Linear regression via Bayesian MethodsSource:
"stan" engine estimates regression parameters using Bayesian estimation.
For this engine, there is a single mode: regression
Some relevant arguments that can be passed to
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. See the
"stan_glmer"engine from the multilevelmod package for that type of model.
prior_intercept: The prior distribution for the intercept (after centering all predictors).
## Linear Regression Model Specification (regression) ## ## Computational engine: stan ## ## Model fit template: ## rstanarm::stan_glm(formula = missing_arg(), data = missing_arg(), ## weights = missing_arg(), family = stats::gaussian, refresh = 0)
Note that the
refresh default prevents logging of the estimation
process. Change this value in
set_engine() to show the MCMC logs.
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.
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.
This model can utilize case weights during model fitting. To use them,
see the documentation in case_weights and the examples