beta_samples

Return multiple samples for the beta parameter from the VB posterior mean.
This is used to simulate new compositions when using <code>sample</code> on an lnm
model.

Logistic-normal Multinomial (LNM) models are common in problems with multivariate count data. This package gives a simple implementation with a 30 line 'Stan' script. This lightweight implementation makes it an easy starting point for other projects, in particular for downstream tasks that require analysis of "compositional" data. It can be applied whenever a multinomial probability parameter is thought to depend linearly on inputs in a transformed, log ratio space. Additional utilities make it easy to inspect, create predictions, and draw samples using the fitted models. More about the LNM can be found in Xia et al. (2013) "A Logistic Normal Multinomial Regression Model for Microbiome Compositional Data Analysis" <doi:10.1111/biom.12079> and Sankaran and Holmes (2023) "Generative Models: An Interdisciplinary Perspective" <doi:10.1146/annurev-statistics-033121-110134>.

Kris Sankaran

miniLNM

Miniature Logistic-Normal Multinomial Models

beta_samples function

<dl><dt>fit</dt>
<dd>An object of class <code>lnm</code> whose estimate slot contains the 'rstan'
fitted logistic normal multinomial model.</dd>
<dt>size</dt>
<dd>The number of draws from the posterior to return.</dd></dl>

Arguments

LNM Posterior Samples — beta_samples

<dl>

<dt>fit</dt>
<dd>An object of class <code>lnm</code> whose estimate slot contains the 'rstan'
fitted logistic normal multinomial model.</dd>


<dt>size</dt>
<dd>The number of draws from the posterior to return.</dd>

</dl>

beta_samples: LNM Posterior Samples

Description

Usage

Value

Arguments

Examples