[R-sig-ME] alternative suggestions to glmmTMB family=beta

[Ben Bolker]

What is bmt? numeric or factor? if factor, how many levels does it
have?  If numeric, centering the predictor often helps.

 - the mgcv package can fit beta-distributed responses; I'm not sure
if it does "unstructured" (general positive-definite)
variance-covariance matrices or not. (It doesn't seem straightforward:
https://stat.ethz.ch/R-manual/R-devel/library/mgcv/html/random.effects.html)

 - you could take the good old-fashioned approach of
logit-transforming your responses and fitting a linear model

 - you could try simplifying the model: i.e. perhaps a diagonal
(diag(bmt|studyid)) or compound-symmetric (cs(bmt|studyid))
variance-covariance model would be adequate?

 - as a last resort, or if you're really attached to this particular
model, you could try to understand precisely which parameters are
flat/strongly correlated.  If you want to do that, respond here and I
(or Mollie Brooks) can try to talk you through extracting the Hessian
of the fit and figuring out which components/directions are
non-positive ...


On Wed, Apr 11, 2018 at 4:26 PM, Nat Holland <jnhollandiii at gmail.com> wrote:
> I have tried to use the following model to fit beta distribution response
> variable, with high frequency of data at upper end of 0 to 1.0 range of
> histogram.
>
> glmmTMB(vas2 ~ bmt + (bmt|studyid), family=list(family="beta",link="logit"))
>
> I get the following warning messages:
> Warning messages:
> 1: In fitTMB(TMBStruc) :
>   Model convergence problem; non-positive-definite Hessian matrix. See
> vignette('troubleshooting')
> 2: In fitTMB(TMBStruc) :
>   Model convergence problem; false convergence (8). See
> vignette('troubleshooting')
>
> Reading about this on the troubleshooting pages suggests "Models with
> non-positive definite Hessian matricies should be excluded from further
> consideration, in general."
>
> Any suggestions on alternative means of analyses to evaluate the above
> model?
>
> Thanks in advance,
> Nat
>
> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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