diff options
| author | Johannes Ranke <jranke@uni-bremen.de> | 2022-09-19 12:55:00 +0200 |
|---|---|---|
| committer | Johannes Ranke <jranke@uni-bremen.de> | 2022-09-19 12:55:00 +0200 |
| commit | 7a3f2ee22419608a8a634fd4d71d7176303b2f41 (patch) | |
| tree | 292447a0f0f647253ac269074ba7b6ce4139c576 /vignettes/web_only/multistart.rmd | |
| parent | 062a1be18677a4e83a64d23622b5214258d9bc7d (diff) | |
Improve parhist and llhist
In particular, adapt the display of parameter boxplots
for saem fits using mkin transformations to the way
used for saem fits using saemix transformations, i.e.
always show parameters on the natural scale, and normalised them by
dividing by the median from the multiple runs.
Diffstat (limited to 'vignettes/web_only/multistart.rmd')
| -rw-r--r-- | vignettes/web_only/multistart.rmd | 6 |
1 files changed, 3 insertions, 3 deletions
diff --git a/vignettes/web_only/multistart.rmd b/vignettes/web_only/multistart.rmd index a25b73ce..8f349973 100644 --- a/vignettes/web_only/multistart.rmd +++ b/vignettes/web_only/multistart.rmd @@ -1,7 +1,7 @@ --- title: Short demo of the multistart method author: Johannes Ranke -date: Last change 16 September 2022 (rebuilt `r Sys.Date()`) +date: Last change 19 September 2022 (rebuilt `r Sys.Date()`) output: html_document vignette: > @@ -34,7 +34,7 @@ random effects for all degradation parameters. f_mmkin <- mmkin("DFOP", dmta_ds, error_model = "tc", cores = 7, quiet = TRUE) 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") +parhist(f_saem_full_multi) ``` We see that not all variability parameters are identifiable, most problematic @@ -44,7 +44,7 @@ removing the intersoil variability for this parameter. ```{r} f_saem_reduced <- update(f_saem_full, covariance.model = diag(c(1, 1, 0, 1))) f_saem_reduced_multi <- multistart(f_saem_reduced, n = 16, cores = 16) -parhist(f_saem_reduced_multi, lpos = "bottomright") +parhist(f_saem_reduced_multi, lpos = "topright") ``` We can also analyse the log-likelihoods obtained in the multiple runs: |