ord_extract_cov

Computes the covariance matrix of ordination scores, optionally using robust estimation.

Provides tools to visualize ordination results in 'R' by adding covariance-based ellipses, centroids, vectors, and confidence regions to plots created with 'ggplot2'.
The package extends the 'vegan' framework and supports Principal Component Analysis (PCA), Redundancy Analysis (RDA), and Non-metric Multidimensional Scaling (NMDS).
Ellipses can represent either group dispersion (standard deviation, SD) or centroid precision (standard error, SE), following Wang et al. (2015) <doi:10.1371/journal.pone.0118537>.
Robust estimators of covariance are implemented, including the Minimum Covariance Determinant (MCD) method of Hubert et al. (2018) <doi:10.1002/wics.1421>.
This approach reduces the influence of outliers.
barrel is particularly useful for multivariate ecological datasets, promoting reproducible, publication-quality ordination graphics with minimal effort.

Diego Barranco-Elena

barrel

Covariance-Based Ellipses and Annotation Tools for Ordination
Plots

ord_extract_cov function

<dl><dt>scores</dt>
<dd>Data frame of ordination scores.</dd>
<dt>axis1</dt>
<dd>Name of first axis column.</dd>
<dt>axis2</dt>
<dd>Name of second axis column.</dd>
<dt>weights</dt>
<dd>Optional numeric vector of weights.</dd>
<dt>method</dt>
<dd>Covariance method: "classic" or "robust".</dd></dl>

Arguments

Extract covariance matrix from ordination scores with optional robust estimation — ord_extract_cov

<dl>

<dt>scores</dt>
<dd>Data frame of ordination scores.</dd>


<dt>axis1</dt>
<dd>Name of first axis column.</dd>


<dt>axis2</dt>
<dd>Name of second axis column.</dd>


<dt>weights</dt>
<dd>Optional numeric vector of weights.</dd>


<dt>method</dt>
<dd>Covariance method: "classic" or "robust".</dd>

</dl>

ord_extract_cov: Extract covariance matrix from ordination scores with optional robust estimation

Description

Usage

Value

Arguments