% 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 = sys.frame(sys.parent()),
  fixed,
  random = fixed,
  groups,
  start,
  correlation = NULL,
  weights = NULL,
  subset,
  method = c("ML", "REML"),
  na.action = na.fail,
  naPattern,
  control = list(),
  verbose = FALSE
)

\method{print}{nlme.mmkin}(x, ...)

\method{update}{nlme.mmkin}(object, ...)
}
\arguments{
\item{model}{An \code{\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, all fixed effects are complemented
with uncorrelated random effects}

\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{...}{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
}
\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.
}
\examples{
ds <- lapply(experimental_data_for_UBA_2019[6:10],
 function(x) subset(x$data[c("name", "time", "value")], name == "parent"))
f <- mmkin("SFO", ds, quiet = TRUE, cores = 1)
library(nlme)
endpoints(f[[1]])
f_nlme <- nlme(f)
print(f_nlme)
endpoints(f_nlme)
\dontrun{
  f_nlme_2 <- nlme(f, start = c(parent_0 = 100, log_k_parent_sink = 0.1))
  update(f_nlme_2, random = parent_0 ~ 1)
  # Test on some real data
  ds_2 <- lapply(experimental_data_for_UBA_2019[6:10],
   function(x) x$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_fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"),
    A1 = mkinsub("SFO"), 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,
   "FOMC-SFO" = m_fomc_sfo,
   "DFOP-SFO" = m_dfop_sfo),
    ds_2, quiet = TRUE)
  plot(f_2["SFO-SFO", 3:4]) # Separate fits for datasets 3 and 4

  f_nlme_sfo_sfo <- nlme(f_2["SFO-SFO", ])
  # plot(f_nlme_sfo_sfo) # not feasible with pkgdown figures
  plot(f_nlme_sfo_sfo, 3:4) # Global mixed model: Fits for datasets 3 and 4

  # With formation fractions
  f_nlme_sfo_sfo_ff <- nlme(f_2["SFO-SFO-ff", ])
  plot(f_nlme_sfo_sfo_ff, 3:4) # chi2 different due to different df attribution

  # For more parameters, we need to increase pnlsMaxIter and the tolerance
  # to get convergence
  f_nlme_fomc_sfo <- nlme(f_2["FOMC-SFO", ],
    control = list(pnlsMaxIter = 100, tolerance = 1e-4), verbose = TRUE)
  f_nlme_dfop_sfo <- nlme(f_2["DFOP-SFO", ],
    control = list(pnlsMaxIter = 120, tolerance = 5e-4), verbose = TRUE)
  plot(f_2["FOMC-SFO", 3:4])
  plot(f_nlme_fomc_sfo, 3:4)

  plot(f_2["DFOP-SFO", 3:4])
  plot(f_nlme_dfop_sfo, 3:4)

  anova(f_nlme_dfop_sfo, f_nlme_fomc_sfo, f_nlme_sfo_sfo)
  anova(f_nlme_dfop_sfo, f_nlme_sfo_sfo) # if we ignore FOMC

  endpoints(f_nlme_sfo_sfo)
  endpoints(f_nlme_dfop_sfo)
}
}
\seealso{
\code{\link{nlme_function}}
}