nsamples

nsamples.brmsfit

An optional integer vector defining a 
subset of samples to be considered.

subset

A flag indicating whether to also 
count warmup / burn-in samples.

incl_warmup

Further arguments passed to or from other methods


Extract the number of posterior samples 
stored in a fitted Bayesian model.


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.

nsamples.brmsfit: Number of Posterior Samples

Description

Usage

Arguments

Details