Stan Development Team
The rstanarm package is an appendage to the rstan package that
enables many of the most common applied regression models to be estimated
using Markov Chain Monte Carlo, variational approximations to the posterior
distribution, or optimization. The rstanarm package allows these models
to be specified using the customary R modeling syntax (e.g., like that of
glm with a
formula and a
The sections below provide an overview of the modeling functions and estimation algorithms used by rstanarm.
The set of models supported by rstanarm is large (and will continue to
grow), but also limited enough so that it is possible to integrate them
tightly with the
pp_check function for graphical posterior
predictive checks with bayesplot and the
posterior_predict function to easily estimate the effect of
specific manipulations of predictor variables or to predict the outcome in a
The objects returned by the rstanarm modeling functions are called
stanreg objects. In addition to all of the
methods defined for fitted model
objects, stanreg objects can be passed to the
in the loo package for model comparison or to the
launch_shinystan function in the shinystan
package in order to visualize the posterior distribution using the ShinyStan
graphical user interface. See the rstanarm vignettes for more details
about the entire process.
See priors help page and the vignette Prior Distributions for rstanarm Models for an overview of the various choices the user can make for prior distributions. The package vignettes for the modeling functions also provide examples of using many of the available priors as well as more detailed descriptions of some of the novel priors used by rstanarm.
The model estimating functions are described in greater detail in their individual help pages and vignettes. Here we provide a very brief overview:
aov but with
novel regularizing priors on the model parameters that are driven by prior
beliefs about \(R^2\), the proportion of variance in the outcome
attributable to the predictors in a linear model.
glm but with various possible prior
distributions for the coefficients and, if applicable, a prior distribution
for any auxiliary parameter in a Generalized Linear Model (GLM) that is
characterized by a
family object (e.g. the shape
parameter in Gamma models). It is also possible to estimate a negative
binomial model in a similar way to the
in the MASS package.
Similar to the
lmer functions in the lme4 package in that GLMs
are augmented to have group-specific terms that deviate from the common
coefficients according to a mean-zero multivariate normal distribution with
a highly-structured but unknown covariance matrix (for which rstanarm
introduces an innovative prior distribution). MCMC provides more
appropriate estimates of uncertainty for models that consist of a mix of
common and group-specific parameters.
nlmer in the lme4 package for
nonlinear "mixed-effects" models, but the group-specific coefficients
have flexible priors on their unknown covariance matrices.
gamm4 in the gamm4 package, which
augments a GLM (possibly with group-specific terms) with nonlinear smooth
functions of the predictors to form a Generalized Additive Mixed Model
(GAMM). Rather than calling
stan_gamm4 essentially calls
stan_glmer, which avoids the optimization issues that often
crop up with GAMMs and provides better estimates for the uncertainty of the
polr in the MASS package in that it
models an ordinal response, but the Bayesian model also implies a prior
distribution on the unknown cutpoints. Can also be used to model binary
outcomes, possibly while estimating an unknown exponent governing the
probability of success.
betareg in that it models an outcome that
is a rate (proportion) but, rather than performing maximum likelihood
estimation, full Bayesian estimation is performed by default, with
customizable prior distributions for all parameters.
clogit in that it models an binary outcome
where the number of successes and failures is fixed within each stratum by
the research design. There are some minor syntactical differences relative
clogit that allow
stan_clogit to accept
group-specific terms as in
A multivariate form of
stan_glmer, whereby the user can
specify one or more submodels each consisting of a GLM with group-specific
terms. If more than one submodel is specified (i.e. there is more than one
outcome variable) then a dependence is induced by assuming that the
group-specific terms for each grouping factor are correlated across submodels.
Estimates shared parameter joint models for longitudinal and time-to-event (i.e. survival) data. The joint model can be univariate (i.e. one longitudinal outcome) or multivariate (i.e. more than one longitudinal outcome). A variety of parameterisations are available for linking the longitudinal and event processes (i.e. a variety of association structures).
The modeling functions in the rstanarm package take an
argument that can be one of the following:
Uses Markov Chain Monte Carlo (MCMC) --- in particular, Hamiltonian Monte
Carlo (HMC) with a tuned but diagonal mass matrix --- to draw from the
posterior distribution of the parameters. See
(rstan) for more details. This is the slowest but most reliable of the
available estimation algorithms and it is the default and
recommended algorithm for statistical inference.
Uses mean-field variational inference to draw from an approximation to the
posterior distribution. In particular, this algorithm finds the set of
independent normal distributions in the unconstrained space that --- when
transformed into the constrained space --- most closely approximate the
posterior distribution. Then it draws repeatedly from these independent
normal distributions and transforms them into the constrained space. The
entire process is much faster than HMC and yields independent draws but
is not recommended for final statistical inference. It can be
useful to narrow the set of candidate models in large problems, particularly
stan_gamm4, but is only
an approximation to the posterior distribution.
Uses full-rank variational inference to draw from an approximation to the posterior distribution by finding the multivariate normal distribution in the unconstrained space that --- when transformed into the constrained space --- most closely approximates the posterior distribution. Then it draws repeatedly from this multivariate normal distribution and transforms the draws into the constrained space. This process is slower than meanfield variational inference but is faster than HMC. Although still an approximation to the posterior distribution and thus not recommended for final statistical inference, the approximation is more realistic than that of mean-field variational inference because the parameters are not assumed to be independent in the unconstrained space. Nevertheless, fullrank variational inference is a more difficult optimization problem and the algorithm is more prone to non-convergence or convergence to a local optimum.
Finds the posterior mode using a C++ implementation of the LBGFS algorithm.
optimizing for more details. If there is no prior
information, then this is equivalent to maximum likelihood, in which case
there is no great reason to use the functions in the rstanarm package
over the emulated functions in other packages. However, if priors are
specified, then the estimates are penalized maximum likelihood estimates,
which may have some redeeming value. Currently, optimization is only
Bates, D., Maechler, M., Bolker, B., and Walker, S. (2015). Fitting linear mixed-Effects models using lme4. Journal of Statistical Software. 67(1), 1--48.
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). Bayesian Data Analysis. Chapman & Hall/CRC Press, London, third edition. http://stat.columbia.edu/~gelman/book/
Gelman, A. and Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press, Cambridge, UK. http://stat.columbia.edu/~gelman/arm/
Stan Development Team. (2017). Stan Modeling Language Users Guide and Reference Manual. http://mc-stan.org/documentation/
Vehtari, A., Gelman, A., and Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing. 27(5), 1413--1432. doi:10.1007/s11222-016-9696-4. arXiv preprint: http://arxiv.org/abs/1507.04544/
Yao, Y., Vehtari, A., Simpson, D., and Gelman, A. (2018) Using stacking to average Bayesian predictive distributions. Bayesian Analysis, advance publication, doi:10.1214/17-BA1091. (online).
Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., and Gelman, A. (2018). Visualization in Bayesian workflow. Journal of the Royal Statistical Society Series A, accepted for publication. arXiv preprint: http://arxiv.org/abs/1709.01449.
Muth, C., Oravecz, Z., and Gabry, J. (2018) User-friendly Bayesian regression modeling: A tutorial with rstanarm and shinystan. The Quantitative Methods for Psychology. 14(2), 99--119. https://www.tqmp.org/RegularArticles/vol14-2/p099/p099.pdf