prepare_newdata

This is a helper function to form the design matrix for an LNM regression
starting from a fitted model's formula object. It is an analog of
model.matrix for the multiresponse setting.

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

prepare_newdata 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>newdata</dt>
<dd>A data.frame containing variables in the formula definition of
the fit, but which hasn't been converted into the matrix format needed for
internal prediction.</dd></dl>

Arguments

Design Matrix for a Model — prepare_newdata

<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>newdata</dt>
<dd>A data.frame containing variables in the formula definition of
the fit, but which hasn't been converted into the matrix format needed for
internal prediction.</dd>

</dl>

prepare_newdata: Design Matrix for a Model

Description

Usage

Value

Arguments

Examples