From b76e401a854021eaeda6f8ba262baf37b4ecfac2 Mon Sep 17 00:00:00 2001
From: Johannes Ranke <jranke@uni-bremen.de>
Date: Thu, 13 Oct 2022 03:51:22 +0200
Subject: Select best fit from multistart, use in parhist

- Add 'best' and 'which.best' generics with methods for multistart
objects
- Per default, scale the parameters in parhist plots using the fit with
the highest log likelihood.
---
 R/multistart.R | 40 +++++++++++++++++++++++++++++++++++++++-
 R/parhist.R    | 42 +++++++++++++++++++++++++++++-------------
 2 files changed, 68 insertions(+), 14 deletions(-)

(limited to 'R')

diff --git a/R/multistart.R b/R/multistart.R
index b65c0bee..a788953e 100644
--- a/R/multistart.R
+++ b/R/multistart.R
@@ -47,8 +47,10 @@
 #' f_saem_full <- saem(f_mmkin)
 #' f_saem_full_multi <- multistart(f_saem_full, n = 16, cores = 16)
 #' parhist(f_saem_full_multi, lpos = "bottomright")
+#' illparms(f_saem_full)
 #'
-#' f_saem_reduced <- update(f_saem_full, covariance.model = diag(c(1, 1, 0, 1)))
+#' f_saem_reduced <- update(f_saem_full, no_random_effect = "log_k2")
+#' illparms(f_saem_reduced)
 #' # On Windows, we need to create a cluster first. When working with
 #' # such a cluster, we need to export the mmkin object to the cluster
 #' # nodes, as it is referred to when updating the saem object on the nodes.
@@ -140,3 +142,39 @@ print.multistart <- function(x, ...) {
   cat("<multistart> object with", length(x), "fits:\n")
   print(convergence(x))
 }
+
+#' @rdname multistart
+#' @export
+best <- function(object, ...)
+{
+  UseMethod("best", object)
+}
+
+#' @export
+#' @return The object with the highest likelihood
+#' @rdname multistart
+best.default <- function(object, ...)
+{
+  return(object[[which.best(object)]])
+}
+
+#' @return The index of the object with the highest likelihood
+#' @rdname multistart
+#' @export
+which.best <- function(object, ...)
+{
+  UseMethod("which.best", object)
+}
+
+#' @rdname multistart
+#' @export
+which.best.default <- function(object, ...)
+{
+  llfunc <- function(object) {
+    ret <- try(logLik(object))
+    if (inherits(ret, "try-error")) return(NA)
+    else return(ret)
+  }
+  ll <- sapply(object, llfunc)
+  return(which.max(ll))
+}
diff --git a/R/parhist.R b/R/parhist.R
index 10730873..5d498664 100644
--- a/R/parhist.R
+++ b/R/parhist.R
@@ -1,12 +1,15 @@
 #' Plot parameter distributions from multistart objects
 #'
-#' Produces a boxplot with all parameters from the multiple runs, divided by
-#' using their medians as in the paper by Duchesne et al. (2021).
+#' Produces a boxplot with all parameters from the multiple runs, scaled
+#' either by the parameters of the run with the highest likelihood,
+#' or by their medians as proposed in the paper by Duchesne et al. (2021).
 #'
 #' @param object The [multistart] object
-#' @param \dots Passed to [boxplot]
+#' @param scale By default, scale parameters using the best available fit.
+#' If 'median', parameters are scaled using the median parameters from all fits.
 #' @param main Title of the plot
 #' @param lpos Positioning of the legend.
+#' @param \dots Passed to [boxplot]
 #' @references Duchesne R, Guillemin A, Gandrillon O, Crauste F. Practical
 #' identifiability in the frame of nonlinear mixed effects models: the example
 #' of the in vitro erythropoiesis. BMC Bioinformatics. 2021 Oct 4;22(1):478.
@@ -14,7 +17,9 @@
 #' @seealso [multistart]
 #' @importFrom stats median
 #' @export
-parhist <- function(object, lpos = "bottomleft", main = "", ...) {
+parhist <- function(object, scale = c("best", "median"),
+  lpos = "bottomleft", main = "", ...)
+{
   oldpar <- par(no.readonly = TRUE)
   on.exit(par(oldpar, no.readonly = TRUE))
 
@@ -48,23 +53,34 @@ parhist <- function(object, lpos = "bottomleft", main = "", ...) {
     colnames(all_parms)[1:length(degparm_names)] <- degparm_names
   }
 
-  median_parms <- apply(all_parms, 2, median)
-  start_scaled_parms <- rep(NA_real_, length(orig_parms))
-  names(start_scaled_parms) <- names(orig_parms)
+  scale <- match.arg(scale)
+  parm_scale <- switch(scale,
+    best = all_parms[which.best(object), ],
+    median = apply(all_parms, 2, median)
+  )
 
-  orig_scaled_parms <- orig_parms / median_parms
-  all_scaled_parms <- t(apply(all_parms, 1, function(x) x / median_parms))
-  start_scaled_parms[names(start_parms)] <-
-    start_parms / median_parms[names(start_parms)]
+  # Boxplots of all scaled parameters
+  all_scaled_parms <- t(apply(all_parms, 1, function(x) x / parm_scale))
   boxplot(all_scaled_parms, log = "y", main = main, ,
     ylab = "Normalised parameters", ...)
 
-  points(orig_scaled_parms, col = 2, cex = 2)
+  # Show starting parameters
+  start_scaled_parms <- rep(NA_real_, length(orig_parms))
+  names(start_scaled_parms) <- names(orig_parms)
+  start_scaled_parms[names(start_parms)] <-
+    start_parms / parm_scale[names(start_parms)]
   points(start_scaled_parms, col = 3, cex = 3)
+
+  # Show parameters of original run
+  orig_scaled_parms <- orig_parms / parm_scale
+  points(orig_scaled_parms, col = 2, cex = 2)
+
+  abline(h = 1, lty = 2)
+
   legend(lpos, inset = c(0.05, 0.05), bty = "n",
     pch = 1, col = 3:1, lty = c(NA, NA, 1),
     legend = c(
       "Starting parameters",
-      "Converged parameters",
+      "Original run",
       "Multistart runs"))
 }
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