cor_cosy

cor_cosy-class

This function is deprecated. Please see <code>cosy</code> for the new syntax.
This functions is a constructor for the <code>cor_cosy</code> class, representing
a compound symmetry structure corresponding to uniform correlation.

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

cor_cosy function

<dl><dt>formula</dt>
<dd>A one sided formula of the form <code>~ t</code>, or <code>~ t | g</code>,
specifying a time covariate <code>t</code> and, optionally, a grouping factor
<code>g</code>. A covariate for this correlation structure must be integer
valued. When a grouping factor is present in <code>formula</code>, the
correlation structure is assumed to apply only to observations within the
same grouping level; observations with different grouping levels are
assumed to be uncorrelated. Defaults to <code>~ 1</code>, which corresponds to
using the order of the observations in the data as a covariate, and no
groups.</dd></dl>

Arguments

(Deprecated) Compound Symmetry (COSY) Correlation Structure — cor_cosy

<dl>

<dt>formula</dt>
<dd>A one sided formula of the form <code>~ t</code>, or <code>~ t | g</code>,
specifying a time covariate <code>t</code> and, optionally, a grouping factor
<code>g</code>. A covariate for this correlation structure must be integer
valued. When a grouping factor is present in <code>formula</code>, the
correlation structure is assumed to apply only to observations within the
same grouping level; observations with different grouping levels are
assumed to be uncorrelated. Defaults to <code>~ 1</code>, which corresponds to
using the order of the observations in the data as a covariate, and no
groups.</dd>

</dl>

(Deprecated) Compound Symmetry (COSY) Correlation Structure

cor_cosy: (Deprecated) Compound Symmetry (COSY) Correlation Structure

Description

Usage

Value

Arguments

Examples