Computation of a single isometric logratio (ILR)

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

ILR function

<dl> <dt>data</dt>
<dd>A compositional data frame or matrix</dd> <dt>numer</dt>
<dd>Vector of parts in the numerator</dd> <dt>denom</dt>
<dd>Vector of parts in the denominator</dd> <dt>weight</dt>
<dd>Logical indicating if a varying weight is returned (default:<code>TRUE</code>). If <code>FALSE</code>, a weight based on equally-weighted parts is returned. Alternatively a positive weight can be specified.</dd></dl>

Arguments

Author

Isometric logratio — ILR

<dl>

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

 <dt>numer</dt>
<dd>Vector of parts in the numerator</dd>

 <dt>denom</dt>
<dd>Vector of parts in the denominator</dd>

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

</dl>

ILR: Isometric logratio

Description

Usage

Value

Arguments

Author

Details

References

See Also

Examples