Hi,

I fitted this model with brms on a large dataset (70 000 observations) :

```
model_formula <- brmsformula(hunting_success | trials(4) ~
Zspeed +
Zspace_covered_rate +
Zprox_mid_PreyGuarding +
Zhook_start_time +
Zgame_duration +
(1 | map_name) +
(1 | player_id) +
(1 | obs))
```

```
base_model <- brm(formula = model_formula,
family = binomial(link = "logit"),
warmup = 3000,
iter = 11000,
thin = 32,
chains = 4,
inits = "0",
threads = threading(10),
backend = "cmdstanr",
seed = 123,
prior = priors,
control = list(adapt_delta = 0.95),
save_pars = save_pars(all = TRUE),
sample_prior = TRUE,
data = data)
```

The size of the output is ~1.5 Go. I want to extract the predicted values on the response scale to compute the linear trends for each fixed effects. However, I don’t understand why extracting the draws takes so much time.

For instance I ran this to have the predicted values of the model, but it’s been running for +20 minutes and it is only 30 draws:

```
draws <- posterior_linpred(base_model,
re_formula = NA,
transform = FALSE,
ndraws = 30,
seed = 123)
```

Am I doing something wrong? Is it because of the overdispersion term? I didn’t have this problem before with an earlier version of brms so I don’t understand what might be going on. Is there a way to extract the predicted values in a faster way so I can easily manipulate them myself?

This is my computer setup :

R version 4.0.4 (2021-02-15)

Platform: x86_64-w64-mingw32/x64 (64-bit)

Running under: Windows 10 x64 (build 19042)

brms_2.16.1, Rcpp_1.0.7

Thank you very much for your help!

Maxime