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

[Meng Liu]

Hi Ben and Guilaume,

Thank you for reply. I am working on a precision experiment design, in
which a sample will be tested using different assay, by different operator
at different site. The measurement is allele frequency of DNA, which is a
continuous proportion outcome. I originally plan to run a beta distribution
random effects model, among which assay, operator and site are all random
factors. However, because I found there are some zeros in the response
data, that's why I am trying to run a zero-inflated beta random effects
model, with random factors in both zero part and non zero part. I.e., we
assume there will be variance from each factor in terms of predicting zero,
and variance from each factor in terms of continuous data. However, the
final research question would be evaluating the total variance contributed
from each factor.I can see here it is more complex for just generalized
linear model because of random effects from two different models. I am
wondering if you have any idea on this or do you know anybody who might
have thoughts on this?

Thank you again for all help!

Best regards,

Meng

On Wed, Jun 13, 2018 at 9:38 AM, Ben Bolker <bbolker using gmail.com> wrote:

>   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.
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> >>
> >>
> >
> > _______________________________________________
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