r descr_models("logistic_reg", "stan")
This engine has no tuning parameters.
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.
logistic_reg() %>% set_engine("stan") %>% translate()
Note that the refresh
default prevents logging of the estimation process. Change this value in set_engine()
to show the MCMC logs.
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.
The "Fitting and Predicting with parsnip" article contains examples for logistic_reg()
with the "stan"
engine.
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