get_y

A <code><a rd-options="" href="/link/brmsfit?package=brms&version=2.12.0" data-mini-rdoc="brms::brmsfit">brmsfit</a></code> object.

Optional names of response variables for which to extract values.

resp

warn

Further arguments passed to <code><a rd-options="" href="/link/standata?package=brms&version=2.12.0" data-mini-rdoc="brms::standata">standata</a></code>.

Extract response values from a <code><a rd-options="" href="/link/brmsfit?package=brms&version=2.12.0" data-mini-rdoc="brms::brmsfit">brmsfit</a></code> object.

internal

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

brms

Bayesian Regression Models using 'Stan'

Paul-Christian B<c3><bc>rkner

get_y function

A <code><a rd-options='' href='brmsfit'>brmsfit</a></code> object.

Further arguments passed to <code><a rd-options='' href='standata'>standata</a></code>.

Extract response values from a <code><a rd-options='' href='brmsfit'>brmsfit</a></code> object.

get_y: Extract response values

Description

Usage

Arguments

Value