naiveVariation

Naive (empirical) estimates of proportionality metrics using only the
observed counts.

Calculates metrics of proportionality using the logit-normal multinomial model.  It can also provide empirical and plugin estimates of these metrics.

Kevin McGregor

countprop

Calculate Model-Based Metrics of Proportionality on Count-Based
Compositional Data

Nneka Okaeme

naiveVariation function

<dl><dt>counts</dt>
<dd>Matrix of counts; samples are rows and features are columns</dd>
<dt>pseudo.count</dt>
<dd>Positive count to be added to all elements of count matrix.</dd>
<dt>type</dt>
<dd>Type of variation metric to be calculated: <code>standard</code>, <code>phi</code>,
<code>phis</code> (a symmetric version of <code>phi</code>), <code>rho</code>, or <code>logp</code> (the variance-covariance matrix of log-transformed proportions)</dd>
<dt>impute.zeros</dt>
<dd>If TRUE, then <code>cmultRepl()</code> from the <code>zCompositions</code> package is used to impute zero values in the counts matrix.</dd>
<dt>...</dt>
<dd>Optional arguments passed to zero-imputation function <code>cmultRepl()</code></dd></dl>

Arguments

Naive (Empirical) Variation — naiveVariation

<dl>

<dt>counts</dt>
<dd>Matrix of counts; samples are rows and features are columns</dd>


<dt>pseudo.count</dt>
<dd>Positive count to be added to all elements of count matrix.</dd>


<dt>type</dt>
<dd>Type of variation metric to be calculated: <code>standard</code>, <code>phi</code>,
<code>phis</code> (a symmetric version of <code>phi</code>), <code>rho</code>, or <code>logp</code> (the variance-covariance matrix of log-transformed proportions)</dd>


<dt>impute.zeros</dt>
<dd>If TRUE, then <code>cmultRepl()</code> from the <code>zCompositions</code> package is used to impute zero values in the counts matrix.</dd>


<dt>...</dt>
<dd>Optional arguments passed to zero-imputation function <code>cmultRepl()</code></dd>

</dl>

naiveVariation: Naive (Empirical) Variation

Description

Usage

Value

Arguments

Examples