Modeling functions available in 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).
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