extracting parameter sets of MCMC iterations from `model_fit` object

[joshyam-k]

extracting parameter sets of MCMC iterations from `model_fit` object

[joshyam-k]

ah, amazing!!! thank you so much!

[hfrick]

You can access the fitted stanreg model stored inside the parsnip model object with extract_fit_engine() and then call as.data.frame() on it like you did in your example 🙌

library(tidymodels)
library(multilevelmod)
library(bayesrules)
library(rstanarm)
#> [...]

data(cherry_blossom_sample)
running <- cherry_blossom_sample

running_model_1 <- stan_glmer(
  net ~ age + (1 | runner), 
  data = running, family = gaussian,
  prior_intercept = normal(100, 10),
  prior = normal(2.5, 1), 
  prior_aux = exponential(1, autoscale = TRUE),
  prior_covariance = decov(reg = 1, conc = 1, shape = 1, scale = 1),
  chains = 4, iter = 5000*2, seed = 84735)
#> [...]
coef_1 <- as.data.frame(running_model_1)

# with multilevelmod
running_model_2 <- linear_reg() %>% 
  set_engine("stan_glmer", family = gaussian,
             prior_intercept = normal(100, 10),
             prior = normal(2.5, 1), 
             prior_aux = exponential(1, autoscale = TRUE),
             prior_covariance = decov(reg = 1, conc = 1, shape = 1, scale = 1),
             chains = 4, iter = 5000*2, seed = 84735) %>% 
  fit(net ~ age + (1 | runner), data = running)

coef_2 <- running_model_2 %>% 
  extract_fit_engine() %>% 
  as.data.frame()

waldo::compare(coef_1, coef_2)
#> ✔ No differences

^{Created on 2022-11-14 with reprex v2.0.2}

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