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

[Ben Bolker]

  I'm not sure how this (variance decomposition based on a
zero-inflated model) would work.
What is your subject-area/scientific question?

On Wed, Jun 13, 2018 at 4:26 AM, Guillaume Chaumet
<guillaumechaumet using gmail.com> wrote:
> My bad, I replied to you the first time without including the list.
> Regarding your last question, perhaps the list and/or Ben could
> provide a more accurate answer than me.
> I'm also curious to know how glmmTMB could do that
>
> 2018-06-13 0:09 GMT+02:00 Meng Liu <liumeng using usc.edu>:
>> Hi Guillaume,
>>
>> Thank you so much for this! I just have another question: for example if I
>> have random factor A and B in both logistic model part and beta model part,
>> then after I fit the whole model and got variance component estimation of
>> random effect for factor A and B for both logistic model part and beta model
>> model part, will there be any way to combine variance together? I.e. I can
>> estimate a total variance from factor A, and a total variance from factor B
>> (i.e. only differ by factor, not model)? Something like variance
>> decomposition but I believe here is more complex as this is a mixture model.
>>
>> Thank you again for all your help
>>
>> Best regards,
>>
>> Meng
>>
>> On Sun, Jun 10, 2018 at 11:03 AM, Guillaume Chaumet
>> <guillaumechaumet using gmail.com> wrote:
>>>
>>> brms:
>>> https://urldefense.proofpoint.com/v2/url?u=https-3A__cran.r-2Dproject.org_web_packages_brms_index.html&d=DwIBaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=Ij73g98b5MaGitndhxmoIw&m=Uy-z_keMG1SfZG-g8FxVqzfz-Ghl2OHun7TY7tfexwo&s=Gfi89kd1PSimpIhWBglYPuJRn3_FF_uNBGvzVDvWe4A&e=
>>>
>>> 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]]
>>> >
>>> > _______________________________________________
>>> > R-sig-mixed-models using r-project.org mailing list
>>> >
>>> > https://urldefense.proofpoint.com/v2/url?u=https-3A__stat.ethz.ch_mailman_listinfo_r-2Dsig-2Dmixed-2Dmodels&d=DwIBaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=Ij73g98b5MaGitndhxmoIw&m=Uy-z_keMG1SfZG-g8FxVqzfz-Ghl2OHun7TY7tfexwo&s=FMNtOORgf7OlXhD5m8VHoGCnuWlt5NLqtXxalxQOhQw&e=
>>
>>
>
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