as.mcmc.brmsfit

as.mcmc

An <code>R</code> object typically of class <code>brmsfit</code>

Names of parameters for which posterior samples 
should be returned, as given by a character vector or regular expressions.
By default, all posterior samples of all parameters are extracted.

pars

Indicates whether parameter names 
should be matched exactly (<code>TRUE</code>) or treated as
regular expressions (<code>FALSE</code>). Default is <code>FALSE</code>.

fixed

Indicates whether chains should be combined.

combine_chains

Indicates if the warmup samples should be included.
Default is <code>FALSE</code>. Warmup samples are used to tune the 
parameters of the sampling algorithm and should not be analyzed.

inc_warmup

Extract posterior samples for use with the coda package

Fit Bayesian generalized (non-)linear multivariate multilevel models
using 'Stan' for full Bayesian inference. A wide range of distributions
and link functions are supported, allowing users to fit -- among others --
linear, robust linear, count data, survival, response times, ordinal,
zero-inflated, hurdle, and even self-defined mixture models all in a
multilevel context. Further modeling options include non-linear and
smooth terms, auto-correlation structures, censored data, meta-analytic
standard errors, and quite a few more. In addition, all parameters of the
response distribution can be predicted in order to perform distributional
regression. Prior specifications are flexible and explicitly encourage
users to apply prior distributions that actually reflect their beliefs.
Model fit can easily be assessed and compared with posterior predictive
checks and leave-one-out cross-validation. References: B<c3><bc>rkner (2017)
<doi:10.18637/jss.v080.i01>; B<c3><bc>rkner (2018) <doi:10.32614/RJ-2018-017>;
Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

PaulChristian Buerkner

as.mcmc.brmsfit: Extract posterior samples for use with the coda package

Description

Usage

Arguments

Value