get_F_stat

This function calculates an F statistic for a test of null hypothesis LB = 0
(against an unrestricted alternative) where B is a
vector of p fixed effects returned by betta() or betta_random() and L is an
m x p matrix with linearly independent rows.

Understanding the drivers of microbial diversity is an important frontier of microbial ecology, and investigating the diversity of samples from microbial ecosystems is a common step in any microbiome analysis. 'breakaway' is the premier package for statistical analysis of microbial diversity. 'breakaway' implements the latest and greatest estimates of species richness, described in Willis and Bunge (2015) <doi:10.1111/biom.12332>, Willis et al. (2017) <doi:10.1111/rssc.12206>, and Willis (2016) <arXiv:1604.02598>, as well as the most commonly used estimates, including the objective Bayes approach described in Barger and Bunge (2010) <doi:10.1214/10-BA527>.

Amy D Willis

breakaway

Species Richness Estimation and Modeling

Bryan D Martin

Pauline Trinh

Sarah Teichman

David Clausen

Kathryn Barger

John Bunge

get_F_stat function

<dl><dt>fitted_betta</dt>
<dd>A fitted betta object -- i.e., the output of either betta() or
betta_random() -- containing fixed effect estimates of interest.</dd>
<dt>L</dt>
<dd>An m x p matrix defining the null LB = 0. L must have full row rank.</dd></dl>

Arguments

Calculate F statistic under null hypothesis LB = 0 using output from betta()
or betta_random() — get_F_stat

<dl>

<dt>fitted_betta</dt>
<dd>A fitted betta object -- i.e., the output of either betta() or
betta_random() -- containing fixed effect estimates of interest.</dd>


<dt>L</dt>
<dd>An m x p matrix defining the null LB = 0. L must have full row rank.</dd>

</dl>

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get_F_stat: Calculate F statistic under null hypothesis LB = 0 using output from betta() or betta_random()

Description

Usage

Value

Arguments