% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/nlme.mmkin.R
\name{nlme.mmkin}
\alias{nlme.mmkin}
\alias{print.nlme.mmkin}
\alias{update.nlme.mmkin}
\title{Create an nlme model for an mmkin row object}
\usage{
\method{nlme}{mmkin}(
  model,
  data = "auto",
  fixed = lapply(as.list(names(mean_degparms(model))), function(el) eval(parse(text =
    paste(el, 1, sep = "~")))),
  random = pdDiag(fixed),
  groups,
  start = mean_degparms(model, random = TRUE),
  correlation = NULL,
  weights = NULL,
  subset,
  method = c("ML", "REML"),
  na.action = na.fail,
  naPattern,
  control = list(),
  verbose = FALSE
)

\method{print}{nlme.mmkin}(x, digits = max(3, getOption("digits") - 3), ...)

\method{update}{nlme.mmkin}(object, ...)
}
\arguments{
\item{model}{An \link{mmkin} row object.}

\item{data}{Ignored, data are taken from the mmkin model}

\item{fixed}{Ignored, all degradation parameters fitted in the
mmkin model are used as fixed parameters}

\item{random}{If not specified, correlated random effects are set up
for all optimised degradation model parameters using the log-Cholesky
parameterization \link[nlme:pdLogChol]{nlme::pdLogChol} that is also the default of
the generic \link{nlme} method.}

\item{groups}{See the documentation of nlme}

\item{start}{If not specified, mean values of the fitted degradation
parameters taken from the mmkin object are used}

\item{correlation}{See the documentation of nlme}

\item{weights}{passed to nlme}

\item{subset}{passed to nlme}

\item{method}{passed to nlme}

\item{na.action}{passed to nlme}

\item{naPattern}{passed to nlme}

\item{control}{passed to nlme}

\item{verbose}{passed to nlme}

\item{x}{An nlme.mmkin object to print}

\item{digits}{Number of digits to use for printing}

\item{...}{Update specifications passed to update.nlme}

\item{object}{An nlme.mmkin object to update}
}
\value{
Upon success, a fitted 'nlme.mmkin' object, which is an nlme object
with additional elements. It also inherits from 'mixed.mmkin'.
}
\description{
This functions sets up a nonlinear mixed effects model for an mmkin row
object. 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.
}
\note{
As the object inherits from \link[nlme:nlme]{nlme::nlme}, there is a wealth of
methods that will automatically work on 'nlme.mmkin' objects, such as
\code{\link[nlme:intervals]{nlme::intervals()}}, \code{\link[nlme:anova.lme]{nlme::anova.lme()}} and \code{\link[nlme:coef.lme]{nlme::coef.lme()}}.
}
\examples{
ds <- lapply(experimental_data_for_UBA_2019[6:10],
 function(x) subset(x$data[c("name", "time", "value")], name == "parent"))
f <- mmkin(c("SFO", "DFOP"), ds, quiet = TRUE, cores = 1)
library(nlme)
f_nlme_sfo <- nlme(f["SFO", ])

\dontrun{

  f_nlme_dfop <- nlme(f["DFOP", ])
  anova(f_nlme_sfo, f_nlme_dfop)
  print(f_nlme_dfop)
  plot(f_nlme_dfop)
  endpoints(f_nlme_dfop)

  ds_2 <- lapply(experimental_data_for_UBA_2019[6:10],
   function(x) x$data[c("name", "time", "value")])
  m_sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"),
    A1 = mkinsub("SFO"), use_of_ff = "min", quiet = TRUE)
  m_sfo_sfo_ff <- mkinmod(parent = mkinsub("SFO", "A1"),
    A1 = mkinsub("SFO"), use_of_ff = "max", quiet = TRUE)
  m_dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
    A1 = mkinsub("SFO"), quiet = TRUE)

  f_2 <- mmkin(list("SFO-SFO" = m_sfo_sfo,
   "SFO-SFO-ff" = m_sfo_sfo_ff,
   "DFOP-SFO" = m_dfop_sfo),
    ds_2, quiet = TRUE)

  f_nlme_sfo_sfo <- nlme(f_2["SFO-SFO", ])
  plot(f_nlme_sfo_sfo)

  # With formation fractions this does not coverge with defaults
  # f_nlme_sfo_sfo_ff <- nlme(f_2["SFO-SFO-ff", ])
  #plot(f_nlme_sfo_sfo_ff)

  # With the log-Cholesky parameterization, this converges in 11
  # iterations and around 100 seconds, but without tweaking control
  # parameters (with pdDiag, increasing the tolerance and pnlsMaxIter was
  # necessary)
  f_nlme_dfop_sfo <- nlme(f_2["DFOP-SFO", ])

  plot(f_nlme_dfop_sfo)

  anova(f_nlme_dfop_sfo, f_nlme_sfo_sfo)

  endpoints(f_nlme_sfo_sfo)
  endpoints(f_nlme_dfop_sfo)

  if (length(findFunction("varConstProp")) > 0) { # tc error model for nlme available
    # Attempts to fit metabolite kinetics with the tc error model are possible,
    # but need tweeking of control values and sometimes do not converge

    f_tc <- mmkin(c("SFO", "DFOP"), ds, quiet = TRUE, error_model = "tc")
    f_nlme_sfo_tc <- nlme(f_tc["SFO", ])
    f_nlme_dfop_tc <- nlme(f_tc["DFOP", ])
    AIC(f_nlme_sfo, f_nlme_sfo_tc, f_nlme_dfop, f_nlme_dfop_tc)
    print(f_nlme_dfop_tc)
  }

  f_2_obs <- mmkin(list("SFO-SFO" = m_sfo_sfo,
   "DFOP-SFO" = m_dfop_sfo),
    ds_2, quiet = TRUE, error_model = "obs")
  f_nlme_sfo_sfo_obs <- nlme(f_2_obs["SFO-SFO", ])
  print(f_nlme_sfo_sfo_obs)
  f_nlme_dfop_sfo_obs <- nlme(f_2_obs["DFOP-SFO", ])

  f_2_tc <- mmkin(list("SFO-SFO" = m_sfo_sfo,
   "DFOP-SFO" = m_dfop_sfo),
    ds_2, quiet = TRUE, error_model = "tc")
  # f_nlme_sfo_sfo_tc <- nlme(f_2_tc["SFO-SFO", ]) # stops with error message
  f_nlme_dfop_sfo_tc <- nlme(f_2_tc["DFOP-SFO", ])
  # We get warnings about false convergence in the LME step in several iterations
  # but as the last such warning occurs in iteration 25 and we have 28 iterations
  # we can ignore these
  anova(f_nlme_dfop_sfo, f_nlme_dfop_sfo_obs, f_nlme_dfop_sfo_tc)

}
}
\seealso{
\code{\link[=nlme_function]{nlme_function()}}, \link{plot.mixed.mmkin}, \link{summary.nlme.mmkin}
}