[R-sig-ME] fitting beta and zero mixture model containing both nested and crossed random effects
Guillaume Chaumet
guill@umech@umet @ending from gm@il@com
Wed Jun 13 10:26:07 CEST 2018
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]]
>> >
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