The rstanarm model-fitting functions return an object of class
'stanreg', which is a list containing at a minimum the components listed
stanreg object will also have additional classes (e.g. 'aov',
'betareg', 'glm', 'polr', etc.) and several additional components depending
on the model and estimation algorithm.
Some additional details apply to models estimated using the
stan_jm modelling functions. The
function returns an object of class
'stanmvreg', which inherits the
'stanreg' class, but has a number of additional elements described in the
subsection below. The
stan_jm modelling function returns an object of class
'stanjm', which inherits both the
classes, but has a number of additional elements described in the subsection below.
'stanmvreg' classes have several of their own
methods for situations in which the default
'stanreg' methods are not
suitable; see the See Also section below.
Point estimates, as described in
Standard errors based on
mad, as described in
Residuals of type
Fitted mean values. For GLMs the linear predictors are transformed by the inverse link function.
Linear fit on the link scale. For linear models this is the same as
Variance-covariance matrix for the coefficients based on draws from the posterior distribution, the variational approximation, or the asymptotic sampling distribution, depending on the estimation algorithm.
If requested, the the model frame, model matrix and response variable used, respectively.
family object used.
The matched call.
The estimation method used.
A list with information about the prior distributions used.
The object of
stanfit-class returned by RStan and a
matrix of various summary statistics from the stanfit object.
The version of the rstan package that was used to fit the model.
The names of the grouping factors and group specific parameters, collapsed across the longitudinal or glmer submodels.
The unique factor levels for each grouping factor, collapsed across the longitudinal or glmer submodels.
The number of longitudinal or glmer submodels.
The number of observations for each longitudinal or glmer submodel.
The number of levels for each grouping factor (for models estimated using
stan_jm, this will be equal to
n_subjects if the
individual is the only grouping factor).
The time taken to fit the model (in minutes).
The names of the variables distinguishing between individuals, and representing time in the longitudinal submodel.
The number of individuals.
The number of non-censored events.
The event (or censoring) time and status indicator for each individual.
A list containing information about the baseline hazard.
An array containing information about the association structure.
The width of the one-sided difference used to numerically evaluate the slope of the longitudinal trajectory; only relevant if a slope-based association structure was specified (e.g. etaslope, muslope, etc).
The number of Gauss-Kronrod quadrature nodes used to evaluate the cumulative hazard in the joint likelihood function.