path: root/man/nlme.mmkin.Rd

% 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.
}
\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", ])
f_nlme_dfop <- nlme(f["DFOP", ])
AIC(f_nlme_sfo, f_nlme_dfop)
print(f_nlme_dfop)
plot(f_nlme_dfop)
endpoints(f_nlme_dfop)

\dontrun{
  f_nlme_2 <- nlme(f["SFO", ], start = c(parent_0 = 100, log_k_parent = 0.1))
  update(f_nlme_2, random = parent_0 ~ 1)
  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
  f_nlme_sfo_sfo_ff <- nlme(f_2["SFO-SFO-ff", ])
  plot(f_nlme_sfo_sfo_ff)

  # For the following fit we need to increase pnlsMaxIter and the tolerance
  # to get convergence
  f_nlme_dfop_sfo <- nlme(f_2["DFOP-SFO", ],
    control = list(pnlsMaxIter = 120, tolerance = 5e-4), verbose = TRUE)

  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)
  # The same with DFOP-SFO does not converge, apparently the variances of
  # parent and A1 are too similar in this case, so that the model is
  # overparameterised
  #f_nlme_dfop_sfo_obs <- nlme(f_2_obs["DFOP-SFO", ], control = list(maxIter = 100))
}
}
\seealso{
\code{\link{nlme_function}}
}