as.mcmc

as.mcmc.brmsfit

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 or treated as regular expression. 
Default is <code>FALSE</code>.

exact_match

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 <span class="pkg">coda</span> package


Fit Bayesian generalized (non-)linear 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, binomial, Poisson, survival, response times, ordinal,
zero-inflated, hurdle, and even non-linear models all in a
multilevel context. Further modeling options include auto-correlation
and smoothing terms, user defined dependence 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.
In addition, model fit can easily be assessed and compared with
posterior predictive checks and leave-one-out cross-validation.

PaulChristian Buerkner

brms

Bayesian Regression Models using Stan

as.mcmc.brmsfit function


Extract posterior samples for use with the <span class = "pkg">coda</span> package


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

Description

Usage

Arguments

Value