Help with model fitting?

Modeling
1

Hi I am having trouble in fitting a model in the brms which has mixed distribution or two peaks!
here is what the variables distribution look like :
first_reward
Patch_count
RR

and this the formula I am using:

m_b2_zip <- brm(
  Patch_count ~ first_Reward + RR + (1 | Name),
  data = data_b2,
  family = zero_inflated_poisson()
)

the pp_check looks like this:
model_ppc

Can someone please help ?

2

Howdy!
Just as you use histograms to visualize the data, you could use histograms to visualize your posterior predictive distribution. So, to start with, try using the pp_check() function with type='hist'. Note, that you may need to adjust the binwidth in the histogram. Since you are using a zero-inflated Poisson, you might also want to check the proportion of zeros. You might also use type='bars' or bars_grouped. In any case, you don’t want to use a density plot on discrete spaces.

pp_check(m_b2_zip, type="hist")

prop_zero <- function(Patch_count) mean(Patch_count == 0)
prop_zero_check <- pp_check(m_b2_zip, type = "stat", stat = "prop_zero")

pp_check(m_b2_zip, type="bars")

Once you have a more appropriate visualization, then you can better check the model.

Note - if you don’t mind coding in base R, even better plots, in my opinion, are like those that Michael Betancourt does in his case studies, similar to the code I used to make the plot in this post Improving PPC Fit in MELS model (brms) when using Multimodal Data - #5 by jd_c . Betancourt has some functions to do this type of plot here GitHub - betanalpha/mcmc_visualization_tools: Markov Chain Monte Carlo Visualization Tools but I personally haven’t had a chance yet to use all of them for plotting purposes.

3

So I ran mixture family using this :
mix ← mixture(skew_normal, gaussian, skew_normal, order = ‘mu’)

the density plots seem fine but i get this warning:
Warning messages:
1: There were 1000 divergent transitions after warmup. See
https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
to find out why this is a problem and how to eliminate them.
2: There were 3000 transitions after warmup that exceeded the maximum treedepth. Increase max_treedepth above 10. See
https://mc-stan.org/misc/warnings.html#maximum-treedepth-exceeded
3: There were 1 chains where the estimated Bayesian Fraction of Missing Information was low. See
https://mc-stan.org/misc/warnings.html#bfmi-low
4: Examine the pairs() plot to diagnose sampling problems

5: The largest R-hat is 4.42, indicating chains have not mixed.
Running the chains for more iterations may help. See
https://mc-stan.org/misc/warnings.html#r-hat
6: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
Running the chains for more iterations may help. See
https://mc-stan.org/misc/warnings.html#bulk-ess
7: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
Running the chains for more iterations may help. See
https://mc-stan.org/misc/warnings.html#tail-ess