# available-models

##### Modeling functions available in rstanarm

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).

##### See Also

*Documentation reproduced from package rstanarm, version 2.18.2, License: GPL (>= 3)*