% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/nlme.R
\name{nlme_function}
\alias{nlme_function}
\alias{mean_degparms}
\alias{nlme_data}
\title{Helper functions to create nlme models from mmkin row objects}
\usage{
nlme_function(object)

mean_degparms(object, random = FALSE, test_log_parms = FALSE, conf.level = 0.6)

nlme_data(object)
}
\arguments{
\item{object}{An mmkin row object containing several fits of the same model to different datasets}

\item{random}{Should a list with fixed and random effects be returned?}

\item{test_log_parms}{If TRUE, log parameters are only considered in
the mean calculations if their untransformed counterparts (most likely
rate constants) pass the t-test for significant difference from zero.}

\item{conf.level}{Possibility to adjust the required confidence level
for parameter that are tested if requested by 'test_log_parms'.}
}
\value{
A function that can be used with nlme

If random is FALSE (default), a named vector containing mean values
of the fitted degradation model parameters. If random is TRUE, a list with
fixed and random effects, in the format required by the start argument of
nlme for the case of a single grouping variable ds.

A \code{\link{groupedData}} object
}
\description{
These functions facilitate setting 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. They are used internally by the \code{\link[=nlme.mmkin]{nlme.mmkin()}} method.
}
\examples{
sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
m_SFO <- mkinmod(parent = mkinsub("SFO"))
d_SFO_1 <- mkinpredict(m_SFO,
  c(k_parent = 0.1),
  c(parent = 98), sampling_times)
d_SFO_1_long <- mkin_wide_to_long(d_SFO_1, time = "time")
d_SFO_2 <- mkinpredict(m_SFO,
  c(k_parent = 0.05),
  c(parent = 102), sampling_times)
d_SFO_2_long <- mkin_wide_to_long(d_SFO_2, time = "time")
d_SFO_3 <- mkinpredict(m_SFO,
  c(k_parent = 0.02),
  c(parent = 103), sampling_times)
d_SFO_3_long <- mkin_wide_to_long(d_SFO_3, time = "time")

d1 <- add_err(d_SFO_1, function(value) 3, n = 1)
d2 <- add_err(d_SFO_2, function(value) 2, n = 1)
d3 <- add_err(d_SFO_3, function(value) 4, n = 1)
ds <- c(d1 = d1, d2 = d2, d3 = d3)

f <- mmkin("SFO", ds, cores = 1, quiet = TRUE)
mean_dp <- mean_degparms(f)
grouped_data <- nlme_data(f)
nlme_f <- nlme_function(f)
# These assignments are necessary for these objects to be
# visible to nlme and augPred when evaluation is done by
# pkgdown to generate the html docs.
assign("nlme_f", nlme_f, globalenv())
assign("grouped_data", grouped_data, globalenv())

library(nlme)
m_nlme <- nlme(value ~ nlme_f(name, time, parent_0, log_k_parent_sink),
  data = grouped_data,
  fixed = parent_0 + log_k_parent_sink ~ 1,
  random = pdDiag(parent_0 + log_k_parent_sink ~ 1),
  start = mean_dp)
summary(m_nlme)
plot(augPred(m_nlme, level = 0:1), layout = c(3, 1))
# augPred does not work on fits with more than one state
# variable
#
# The procedure is greatly simplified by the nlme.mmkin function
f_nlme <- nlme(f)
plot(f_nlme)
}
\seealso{
\code{\link{nlme.mmkin}}
}