Set up an autoregressive (AR) term of order p in brms. The function
does not evaluate its arguments -- it exists purely to help set up a model
with AR terms.

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 both theory-driven and
data-driven non-linear terms, auto-correlation structures, censoring and
truncation, 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 prior knowledge. Models can easily be evaluated and
compared using several methods assessing posterior or prior predictions.
References: Bürkner (2017) <doi:10.18637/jss.v080.i01>;
Bürkner (2018) <doi:10.32614/RJ-2018-017>;
Bürkner (2021) <doi:10.18637/jss.v100.i05>; Carpenter et al. (2017)
<doi:10.18637/jss.v076.i01>.

PaulChristian Buerkner

brms

Bayesian Regression Models using 'Stan'

Paul-Christian Bürkner

Jonah Gabry

Sebastian Weber

Andrew Johnson

Martin Modrak

Hamada S. Badr

Frank Weber

Mattan S. Ben-Shachar

Hayden Rabel

Simon C. Mills

Stephen Wild

ar function

<dl><dt>time</dt>
<dd>An optional time variable specifying the time ordering
of the observations. By default, the existing order of the observations
in the data is used.</dd>
<dt>gr</dt>
<dd>An optional grouping variable. If specified, the correlation
structure is assumed to apply only to observations within the same grouping
level.</dd>
<dt>p</dt>
<dd>A non-negative integer specifying the autoregressive (AR)
order of the ARMA structure. Default is <code>1</code>.</dd>
<dt>cov</dt>
<dd>A flag indicating whether ARMA effects should be estimated by
means of residual covariance matrices. This is currently only possible for
stationary ARMA effects of order 1. If the model family does not have
natural residuals, latent residuals are added automatically. If
<code>FALSE</code> (the default), a regression formulation is used that is
considerably faster and allows for ARMA effects of order higher than 1 but
is only available for <code>gaussian</code> models and some of its
generalizations.</dd></dl>

Arguments

Set up AR(p) correlation structures — ar

<dl>

<dt>time</dt>
<dd>An optional time variable specifying the time ordering
of the observations. By default, the existing order of the observations
in the data is used.</dd>


<dt>gr</dt>
<dd>An optional grouping variable. If specified, the correlation
structure is assumed to apply only to observations within the same grouping
level.</dd>


<dt>p</dt>
<dd>A non-negative integer specifying the autoregressive (AR)
order of the ARMA structure. Default is <code>1</code>.</dd>


<dt>cov</dt>
<dd>A flag indicating whether ARMA effects should be estimated by
means of residual covariance matrices. This is currently only possible for
stationary ARMA effects of order 1. If the model family does not have
natural residuals, latent residuals are added automatically. If
<code>FALSE</code> (the default), a regression formulation is used that is
considerably faster and allows for ARMA effects of order higher than 1 but
is only available for <code>gaussian</code> models and some of its
generalizations.</dd>

</dl>

Set up AR(p) correlation structures

ar: Set up AR(p) correlation structures

Description

Usage

Value

Arguments

See Also

Examples