details_logistic_reg_stan: Logistic regression via stan

In parsnip: A Common API to Modeling and Analysis Functions

Logistic regression via stan

Description

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

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. 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.

References

• McElreath, R. 2020 Statistical Rethinking. CRC Press.

parsnip documentation built on June 24, 2024, 5:14 p.m.