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authorJohannes Ranke <jranke@uni-bremen.de>2014-10-15 01:24:40 +0200
committerJohannes Ranke <jranke@uni-bremen.de>2014-10-15 01:24:40 +0200
commit1c1106777a98119cf29c05c3f6e0de7077eb19b5 (patch)
tree67252068119f8b2aca8a3f8bb3882ddf15cb36e2
parent63e0ce4866e63cc6a844bfcb7b98c98619dd2831 (diff)
Switch to using the Port algorithm per default
Actually this only changes the order of entries in the dropbox, the default is set in mkin::mkinfit().
-rw-r--r--inst/GUI/gmkin.R2
-rw-r--r--vignettes/gmkin_manual.Rmd9
2 files changed, 6 insertions, 5 deletions
diff --git a/inst/GUI/gmkin.R b/inst/GUI/gmkin.R
index 9ffe1c7..50cd4ed 100644
--- a/inst/GUI/gmkin.R
+++ b/inst/GUI/gmkin.R
@@ -906,7 +906,7 @@ f.gg.opts.reweight.tol <- gedit(1e-8, label = "reweight.tol",
width = 20, cont = f.gg.opts)
f.gg.opts.reweight.max.iter <- gedit(10, label = "reweight.max.iter",
width = 20, cont = f.gg.opts)
-optimisation_methods <- c("Marq", "Port", "SANN")
+optimisation_methods <- c("Port", "Marq", "SANN")
f.gg.opts.method.modFit <- gcombobox(optimisation_methods, selected = 1,
label = "method.modFit",
width = 200,
diff --git a/vignettes/gmkin_manual.Rmd b/vignettes/gmkin_manual.Rmd
index 39669e8..af8a3b8 100644
--- a/vignettes/gmkin_manual.Rmd
+++ b/vignettes/gmkin_manual.Rmd
@@ -329,10 +329,11 @@ refer to the `mkinfit` [documentation](http://kinfit.r-forge.r-project.org/mkin_
for more details.
The drop down box "method.modFit" makes it possible to choose between the optimisation
-algorithms "Marq" (the default Levenberg-Marquardt implementation from the R package
-`minpack.lm`), "Port" (an alternative, also local optimisation algorithm) and
-"SANN" (the simulated annealing method - robust but inefficient and without a
-convergence criterion).
+algorithms "Port" (the default in mkin versions > 0.9-33, a local optimisation
+algorithm using a model/trust region approach), "Marq" (the former default in
+mkin, a Levenberg-Marquardt variant from the R package `minpack.lm`),
+and "SANN" (the simulated annealing method - robust but inefficient and without
+a convergence criterion).
Finally, the maximum number of iterations for the optimisation can be adapted using the
"maxit.modFit" field.

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