fflm

Tailored for ALDEx3 where covariates are shared between massive numbers of
linear regressions where only Y is changing.

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

fflm function

<dl><dt>Y</dt>
<dd>a numeric array (N x D X S) where D is the number of taxa/genes, N
is the number of samples, and S is the number of posterior samples</dd>
<dt>X</dt>
<dd>a numeric matrix (N x P) where P is number of covariates</dd>
<dt>test</dt>
<dd>(default t.HC3), "t", t test is performed for each covariate
(fast); "t.HC0" Heteroskedasticsity-Robust Standard Errors used (HC0;
White's; slower); "t.HC3" (default) Heteroskedasticsity-Robust Standard
Errors used (HC3; unlike HC0, this includes a leverage adjustment and is
better for small sample sizes or when there are data with high leverage;
slowest). To learn more about these, loko at Long and Ervin (2000) Using
Heteroscedasticity Consistent Standard Errors in the Linear Regression
Model, The American Statistician.</dd></dl>

Arguments

Author

Freaking Fask Linear Models — fflm

<dl>

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


<dt>X</dt>
<dd>a numeric matrix (N x P) where P is number of covariates</dd>


<dt>test</dt>
<dd>(default t.HC3), "t", t test is performed for each covariate
(fast); "t.HC0" Heteroskedasticsity-Robust Standard Errors used (HC0;
White's; slower); "t.HC3" (default) Heteroskedasticsity-Robust Standard
Errors used (HC3; unlike HC0, this includes a leverage adjustment and is
better for small sample sizes or when there are data with high leverage;
slowest). To learn more about these, loko at Long and Ervin (2000) Using
Heteroscedasticity Consistent Standard Errors in the Linear Regression
Model, The American Statistician.</dd>

</dl>

fflm: Freaking Fask Linear Models

Description

Usage

Value

Arguments

Author