[R-sig-ME] Beta-Binomial Model Question

Alex Waldman @|ex@w@|dm@n @end|ng |rom @jc@ox@@c@uk
Fri Sep 3 14:43:48 CEST 2021

Dear All,

My apologies for the additional question. I am working with data in which I have information on the proportional area taken by a lesion in different locations (region 1, 2, and 3) stratified by different lesion types (lesion type 1, 2, and 3). The denominator to derive the proportion will be different for each of the different locations. The data includes 0 and 1. Therefore, I was thinking of using a beta-binomial model. However, in my formula I included the proportion information as the response:

glmmTMB::glmmTMB(LesionAreaRatio ~ Location*LesionType + (1 | ID), family=betabinomial, data=total_data_staged, REML=TRUE, control=glmmTMBControl(optimizer=optim, optArgs=list(method="BFGS")))

I then got the following warnings:

  1.  In eval(family$initialize) : non-integer #successes in a binomial glm!
  2.  In fitTMB(TMBStruc) : Model convergence problem; extreme or very small eigenvalues detected. See vignette('troubleshooting')

I looked in the vignette and this made we wonder if this would be the right model type to use since the proportions are not success/failure data per se but rather represent a normalized area?

In addition, my data seems to be skewed toward 0s and I thought zero-inflation may be appropriate. When trying varying zero-inflation formulas I consistently received the following error (the eigenvalue error went away):

In fitTMB(TMBStruc) :
Model convergence problem; non-positive-definite Hessian matrix. See vignette('troubleshooting')

I looked in the vignette and noticed that the zero-inflation parameter was very negative no matter what I included in the zero-inflation model formula. I don’t get the Hessian matrix error if the zero-inflation is removed. Therefore, would that indicate that it is appropriate to leave out the zero-inflation?

Thanks again for all your help as this is all new to me and I want to make sure I’m going down the right path and not unnecessarily overcomplicating things.

Warm Regards,

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