LogisticNormal

dlogistic_normal

rlogistic_normal

Density function and random generation for the (multivariate) logistic normal
distribution with latent mean vector <code>mu</code> and covariance matrix <code>Sigma</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 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

Aki Vehtari

Mattan S. Ben-Shachar

Hayden Rabel

Simon C. Mills

Stephen Wild

Ven Popov

LogisticNormal function

<dl><dt>x</dt>
<dd>Vector or matrix of quantiles. If <code>x</code> is a matrix,
each row is taken to be a quantile.</dd>
<dt>mu</dt>
<dd>Mean vector with length equal to the number of dimensions.</dd>
<dt>Sigma</dt>
<dd>Covariance matrix.</dd>
<dt>refcat</dt>
<dd>A single integer indicating the reference category.
Defaults to <code>1</code>.</dd>
<dt>log</dt>
<dd>Logical; If <code>TRUE</code>, values are returned on the log scale.</dd>
<dt>check</dt>
<dd>Logical; Indicates whether several input checks
should be performed. Defaults to <code>FALSE</code> to improve
efficiency.</dd>
<dt>n</dt>
<dd>Number of draws to sample from the distribution.</dd></dl>

Arguments

The (Multivariate) Logistic Normal Distribution — LogisticNormal

<dl>

<dt>x</dt>
<dd>Vector or matrix of quantiles. If <code>x</code> is a matrix,
each row is taken to be a quantile.</dd>


<dt>mu</dt>
<dd>Mean vector with length equal to the number of dimensions.</dd>


<dt>Sigma</dt>
<dd>Covariance matrix.</dd>


<dt>refcat</dt>
<dd>A single integer indicating the reference category.
Defaults to <code>1</code>.</dd>


<dt>log</dt>
<dd>Logical; If <code>TRUE</code>, values are returned on the log scale.</dd>


<dt>check</dt>
<dd>Logical; Indicates whether several input checks
should be performed. Defaults to <code>FALSE</code> to improve
efficiency.</dd>


<dt>n</dt>
<dd>Number of draws to sample from the distribution.</dd>

</dl>

The (Multivariate) Logistic Normal Distribution

LogisticNormal: The (Multivariate) Logistic Normal Distribution

Description

Usage

Arguments