coef.lmCoDa

These functions work as in the usual lm object.
They additionally offer the possibility use the <code>space</code> argument
which transforms them into directly into clr space or in the simplex.

Provides methods for interpreting CoDa (Compositional Data) regression models along the lines of "Pairwise share ratio interpretations of compositional regression models" (Dargel and Thomas-Agnan 2024) <doi:10.1016/j.csda.2024.107945>.
The new methods include variation scenarios, elasticities, elasticity differences and share ratio elasticities.
These tools are independent of log-ratio transformations and allow an interpretation in the original space of shares.
'CoDaImpact' is designed to be used with the 'compositions' package and its ecosystem.

Lukas Dargel

CoDaImpact

Interpreting CoDa Regression Models

Christine Thomas-Agnan

Rodrigue Nasr

Sijia Pan

Iban Rendo Barreiro

Shuyao Li

coef.lmCoDa function

<dl><dt>object</dt>
<dd>class "lmCoDa"</dd>
<dt>space</dt>
<dd>a character indicating in which space the prediction should
be returned. Supported are the options <code>c("clr", "simplex")</code>.</dd>
<dt>split</dt>
<dd>logical, if <code>TRUE</code> the coefficients are reported as a list instead
of a matrix, where list structure reflects the explanatory variables of the model</dd>
<dt>...</dt>
<dd>not used</dd></dl>

Arguments

Author

Predictions, fitted values, residuals, and coefficients in CoDa models — coef.lmCoDa

<dl>

<dt>object</dt>
<dd>class "lmCoDa"</dd>


<dt>space</dt>
<dd>a character indicating in which space the prediction should
be returned. Supported are the options <code>c("clr", "simplex")</code>.</dd>


<dt>split</dt>
<dd>logical, if <code>TRUE</code> the coefficients are reported as a list instead
of a matrix, where list structure reflects the explanatory variables of the model</dd>


<dt>...</dt>
<dd>not used</dd>

</dl>

coef.lmCoDa: Predictions, fitted values, residuals, and coefficients in CoDa models

Description

Usage

Value

Arguments

Author