`rstanarm::stan_glm()`

fits a generalized linear model for binary outcomes.
A linear combination of the predictors is used to model the log odds of an
event.

## Details

For this engine, there is a single mode: classification

### 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. This`"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

```
logistic_reg() %>%
set_engine("stan") %>%
translate()
```

```
## Logistic Regression Model Specification (classification)
##
## Computational engine: stan
##
## Model fit template:
## rstanarm::stan_glm(formula = missing_arg(), data = missing_arg(),
## weights = missing_arg(), family = stats::binomial, refresh = 0)
```

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()`

, 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.

### Case weights

This model can utilize case weights during model fitting. To use them,
see the documentation in case_weights and the examples
on `tidymodels.org`

.

The `fit()`

and `fit_xy()`

arguments have arguments called
`case_weights`

that expect vectors of case weights.

### Examples

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

with the `"stan"`

engine.