`rstanarm::stan_glm()`

uses Bayesian estimation to fit a model for
count data.

## Details

For this engine, there is a single mode: regression

### Tuning Parameters

This engine 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. The `"stan"`

engine does not fit any
hierarchical terms.

`prior_intercept`

: The prior distribution for the intercept (after
centering all predictors).

See `rstan::sampling()`

and
`rstanarm::priors()`

for more information on these
and other options.

### Translation from parsnip to the original package

```
## Poisson Regression Model Specification (regression)
##
## Computational engine: stan
##
## Model fit template:
## rstanarm::stan_glm(formula = missing_arg(), data = missing_arg(),
## weights = missing_arg(), family = stats::poisson)
```

Note that the `refresh`

default prevents logging of the estimation
process. Change this value in `set_engine()`

to show the MCMC logs.

### Preprocessing requirements

Factor/categorical predictors need to be converted to numeric values
(e.g., dummy or indicator variables) for this engine. When using the
formula method via
`fit.model_spec()`

, parsnip will
convert factor columns to indicators.

### Other details

For prediction, the `"stan"`

engine can compute posterior intervals
analogous to confidence and prediction intervals. In these instances,
the units are the original outcome and when `std_error = TRUE`

, the
standard deviation of the posterior distribution (or posterior
predictive distribution as appropriate) is returned.

### Examples

The “Fitting and Predicting with parsnip” article contains
examples
for `poisson_reg()`

with the `"stan"`

engine.

### References