alternative suggestions to glmmTMB family=beta

Thanks...
bmt is a factor with 10 levels.

Here is freq distribution untransformed percentages...



I am ok with good ole fashioned transformation, but when I logit transform
proportions using (car) logit function, then i still get the spike at upper
end:


This distribution is not sufficient for linear model...

Nat



~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
J. Nathaniel Holland, Ph.D.
Research and Data Scientist
e-mail: jnhollandiii at gmail.com
LinkedIn:  https://www.linkedin.com/in/jnhollandiii/
Google Scholar:  https://scholar.google.com/cit
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On Thu, Apr 12, 2018 at 11:25 AM, Ben Bolker <bbolker at gmail.com> wrote:

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/rand
om.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

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
J. Nathaniel Holland, Ph.D.
e-mail: jnhollandiii at gmail.com
LinkedIn:  https://www.linkedin.com/in/jnhollandiii/
Google Scholar:
https://scholar.google.com/citations?user=VbHqPXEAAAAJ&hl=en
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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