Dear Diederik,
The lack of convergence is because the residual variance is
non-identifiable with binary data but you have a very weak prior on
it. You should fix the residual variance at something (I usually use 1):
prior.test<-list(R=list(V=1,fix=1), G=list(G1=list(V=1, nu=0.002),G2 =
Dear all,
A while ago, I was kindly advised to try MCMCglmm to investigate
invasion success of non-native species while accounting for phylogenetic
relatedness. I have managed to run some explanatory models but stumble
upon converge problems…
The data are (1) /introduction events/ of
Hi all,
thank you for your suggestions! I will delve into the suggested packages
and may come back to you when the learning curve becomes too steep :-).
thanks again,
Diederik
On 6/3/2015 5:44 PM, Peter Smits wrote:
The alternative to MCMCglmm would be to use stan or bugs for writing
your
MCMCglmm can definitely handle all of that. Post back here and/or at
the R-sig-mixed-models list for help with priors and others stuff when
you've got some code developed.
Diederik Strubbe wrote:
Dear all,
I am struggling with analysing a dataset aimed at explaining invasion
success of
Hi Diederik,
you can use MCMCglmm. The package allows for inclusion of phylogenetic
information, random effects and zero-inflated response variables. However, it
may take some time to get familiar with the package.
Best,
J
—
Jörg Albrecht, PhD
Postdoctoral researcher
Institute of Nature
The alternative to MCMCglmm would be to use stan or bugs for writing your
own sampling statement + priors. You'll have more control than with
MCMCglmm, but it will have even more of a learning curve.
Using stan will also most likely be faster than using any single R package.
Cheers,
Peter
On
Dear all,
I am struggling with analysing a dataset aimed at explaining invasion
success of non-native species. At a country level, I need to relate
invasion success (binomial: 0 for failed invasions, 1 for success) to
socio-economic variables, taking into account
- Phylogenetic
