available-models: Modeling functions available in 'rstanarm'
In rstanarm: Bayesian Applied Regression Modeling via Stan

available-models

R Documentation

Modeling functions available in rstanarm

Description

Modeling functions available in rstanarm

Modeling functions

The model estimating functions are described in greater detail in their individual help pages and vignettes. Here we provide a very brief overview:

stan_lm, stan_aov, stan_biglm: Similar to lm or 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.
stan_glm, stan_glm.nb: Similar to 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 glm.nb function in the MASS package.
stan_glmer, stan_glmer.nb, stan_lmer: Similar to the glmer, glmer.nb and 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.
stan_nlmer: Similar to nlmer in the lme4 package for nonlinear "mixed-effects" models, but the group-specific coefficients have flexible priors on their unknown covariance matrices.
stan_gamm4: Similar to 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 glmer like gamm4 does, 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 parameter estimates.
stan_polr: Similar to 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.
stan_betareg: Similar to 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.
stan_clogit: Similar to 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 to clogit that allow stan_clogit to accept group-specific terms as in stan_glmer.
stan_mvmer: 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.
stan_jm: 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).