[R-sig-ME] Fwd: fitting beta and zero mixture model containing both nested and crossed random effects

[Ben Bolker]

I think Guillaume is right that brms can do this, but  I don't see why
glmmTMB *can't* do all of this (fixed and (nested and/or crossed
random effects)) in both the binomial (logistic) and beta components)

On Sun, Jun 10, 2018 at 11:04 AM, Guillaume Chaumet
<guillaumechaumet using gmail.com> wrote:
> ---------- Forwarded message ----------
> From: Guillaume Chaumet <guillaumechaumet using gmail.com>
> Date: 2018-06-10 17:03 GMT+02:00
> Subject: Re: [R-sig-ME] fitting beta and zero mixture model containing
> both nested and crossed random effects
> To: Meng Liu <liumeng using usc.edu>
>
>
> brms: https://cran.r-project.org/web/packages/brms/index.html
>
> 2018-06-09 21:06 GMT+02:00 Meng Liu <liumeng using usc.edu>:
>> To whom it may concern,
>>
>> I am trying to fit a model for a data among which the response value is
>> within [0,1). I am thinking about fitting the zeros as a complete separate
>> category from the non-zero data, i.e. a binomial (Bernoulli) model to "==0
>> vs >0" and a Beta model to the >0 responses. Also, my data contains both
>> nested factors and crossed factors, which means I need to add nested random
>> effects and crossed random effects to both logistic model part and beta
>> model model. However, I didn't find any R packages can do exactly what I
>> want (By far I found gamlss, glmmTMB, zoib but they either can only assume
>> random zero or they can only fit repeated measures/clustered data but not
>> nested and crossed design). Therefore, I am wondering if any one know if
>> there is any available package or function can do this.
>>
>> Thank you very much for your help!
>>
>> Best regards
>>
>> Meng
>>
>>         [[alternative HTML version deleted]]
>>
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