[R-sig-ME] Fwd: alternative suggestions to glmmTMB family=beta
Ben Bolker
bbolker at gmail.com
Thu Apr 12 19:47:21 CEST 2018
(resending: pix were embedded, so the message got bounced)
A couple of things here:
(1) fitting (bmt | studyid) is very ambitious if bmt is a factor with 10
levels - that means fitting a 10x10 variance-covariance matrix (55
variance-covariance parameters). OK if you have a giant data set, but
otherwise (a) consider structured (compound symmetric or diagonal) var-cov
matrix; (b) use brms package (which does have beta distribution, and
allows/requires a prior on the variance-covariance matrix which will make
things better).
(2) the marginal distribution not looking right doesn't necessarily mean
the residual distribution isn't OK for linear models.
cheers
Ben Bolker
On Thu, Apr 12, 2018 at 12:54 PM, Nat Holland <jnhollandiii at gmail.com>
wrote:
> 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
> ations?user=VbHqPXEAAAAJ&hl=en
> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
>
> 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
>> > ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
>> >
>> > [[alternative HTML version deleted]]
>> >
>> > _______________________________________________
>> > R-sig-mixed-models at r-project.org mailing list
>> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>
>
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