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

1 files changed, 18 insertions, 70 deletions

diff --git a/R/nlme.R b/R/nlme.R
index fafaa7b6..ef93327c 100644
--- a/R/nlme.R
+++ b/R/nlme.R
@@ -8,6 +8,7 @@
 #' @param object An mmkin row object containing several fits of the same model to different datasets
 #' @import nlme
 #' @rdname nlme
+#' @seealso \code{\link{nlme.mmkin}}
 #' @examples
 #' sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
 #' m_SFO <- mkinmod(parent = mkinsub("SFO"))
@@ -47,73 +48,9 @@
 #'   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)
-#' }
 #' @return A function that can be used with nlme
 #' @export
 nlme_function <- function(object) {
@@ -185,14 +122,25 @@ nlme_function <- function(object) {
 }
 
 #' @rdname nlme
-#' @return A named vector containing mean values of the fitted degradation model parameters
+#' @return 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?
+#' @param random Should a list with fixed and random effects be returned?
 #' @export
-mean_degparms <- function(object) {
+mean_degparms <- function(object, random = FALSE) {
   if (nrow(object) > 1) stop("Only row objects allowed")
   degparm_mat_trans <- sapply(object, parms, transformed = TRUE)
   mean_degparm_names <- setdiff(rownames(degparm_mat_trans), names(object[[1]]$errparms))
-  res <- apply(degparm_mat_trans[mean_degparm_names, ], 1, mean)
-  return(res)
+  fixed <- apply(degparm_mat_trans[mean_degparm_names, ], 1, mean)
+  if (random) {
+    degparm_mat_trans[mean_degparm_names, ]
+    random <- t(apply(degparm_mat_trans[mean_degparm_names, ], 2, function(column) column - fixed))
+    rownames(random) <- as.character(1:nrow(random))
+    return(list(fixed = fixed, random = list(ds = random)))
+  } else {
+    return(fixed)
+  }
 }
 
 #' @rdname nlme