In parsnip: A Common API to Modeling and Analysis Functions

r descr_models("poisson_reg", "stan_glmer")

Tuning Parameters

This model has no tuning parameters.

Important engine-specific options

Some relevant arguments that can be passed to set_engine():

chains: A positive integer specifying the number of Markov chains. The default is 4.
iter: A positive integer specifying the number of iterations for each chain (including warmup). The default is 2000.
seed: The seed for random number generation.
cores: Number of cores to use when executing the chains in parallel.
prior: The prior distribution for the (non-hierarchical) regression coefficients.
prior_intercept: The prior distribution for the intercept (after centering all predictors).

See ?rstanarm::stan_glmer and ?rstan::sampling for more information.

Translation from parsnip to the original package

r uses_extension("poisson_reg", "stan_glmer", "regression")

library(multilevelmod)

poisson_reg(engine = "stan_glmer") %>% 
  set_engine("stan_glmer") %>% 
  translate()

Preprocessing requirements

There are no specific preprocessing needs. However, it is helpful to keep the clustering/subject identifier column as factor or character (instead of making them into dummy variables). See the examples in the next section.

Other details

The model can accept case weights.

With parsnip, we suggest using the formula method when fitting:

library(tidymodels)

poisson_reg() %>% 
  set_engine("stan_glmer") %>% 
  fit(y ~ time + x + (1 | subject), data = longitudinal_counts)

When using tidymodels infrastructure, it may be better to use a workflow. In this case, you can add the appropriate columns using add_variables() then supply the typical formula when adding the model:

library(tidymodels)

glmer_spec <- 
  poisson_reg() %>% 
  set_engine("stan_glmer")

glmer_wflow <- 
  workflow() %>% 
  # The data are included as-is using:
  add_variables(outcomes = y, predictors = c(time, x, subject)) %>% 
  add_model(glmer_spec, formula = y ~ time + x + (1 | subject))

fit(glmer_wflow, data = longitudinal_counts)

For prediction, the "stan_glmer" engine can compute posterior intervals analogous to confidence and prediction intervals. In these instances, the units are the original outcome. When std_error = TRUE, the standard deviation of the posterior distribution (or posterior predictive distribution as appropriate) is returned.

Case weights

References

McElreath, R. 2020 Statistical Rethinking. CRC Press.
Sorensen, T, Vasishth, S. 2016. Bayesian linear mixed models using Stan: A tutorial for psychologists, linguists, and cognitive scientists, arXiv:1506.06201.