| available-models {rstanarm} | 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
lmoraovbut with novel regularizing priors on the model parameters that are driven by prior beliefs aboutR^2, the proportion of variance in the outcome attributable to the predictors in a linear model. stan_glm,stan_glm.nb-
Similar to
glmbut 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 afamilyobject (e.g. the shape parameter in Gamma models). It is also possible to estimate a negative binomial model in a similar way to theglm.nbfunction in the MASS package. stan_glmer,stan_glmer.nb,stan_lmer-
Similar to the
glmer,glmer.nbandlmerfunctions 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
nlmerin 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
gamm4in 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 callingglmerlikegamm4does,stan_gamm4essentially callsstan_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
polrin 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
betaregin 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
clogitin 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 toclogitthat allowstan_clogitto accept group-specific terms as instan_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).