[R-sig-ME] glmmTMB syntax to brm() syntax

[Simon Harmel]

Super interesting! Thank you very much!

On Sun, Oct 27, 2024 at 3:31 PM Ben Bolker <bbolker using gmail.com> wrote:
>
>    the way you would do this in glmmTMB (with simulate_new [sic], you
> wouldn't need a different function:
>
>   * conditional model: ~condition + Cognitive_Rate ... (not surprisingly)
>   * random effect model :  ... + (1|subject) (also not surprising
>   * varIdent:  dispformula = ~  (1|condition)
>
>
> The tricky part is the corSymm part.  To do this you'd probably need to
> add an 'observation number within subject' variable (say it's called
> `obs`) and use  ... + (0 + obs | subject).
>
>    I don't know if that would correspond exactly. You could try some
> experiments.
>
>    If not, you might have to implement the simulation code yourself ...
>
>    Ben Bolker
>
> On 10/26/24 21:51, Simon Harmel wrote:
> > Ben, can I ask a similar question? Is there also a function similar to
> > glmmTMB::new_simulate() to easily simulate the following model?
> >
> > nlme::lme(Y ~ condition + Cognitive_Rate, random= ~1 | subject, data = DATA,
> > weights = varIdent(form=~1|condition),
> >   correlation = corSymm(form = ~1| subject))
> >
> > Thank you!
> > Simon
> >
> > On Thu, Oct 24, 2024 at 9:11 PM Ben Bolker <bbolker using gmail.com> wrote:
> >>
> >>     Most of the time taken by the brms version is in compilation. It may
> >> be possible (I don't remember how) to cache the compiled model and
> >> re-use it for subsequent models, if you are going to be (for example)
> >> fitting the same model to many different data sets ...
> >>
> >> On 10/24/24 21:44, Simon Harmel wrote:
> >>> Thank you so very much, Ben! And wow, the brm() version is extremely slow.
> >>>
> >>> Simon
> >>>
> >>> On Thu, Oct 24, 2024 at 11:11 AM Ben Bolker <bbolker using gmail.com> wrote:
> >>>>
> >>>>      See below.  The two models (glmmTMB and brms) give sufficiently
> >>>> similar estimates that I'm confident that the specifications match.
> >>>>
> >>>> set.seed(101)
> >>>> library(glmmTMB)
> >>>> library(brms)
> >>>> library(broom.mixed)
> >>>> library(tidyverse)
> >>>>
> >>>> dd <- data.frame(ID = rep(1:100, each = 10),
> >>>>                     TRIAL_INDEX = rep(1:10, 100),
> >>>>                     con = rnorm(1000))
> >>>> dd$pic_percent <- simulate_new(
> >>>>        ~ con + (0+con | ID) +
> >>>>            (0+con | TRIAL_INDEX),
> >>>>        ziformula = ~1,
> >>>>        family = beta_family(),
> >>>>        newdata = dd,
> >>>>        newparams = list(beta = c(0, 0.5), theta = rep(-1,2),
> >>>>                         betadisp = 1, betazi = -2))[[1]]
> >>>>
> >>>>
> >>>> m1 <- glmmTMB(pic_percent ~ con + (0+con | ID) +
> >>>>        (0+con | TRIAL_INDEX),
> >>>>            data=dd,
> >>>>            family = beta_family(),
> >>>>            ziformula = ~1)
> >>>>
> >>>> ##
> >>>> https://mvuorre.github.io/posts/2019-02-18-analyze-analog-scale-ratings-with-zero-one-inflated-beta-models/
> >>>> m2 <- brm(
> >>>>        bf(pic_percent ~ con + (0+con | ID) +
> >>>>               (0+con | TRIAL_INDEX),
> >>>>        zi = ~ 1),
> >>>>        data=dd,
> >>>>        family = zero_inflated_beta()
> >>>> )
> >>>>
> >>>>
> >>>> (purrr::map_dfr(list(glmmTMB = m1, brms = m2), tidy, .id = "model")
> >>>>        |> select(model, effect, component, group, term, estimate)
> >>>>        |> pivot_wider(names_from = model, values_from = estimate)
> >>>> )
> >>>>
> >>>>
> >>>>
> >>>> On 10/23/24 19:13, Simon Harmel wrote:
> >>>>> Hello all,
> >>>>>
> >>>>> I was wondering what is the closest equivalent of my glmmTMB syntax below
> >>>>> in brms::brm() syntax?
> >>>>>
> >>>>> glmmTMBglmmTMB(pic_percent ~ con +
> >>>>>                          (0+con | ID) +
> >>>>>                          (0+con | TRIAL_INDEX),
> >>>>>                        data=DATA,
> >>>>>            family = beta_family(),
> >>>>>            ziformula = ~1)
> >>>>>
> >>>>>
> >>>>> Thank you,
> >>>>>
> >>>>> Simon
> >>>>>
> >>>>>         [[alternative HTML version deleted]]
> >>>>>
> >>>>> _______________________________________________
> >>>>> R-sig-mixed-models using r-project.org mailing list
> >>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >>>>
> >>>> --
> >>>> Dr. Benjamin Bolker
> >>>> Professor, Mathematics & Statistics and Biology, McMaster University
> >>>> Director, School of Computational Science and Engineering
> >>>> * E-mail is sent at my convenience; I don't expect replies outside of
> >>>> working hours.
> >>>>
> >>>> _______________________________________________
> >>>> R-sig-mixed-models using r-project.org mailing list
> >>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >>
> >> --
> >> Dr. Benjamin Bolker
> >> Professor, Mathematics & Statistics and Biology, McMaster University
> >> Director, School of Computational Science and Engineering
> >> * E-mail is sent at my convenience; I don't expect replies outside of
> >> working hours.
> >>
>
> --
> Dr. Benjamin Bolker
> Professor, Mathematics & Statistics and Biology, McMaster University
> Director, School of Computational Science and Engineering
> * E-mail is sent at my convenience; I don't expect replies outside of
> working hours.
>