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% 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}
}
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