lnm_data

Simulates data from a Logistic Normal Multinomial 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

lnm_data function

<dl><dt>N</dt>
<dd>The number of samples in the output data.</dd>
<dt>D</dt>
<dd>The number of covariates, each of which can influence the response
composition vector (e.g., the timepoint or disease status).</dd>
<dt>K</dt>
<dd>The number of output dimensions (e.g., number of taxa).</dd></dl>

Arguments

Simulates data from a Logistic Normal Multinomial Model. — lnm_data

<dl>

<dt>N</dt>
<dd>The number of samples in the output data.</dd>


<dt>D</dt>
<dd>The number of covariates, each of which can influence the response
composition vector (e.g., the timepoint or disease status).</dd>


<dt>K</dt>
<dd>The number of output dimensions (e.g., number of taxa).</dd>

</dl>

lnm_data: Simulates data from a Logistic Normal Multinomial Model.

Description

Usage

Value

Arguments

Examples