Include random effects in starting parameters

- mean_degparms() now optionally returns starting values for fixed and
  random effects, which makes it possible to obtain acceptable fits
  also in more difficult cases (with more parameters)
- Fix the anova method, as it is currently not enough to inherit from
  lme: https://bugs.r-project.org/bugzilla/show_bug.cgi?id=17761
- Show fit information, and per default also errmin information
  in plot.nlme.mmkin()
- Examples for nlme.mmkin: Decrease tolerance and increase the number of
  iterations in the PNLS step in order to be able to fit FOMC-SFO and
  DFOP-SFO

4 files changed, 86 insertions, 73 deletions

diff --git a/man/nlme.Rd b/man/nlme.Rd
index f31c7a4f..4a668ac0 100644
--- a/man/nlme.Rd
+++ b/man/nlme.Rd
@@ -8,17 +8,22 @@
 \usage{
 nlme_function(object)
 
-mean_degparms(object)
+mean_degparms(object, random = FALSE)
 
 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?}
 }
 \value{
 A function that can be used with nlme
 
-A named vector containing mean values of the fitted degradation model parameters
+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
 }
@@ -67,71 +72,10 @@ m_nlme <- nlme(value ~ nlme_f(name, time, parent_0, log_k_parent_sink),
   start = mean_dp)
 summary(m_nlme)
 plot(augPred(m_nlme, level = 0:1), layout = c(3, 1))
+# augPred does not seem to work on fits with more than one state
+# variable
 
-\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", ])
-  assign("nlme_f_sfo_sfo", nlme_f_sfo_sfo, globalenv())
-  assign("nlme_f_sfo_sfo_ff", nlme_f_sfo_sfo_ff, globalenv())
-  assign("nlme_f_fomc_sfo", nlme_f_fomc_sfo, globalenv())
-
-  # Allowing for correlations between random effects (not shown)
-  # 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)
-   # augPred does not see to work on this object, so no plot is shown
-
-  # 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)
-
-  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)
 }
+\seealso{
+\code{\link{nlme.mmkin}}
 }
diff --git a/man/nlme.mmkin.Rd b/man/nlme.mmkin.Rd
index 1fecb5dd..26dcce66 100644
--- a/man/nlme.mmkin.Rd
+++ b/man/nlme.mmkin.Rd
@@ -2,6 +2,8 @@
 % 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}(
@@ -20,11 +22,15 @@
   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{data}{Should the data be printed?}
 
 \item{fixed}{Ignored, all degradation parameters fitted in the
 mmkin model are used as fixed parameters}
@@ -52,6 +58,12 @@ parameters taken from the mmkin object are used}
 \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
@@ -68,9 +80,52 @@ ds <- lapply(experimental_data_for_UBA_2019[6:10],
 f <- mmkin("SFO", ds, quiet = TRUE, cores = 1)
 library(nlme)
 f_nlme <- nlme(f)
-nlme(f, random = parent_0 ~ 1)
-f_nlme <- nlme(f, start = c(parent_0 = 100, log_k_parent_sink = 0.1))
-update(f_nlme, random = parent_0 ~ 1)
+print(f_nlme)
+f_nlme_2 <- nlme(f, start = c(parent_0 = 100, log_k_parent_sink = 0.1))
+update(f_nlme_2, random = parent_0 ~ 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", 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
+}
 }
 \seealso{
 \code{\link{nlme_function}}
diff --git a/man/plot.mmkin.Rd b/man/plot.mmkin.Rd
index 982e8db6..600881ea 100644
--- a/man/plot.mmkin.Rd
+++ b/man/plot.mmkin.Rd
@@ -33,7 +33,7 @@ column.}
 values, with the error model, using \code{\link{mkinerrplot}}.}
 
 \item{standardized}{Should the residuals be standardized? This option
-is passed to \code{\link{mkinresplot}}, it only takes effect if 
+is passed to \code{\link{mkinresplot}}, it only takes effect if
 `resplot = "time"`.}
 
 \item{show_errmin}{Should the chi2 error level be shown on top of the plots
@@ -76,6 +76,7 @@ latex is being used for the formatting of the chi2 error level.
                 cores = 1, quiet = TRUE, error_model = "tc")
   plot(fits[, "FOCUS C"])
   plot(fits["FOMC", ])
+  plot(fits["FOMC", ], show_errmin = FALSE)
 
   # We can also plot a single fit, if we like the way plot.mmkin works, but then the plot
   # height should be smaller than the plot width (this is not possible for the html pages
diff --git a/man/plot.nlme.mmkin.Rd b/man/plot.nlme.mmkin.Rd
index c0e749aa..9bea7013 100644
--- a/man/plot.nlme.mmkin.Rd
+++ b/man/plot.nlme.mmkin.Rd
@@ -11,6 +11,9 @@
   legends = 1,
   resplot = c("time", "errmod"),
   standardized = FALSE,
+  show_errmin = TRUE,
+  errmin_var = "All data",
+  errmin_digits = 3,
   cex = 0.7,
   rel.height.middle = 0.9,
   ymax = "auto",
@@ -35,6 +38,15 @@ values, with the error model, using \code{\link{mkinerrplot}}.}
 is passed to \code{\link{mkinresplot}}, it only takes effect if
 `resplot = "time"`.}
 
+\item{show_errmin}{Should the chi2 error level be shown on top of the plots
+to the left?}
+
+\item{errmin_var}{The variable for which the FOCUS chi2 error value should
+be shown.}
+
+\item{errmin_digits}{The number of significant digits for rounding the FOCUS
+chi2 error percentage.}
+
 \item{cex}{Passed to the plot functions and \code{\link{mtext}}.}
 
 \item{rel.height.middle}{The relative height of the middle plot, if more
@@ -56,11 +68,12 @@ ds <- lapply(experimental_data_for_UBA_2019[6:10],
  function(x) subset(x$data[c("name", "time", "value")], name == "parent"))
 f <- mmkin("SFO", ds, quiet = TRUE, cores = 1)
 #plot(f) # too many panels for pkgdown
+plot(f[, 3:4])
 library(nlme)
 f_nlme <- nlme(f)
 
 #plot(f_nlme) # too many panels for pkgdown
-plot(f_nlme, 1:2)
+plot(f_nlme, 3:4)
 }
 \author{
 Johannes Ranke