[R-sig-ME] mixed mutlinomial regression for count data with, overdisperion & zero-inflation

[Stéphanie Périquet]

Hi Dave,

Thanks for all this detailed info. I'm on Mac unfortunately. But as the
model with nbinom2 runs and is a better fit, I'll inspect the rep file fo
this one. Or is there a way to access this directly on R?

Best,
Stephanie

On 20 May 2016 at 05:05, dave fournier <davef at otter-rsch.com> wrote:

> I found a version of glmmadmb for Windows 64bit ( I sadly assume) that
> almost does the job out of the
> box. Using the information from R Forge
>
>    http://glmmadmb.r-forge.r-project.org/
>
> which points to
>
>       http://www.admb-project.org/buildbot/glmmadmb/
>
> where I found this exe
>
>             glmmadmb-mingw64-r3274-windows10-mingw64.exe
>
>  Using your glmmadmb.pin and glmmadmb.dat files (which you renamed to
> glmmadmb1.pin and glmmadmb1.dat) I ran the following script.
>
>    ./glmmadmb-mingw64-r3274-windows10-mingw64.exe -crit 1.e-6  -ind
> glmmadmb1.dat -ainp glmmadmb.par -maxph 5 -shess -noinit -phase 7
>
> (I have added two extra phases and modified the convergence criterion to
> 1.e-6.)
>
> which converged with nice estimates etc.   Now one of the really nice
> things about actually fitting the model
> rather than resorting to other diagnostics is that if you are successful
> you can look at the fit.  In this
> case the squared residuals divided by the predicted variance.  These are
> in the output glmmadmb.rep file.
>
> I sorted them in R and looked at the large ones at the end.
>
>
> index           obs    pred             whatever its called
> 1206 1206  319 3.47536e+01 1.24498e+01
> 305   305 4490 2.18655e+03 1.29949e+01
> 1074 1074  413 5.13552e+01 1.36380e+01
> 1385 1385 4002 1.68879e+03 1.69679e+01
> 854   854  224 1.22219e+01 1.96515e+01
> 1691 1691 2713 8.33316e+02 2.27056e+01
> 1427 1427 1732 3.92621e+02 2.44684e+01
> 1433 1433 1612 3.25266e+02 2.72590e+01
> 1313 1313 1815 3.52356e+02 3.25137e+01
> 341   341 2031 3.55824e+02 4.22336e+01
> 191   191 5814 7.18097e+02 1.93656e+02
> 599   599 3586 2.68911e+02 2.19118e+02
>
> So you have a few gigantic outliers.  This is fairly common with data for
> which the model
> has problems converging.  But rather than celebrating the failure of the
> model as a useful diagnostic for
> bad data it is really useful to coax it to fit the data and investigate
> the residuals,
> If you can explain them it should help.
>
>
>
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>


-- 
*Stéphanie PERIQUET (PhD) * - Bat-eared Fox Research Project
*Dept of Zoology & Entomology*
*University of the Free State, Qwaqwa Campus*
*Cell: +27 79 570 2683*
ResearchGate profile
<https://www.researchgate.net/profile/Stephanie_Periquet>


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