I have a simple doubt about the interpretation of hurdle coefficients when using hurdle models through brms. I adapted one example from the brms vignette on distributional models [link].
In the output below the coefficient
hu_child would be the log odds of catching 0 fish, i.e, for each child in the visiting group the model predicts odds 3 times higher (exp(1.13)) of catching 0 fish. Therefore, the plot below shows the predicted probability of catching 0 fish according to number of children.
Is this a correct interpretation?
zinb <- read.csv("https://paul-buerkner.github.io/data/fish.csv") library(brms) fit_hu <- brm(bf(count ~ persons + child + camper, hu ~ child), data = zinb, family = hurdle_poisson()) summary(fit_hu) #> Family: hurdle_poisson #> Links: mu = log; hu = logit #> Formula: count ~ persons + child + camper #> hu ~ child #> Data: zinb (Number of observations: 250) #> Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1; #> total post-warmup samples = 4000 #> #> Population-Level Effects: #> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS #> Intercept -0.84 0.17 -1.18 -0.51 1.00 3767 2545 #> hu_Intercept -0.39 0.17 -0.73 -0.06 1.00 4589 2877 #> persons 0.84 0.04 0.75 0.92 1.00 3632 2946 #> child -1.15 0.09 -1.33 -0.96 1.00 3650 2974 #> camper 0.74 0.09 0.56 0.93 1.00 4080 2563 #> hu_child 1.13 0.21 0.74 1.57 1.00 3753 2579 #> #> Samples were drawn using sampling(NUTS). For each parameter, Bulk_ESS #> and Tail_ESS are effective sample size measures, and Rhat is the potential #> scale reduction factor on split chains (at convergence, Rhat = 1). plot(conditional_effects(fit_hu, effects = "child", dpar = "hu"))
Looking at a similar question [link] I get the idea that this should be correct. But I got confused because bernoulli models in brms are interpreted in the opposite way. In any case, I would also like to know which should be the most reliable source to check this (beyond this example or distribution family).