Need help understanding hurdle (hurdle_gamma) models using brms

Interfaces brms
1

Hi,

I am trying to use hurdle gamma model for one of my use cases, to handle a zero-inflated scenario. I have a very simple code creating dummy data with quite a few zeros.

# Dataset prep

non_zero <- rbinom(1000, 1, 0.1)
g_vals <- rgamma(n = 1000, shape = 2, scale = 2)
dat <- data.frame(x = non_zero * g_vals)

The model is written as

hum <- brm(bf(x ~ 1, hu ~ 1), data = dat, family = hurdle_gamma)

I would like to understand the results and the associated parameters.

A plot of the predicted results from the model using

tibble(x=1) %>% add_fitted_draws(hum) %>% ggplot(aes(x = .value)) + geom_density()

is as follows

image
image700Ă—432 7.63 KB

The posterior summary is:

                  Estimate Est.Error        Q2.5       Q97.5
b_Intercept       1.468677 0.1037202    1.271352    1.681500
b_hu_Intercept    2.081498 0.1474433    1.802057    2.372279
shape             1.757053 0.3114681    1.203522    2.418776
lp__           -315.947968 1.2657819 -319.090956 -314.508678

I don’t see any divergences in the model fit. Also there is pretty good mixing of the chains for the parameters. Given the whole reason for me to consider the hurdle model was to see a model predict zeros in abundance, I am unable to understand the predictions. Shouldn’t I see a lot of zeros?

It would be great if someone can throw light on the share a simple test case on using the hurdle model. I am unable to find a nice write up of modeling using hurdle_gamma using brms.

  • Operating System: Ubuntu 18.04
  • brms Version:2.13
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2

Try using the predict functions in brms, or analogs. It looks like you’re estimating fitted results, which are conditional averages, not draws from the posterior predictive distribution.

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3

Ah! Thanks for the suggestion. I used add_predicted_draws instead and it does seem to give me results as I was looking for. I was incorrectly using add_fitted_draws instead.

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