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.