add_ic

add_ic.brmsfit

add_ic&lt;-

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

Further arguments passed to the underlying 
functions computing the model fit criteria.

Names of model fit criteria
to compute. Currently supported are <code>"loo"</code>, 
<code>"waic"</code>, <code>"kfold"</code>, <code>"R2"</code> (R-squared), and 
<code>"marglik"</code> (log marginal likelihood).

ic, value

Optional name of the model. If <code>NULL</code>
(the default) the name is taken from the call to <code>x</code>.

model_name

Deprecated alias of <code><a rd-options="" href="/link/add_criterion?package=brms&version=2.9.0" data-mini-rdoc="brms::add_criterion">add_criterion</a></code>.

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>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

PaulChristian Buerkner

brms

Bayesian Regression Models using 'Stan'

Paul-Christian B<c3><bc>rkner

add_ic function

Deprecated alias of <code><a rd-options='' href='add_criterion'>add_criterion</a></code>.

add_ic: Add model fit criteria to model objects

Description

Usage

Arguments

Value