sr.mem

Implementation of SR-MEM: scale-reliant mixed effects models.

Provides scalable generalized linear and mixed effects models tailored for sequence count data analysis (e.g., analysis of 16S or RNA-seq data). Uses Dirichlet-multinomial sampling to quantify uncertainty in relative abundance or relative expression conditioned on observed count data.
Implements scale models as a generalization of normalizations which account for uncertainty in scale (e.g., total abundances) as described in Nixon et al. (2025) <doi:10.1186/s13059-025-03609-3> and McGovern et al. (2025) <doi:10.1101/2025.08.05.668734>.

Justin Silverman

ALDEx3

Linear Models for Sequence Count Data

Greg Gloor

Kyle McGovern

sr.mem function

<dl><dt>logW</dt>
<dd>a numeric array (N x D X S) where D is the number of taxa,
N is the number of samples, and S is the number of posterior samples</dd>
<dt>formula</dt>
<dd>an lme4 formula with fixed and random effects for lmer</dd>
<dt>data</dt>
<dd>A data.frame with the random and fixed effects items in formula</dd>
<dt>n.cores</dt>
<dd>The number of cores for parallelization. If n.cores=1, no
parallelization is used</dd>
<dt>method</dt>
<dd>The mem method to use: either nlme or lme4</dd>
<dt>mem.args</dt>
<dd>Additional arguments including random or correlation</dd></dl>

Arguments

Author

Implementation of SR-MEM: scale-reliant mixed effects models. — sr.mem

<dl>

<dt>logW</dt>
<dd>a numeric array (N x D X S) where D is the number of taxa,
N is the number of samples, and S is the number of posterior samples</dd>


<dt>formula</dt>
<dd>an lme4 formula with fixed and random effects for lmer</dd>


<dt>data</dt>
<dd>A data.frame with the random and fixed effects items in formula</dd>


<dt>n.cores</dt>
<dd>The number of cores for parallelization. If n.cores=1, no
parallelization is used</dd>


<dt>method</dt>
<dd>The mem method to use: either nlme or lme4</dd>


<dt>mem.args</dt>
<dd>Additional arguments including random or correlation</dd>

</dl>

sr.mem: Implementation of SR-MEM: scale-reliant mixed effects models.

Description

Usage

Value

Arguments

Author