Computation of the set of pivot logratios(PLRs) based on the specified ordering of parts

logratio

Univariate and multivariate methods for compositional data
analysis, based on logratios. The package implements the approach in the
book Compositional Data Analysis in Practice by Michael Greenacre (2018),
where accent is given to simple pairwise logratios. Selection can be made
of logratios that account for a maximum percentage of logratio variance.
Various multivariate analyses of logratios are included in the package.

Michael Greenacre

easyCODA

Compositional Data Analysis in Practice

PLR function

<dl> <dt>data</dt>
<dd>A compositional data frame or matrix</dd> <dt>ordering</dt>
<dd>The ordering of the parts to be used in the PLRs (by default, the original ordering of the columns)</dd> <dt>weight</dt>
<dd>Logical indicating if varying weights are returned (default:<code>TRUE</code>). If <code>FALSE</code>, weights based on equally-weighted parts are returned. Alternatively a set of positive weights can be specified.</dd></dl>

Arguments

Author

Pivot logratios — PLR

<dl>

 <dt>data</dt>
<dd>A compositional data frame or matrix</dd>

 <dt>ordering</dt>
<dd>The ordering of the parts to be used in the PLRs (by default, the original ordering of the columns)</dd>

 <dt>weight</dt>
<dd>Logical indicating if varying weights are returned (default:<code>TRUE</code>). If <code>FALSE</code>, weights based on equally-weighted parts are returned. Alternatively a set of positive weights can be specified.</dd>

</dl>

PLR: Pivot logratios

Description

Usage

Value

Arguments

Author

Details

References

See Also

Examples