glmmTMB syntax to brm() syntax

   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 at 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

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--
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.