% 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]]$err_mod,
test_log_parms = TRUE,
conf.level = 0.6,
degparms_start = "auto",
eta_start = "auto",
...,
save = NULL,
envir = parent.frame()
)
\method{print}{nlmixr.mmkin}(x, digits = max(3, getOption("digits") - 3), ...)
nlmixr_model(
object,
est = c("saem", "focei"),
degparms_start = "auto",
eta_start = "auto",
test_log_parms = TRUE,
conf.level = 0.6,
error_model = object[[1]]$err_mod,
add_attributes = FALSE
)
nlmixr_data(object, ...)
}
\arguments{
\item{object}{An \link{mmkin} row object containing several fits of the same
\link{mkinmod} model to different datasets}
\item{data}{Not used, as the data are extracted from the mmkin row object}
\item{est}{Estimation method passed to \link[nlmixr:nlmixr]{nlmixr::nlmixr}}
\item{control}{Passed to \link[nlmixr:nlmixr]{nlmixr::nlmixr}}
\item{table}{Passed to \link[nlmixr:nlmixr]{nlmixr::nlmixr}}
\item{error_model}{Optional argument to override the error model which is
being set based on the error model used in the mmkin row object.}
\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{degparms_start}{Parameter values given as a named numeric vector will
be used to override the starting values obtained from the 'mmkin' object.}
\item{eta_start}{Standard deviations on the transformed scale given as a
named numeric vector will be used to override the starting values obtained
from the 'mmkin' object.}
\item{\dots}{Passed to \link{nlmixr_model}}
\item{save}{Passed to \link[nlmixr:nlmixr]{nlmixr::nlmixr}}
\item{envir}{Passed to \link[nlmixr:nlmixr]{nlmixr::nlmixr}}
\item{x}{An nlmixr.mmkin object to print}
\item{digits}{Number of digits to use for printing}
\item{add_attributes}{Should the starting values used for degradation model
parameters and their distribution and for the error model parameters
be returned as attributes?}
}
\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) or First Order Conditional
Estimation with Interaction (FOCEI).
}
\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{
\dontrun{
ds <- lapply(experimental_data_for_UBA_2019[6:10],
function(x) subset(x$data[c("name", "time", "value")]))
names(ds) <- paste("Dataset", 6:10)
f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP", "HS"), ds, quiet = TRUE, cores = 1)
f_mmkin_parent_tc <- mmkin(c("SFO", "FOMC", "DFOP"), ds, error_model = "tc",
cores = 1, quiet = TRUE)
library(nlmixr)
f_nlmixr_sfo_saem <- nlmixr(f_mmkin_parent["SFO", ], est = "saem",
control = saemControl(print = 0))
f_nlmixr_sfo_focei <- nlmixr(f_mmkin_parent["SFO", ], est = "focei",
control = foceiControl(print = 0))
f_nlmixr_fomc_saem <- nlmixr(f_mmkin_parent["FOMC", ], est = "saem",
control = saemControl(print = 0))
f_nlmixr_fomc_focei <- nlmixr(f_mmkin_parent["FOMC", ], est = "focei",
control = foceiControl(print = 0))
f_nlmixr_dfop_saem <- nlmixr(f_mmkin_parent["DFOP", ], est = "saem",
control = saemControl(print = 0))
f_nlmixr_dfop_focei <- nlmixr(f_mmkin_parent["DFOP", ], est = "focei",
control = foceiControl(print = 0))
f_nlmixr_hs_saem <- nlmixr(f_mmkin_parent["HS", ], est = "saem",
control = saemControl(print = 0))
f_nlmixr_hs_focei <- nlmixr(f_mmkin_parent["HS", ], est = "focei",
control = foceiControl(print = 0))
f_nlmixr_fomc_saem_tc <- nlmixr(f_mmkin_parent_tc["FOMC", ], est = "saem",
control = saemControl(print = 0))
f_nlmixr_fomc_focei_tc <- nlmixr(f_mmkin_parent_tc["FOMC", ], est = "focei",
control = foceiControl(print = 0))
AIC(
f_nlmixr_sfo_saem$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", ]))
# The FOCEI fit of FOMC with constant variance or the tc error model is best
plot(f_nlmixr_fomc_saem_tc)
sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"),
A1 = mkinsub("SFO"), quiet = TRUE)
fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"),
A1 = mkinsub("SFO"), quiet = TRUE)
dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
A1 = mkinsub("SFO"), quiet = TRUE)
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")
nlmixr_model(f_mmkin_const["SFO-SFO", ])
# 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}
}
