsub("SFO"))
+fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"),
+  A1 = mkinsub("SFO"))
+dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
+  A1 = mkinsub("SFO"))
+
+f_mmkin_const <- mmkin(list(
+    "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo),
+  ds, quiet = TRUE, error_model = "const")
+f_mmkin_obs <- mmkin(list(
+    "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo),
+  ds, quiet = TRUE, error_model = "obs")
+f_mmkin_tc <- mmkin(list(
+    "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo),
+  ds, quiet = TRUE, error_model = "tc")
+
+# A single constant variance is currently only possible with est = 'focei' in nlmixr
+f_nlmixr_sfo_sfo_focei_const <- nlmixr(f_mmkin_const["SFO-SFO", ], est = "focei")
+f_nlmixr_fomc_sfo_focei_const <- nlmixr(f_mmkin_const["FOMC-SFO", ], est = "focei")
+f_nlmixr_dfop_sfo_focei_const <- nlmixr(f_mmkin_const["DFOP-SFO", ], est = "focei")
+
+# Variance by variable is supported by 'saem' and 'focei'
+f_nlmixr_fomc_sfo_saem_obs <- nlmixr(f_mmkin_obs["FOMC-SFO", ], est = "saem")
+f_nlmixr_fomc_sfo_focei_obs <- nlmixr(f_mmkin_obs["FOMC-SFO", ], est = "focei")
+f_nlmixr_dfop_sfo_saem_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "saem")
+f_nlmixr_dfop_sfo_focei_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "focei")
+
+# Identical two-component error for all variables is only possible with
+# est = 'focei' in nlmixr
+f_nlmixr_fomc_sfo_focei_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "focei")
+f_nlmixr_dfop_sfo_focei_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei")
+
+# Two-component error by variable is possible with both estimation methods
+# Variance by variable is supported by 'saem' and 'focei'
+f_nlmixr_fomc_sfo_saem_obs_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "saem",
+  error_model = "obs_tc")
+f_nlmixr_fomc_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "focei",
+  error_model = "obs_tc")
+f_nlmixr_dfop_sfo_saem_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "saem",
+  error_model = "obs_tc")
+f_nlmixr_dfop_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei",
+  error_model = "obs_tc")
+
+AIC(
+  f_nlmixr_sfo_sfo_focei_const$nm,
+  f_nlmixr_fomc_sfo_focei_const$nm,
+  f_nlmixr_dfop_sfo_focei_const$nm,
+  f_nlmixr_fomc_sfo_saem_obs$nm,
+  f_nlmixr_fomc_sfo_focei_obs$nm,
+  f_nlmixr_dfop_sfo_saem_obs$nm,
+  f_nlmixr_dfop_sfo_focei_obs$nm,
+  f_nlmixr_fomc_sfo_focei_tc$nm,
+  f_nlmixr_dfop_sfo_focei_tc$nm,
+  f_nlmixr_fomc_sfo_saem_obs_tc$nm,
+  f_nlmixr_fomc_sfo_focei_obs_tc$nm,
+  f_nlmixr_dfop_sfo_saem_obs_tc$nm,
+  f_nlmixr_dfop_sfo_focei_obs_tc$nm
+)
+# Currently, FOMC-SFO with two-component error by variable fitted by focei gives the
+# lowest AIC
+plot(f_nlmixr_fomc_sfo_focei_obs_tc)
+summary(f_nlmixr_fomc_sfo_focei_obs_tc)
+}
+}
+\seealso{
+\link{summary.nlmixr.mmkin} \link{plot.mixed.mmkin}
+}