% 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$data[c("name", "time", "value")], name == "parent"))
f <- mmkin(c("SFO", "DFOP"), ds, quiet = TRUE, cores = 1)
library(nlme)
f_nlme_sfo <- nlme(f["SFO", ])
f_nlme_dfop <- nlme(f["DFOP", ])
AIC(f_nlme_sfo, f_nlme_dfop)
print(f_nlme_dfop)
endpoints(f_nlme_dfop)
\dontrun{
f_nlme_2 <- nlme(f["SFO", ], start = c(parent_0 = 100, log_k_parent = 0.1))
update(f_nlme_2, random = parent_0 ~ 1)
ds_2 <- lapply(experimental_data_for_UBA_2019[6:10],
function(x) x$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_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)
if (findFunction("varConstProp")) { # tc error model for nlme available
# Attempts to fit metabolite kinetics with the tc error model
#f_2_tc <- 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,
# error_model = "tc")
#f_nlme_sfo_sfo_tc <- nlme(f_2_tc["SFO-SFO", ], control = list(maxIter = 100))
#f_nlme_dfop_sfo_tc <- nlme(f_2_tc["DFOP-SFO", ])
#f_nlme_dfop_sfo_tc <- update(f_nlme_dfop_sfo, weights = varConstProp(),
# control = list(sigma = 1, msMaxIter = 100, pnlsMaxIter = 15))
# Fitting metabolite kinetics with nlme.mmkin and the two-component
# error model currently does not work, at least not with these data.
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", ])
# 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}}
}
