% 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{Estimation of parameter distributions from mmkin row objects}
\usage{
nlme_function(object)

mean_degparms(object)

nlme_data(object)
}
\arguments{
\item{object}{An mmkin row object containing several fits of the same model to different datasets}
}
\value{
A function that can be used with nlme

A named vector containing mean values of the fitted degradation model parameters

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.
}
\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_sink = 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_sink = 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_sink = 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 generated 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))

\dontrun{
  # 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")
  m_sfo_sfo_ff <- mkinmod(parent = mkinsub("SFO", "A1"),
    A1 = mkinsub("SFO"), use_of_ff = "max")
  m_fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"),
    A1 = mkinsub("SFO"))
  m_dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
    A1 = mkinsub("SFO"))
  m_sforb_sfo <- mkinmod(parent = mkinsub("SFORB", "A1"),
    A1 = mkinsub("SFO"))

  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,
   "SFORB-SFO" = m_sforb_sfo),
    ds_2)

  grouped_data_2 <- nlme_data(f_2["SFO-SFO", ])

  mean_dp_sfo_sfo <- mean_degparms(f_2["SFO-SFO", ])
  mean_dp_sfo_sfo_ff <- mean_degparms(f_2["SFO-SFO-ff", ])
  mean_dp_fomc_sfo <- mean_degparms(f_2["FOMC-SFO", ])
  mean_dp_dfop_sfo <- mean_degparms(f_2["DFOP-SFO", ])
  mean_dp_sforb_sfo <- mean_degparms(f_2["SFORB-SFO", ])

  nlme_f_sfo_sfo <- nlme_function(f_2["SFO-SFO", ])
  nlme_f_sfo_sfo_ff <- nlme_function(f_2["SFO-SFO-ff", ])
  nlme_f_fomc_sfo <- nlme_function(f_2["FOMC-SFO", ])

  # Allowing for correlations between random effects leads to non-convergence
  f_nlme_sfo_sfo <- nlme(value ~ nlme_f_sfo_sfo(name, time,
       parent_0, log_k_parent_sink, log_k_parent_A1, log_k_A1_sink),
     data = grouped_data_2,
     fixed = parent_0 + log_k_parent_sink + log_k_parent_A1 + log_k_A1_sink ~ 1,
     random = pdDiag(parent_0 + log_k_parent_sink + log_k_parent_A1 + log_k_A1_sink ~ 1),
     start = mean_dp_sfo_sfo)

  # The same model fitted with transformed formation fractions does not converge
  f_nlme_sfo_sfo_ff <- nlme(value ~ nlme_f_sfo_sfo_ff(name, time,
       parent_0, log_k_parent, log_k_A1, f_parent_ilr_1),
     data = grouped_data_2,
     fixed = parent_0 + log_k_parent + log_k_A1 + f_parent_ilr_1 ~ 1,
     random = pdDiag(parent_0 + log_k_parent + log_k_A1 + f_parent_ilr_1 ~ 1),
     start = mean_dp_sfo_sfo_ff)

  # It does converge with this version of reduced random effects
  f_nlme_sfo_sfo_ff <- nlme(value ~ nlme_f_sfo_sfo_ff(name, time,
       parent_0, log_k_parent, log_k_A1, f_parent_ilr_1),
     data = grouped_data_2,
     fixed = parent_0 + log_k_parent + log_k_A1 + f_parent_ilr_1 ~ 1,
     random = pdDiag(parent_0 + log_k_parent ~ 1),
     start = mean_dp_sfo_sfo_ff)

  f_nlme_fomc_sfo <- nlme(value ~ nlme_f_fomc_sfo(name, time,
       parent_0, log_alpha, log_beta, log_k_A1, f_parent_ilr_1),
     data = grouped_data_2,
     fixed = parent_0 + log_alpha + log_beta + log_k_A1 + f_parent_ilr_1 ~ 1,
     random = pdDiag(parent_0 + log_alpha + log_beta + log_k_A1 + f_parent_ilr_1 ~ 1),
     start = mean_dp_fomc_sfo)

  # DFOP-SFO and SFORB-SFO did not converge with full random effects

  anova(f_nlme_fomc_sfo, f_nlme_sfo_sfo)
}
}