diss

diss.distributional

diss.compositional

diss.counts

diss.varietal

Calculate the dissimilarity matrix between two datasets of class
<code>distributional</code> or <code>compositional</code> using the Kolmogorov-Smirnov,
Sircombe-Hazelton, Aitchison or Bray-Curtis distance

Bundles a number of established statistical methods to facilitate the visual interpretation of large datasets in sedimentary geology. Includes functionality for adaptive kernel density estimation, principal component analysis, correspondence analysis, multidimensional scaling, generalised procrustes analysis and individual differences scaling using a variety of dissimilarity measures. Univariate provenance proxies, such as single-grain ages or (isotopic) compositions are compared with the Kolmogorov-Smirnov, Kuiper, Wasserstein-2 or Sircombe-Hazelton L2 distances. Categorical provenance proxies such as chemical compositions are compared with the Aitchison and Bray-Curtis distances,and count data with the chi-square distance. Varietal data can either be converted to one or more distributional datasets, or directly compared using the multivariate Wasserstein distance. Also included are tools to plot compositional and count data on ternary diagrams and point-counting data on radial plots, to calculate the sample size required for specified levels of statistical precision, and to assess the effects of hydraulic sorting on detrital compositions. Includes an intuitive query-based user interface for users who are not proficient in R.

Pieter Vermeesch

provenance

Statistical Toolbox for Sedimentary Provenance Analysis

diss function

<dl><dt>x</dt>
<dd>an object of class <code>distributional</code>,
<code>compositional</code> or <code>counts</code></dd>
<dt>method</dt>
<dd>if <code>x</code> has class <code>distributional</code>: either
 <code>"KS"</code>, <code>"Wasserstein"</code>, <code>"Kuiper"</code> or
 <code>"SH"</code>;
if <code>x</code> has class <code>compositional</code>: either
<code>"aitchison"</code> or <code>"bray"</code>;
if <code>x</code> has class <code>counts</code>: either <code>"chisq"</code> or
<code>"bray"</code>;
if <code>x</code> has class <code>varietal</code>: either <code>"KS"</code>,
<code>"W2_1D"</code> or <code>"W2"</code>.</dd>
<dt>...</dt>
<dd>optional arguments</dd>
<dt>log</dt>
<dd>logical. If <code>TRUE</code>, subjects the distributional
data to a logarithmic transformation before calculating the
Wasserstein distance.</dd>
<dt>verbose</dt>
<dd>logical. If <code>TRUE</code>, gives progress updates
during the construction of the dissimilarity matrix.</dd></dl>

Arguments

Calculate the dissimilarity matrix between two datasets of class
distributional, compositional, counts or
varietal — diss

<dl>

<dt>x</dt>
<dd>an object of class <code>distributional</code>,
<code>compositional</code> or <code>counts</code></dd>


<dt>method</dt>
<dd>if <code>x</code> has class <code>distributional</code>: either
 <code>"KS"</code>, <code>"Wasserstein"</code>, <code>"Kuiper"</code> or
 <code>"SH"</code>;
if <code>x</code> has class <code>compositional</code>: either
<code>"aitchison"</code> or <code>"bray"</code>;
if <code>x</code> has class <code>counts</code>: either <code>"chisq"</code> or
<code>"bray"</code>;
if <code>x</code> has class <code>varietal</code>: either <code>"KS"</code>,
<code>"W2_1D"</code> or <code>"W2"</code>.</dd>


<dt>...</dt>
<dd>optional arguments</dd>


<dt>log</dt>
<dd>logical. If <code>TRUE</code>, subjects the distributional
data to a logarithmic transformation before calculating the
Wasserstein distance.</dd>


<dt>verbose</dt>
<dd>logical. If <code>TRUE</code>, gives progress updates
during the construction of the dissimilarity matrix.</dd>

</dl>

Calculate the dissimilarity matrix between two datasets of class
<code>distributional</code>, <code>compositional</code>, <code>counts</code> or
<code>varietal</code>

diss: Calculate the dissimilarity matrix between two datasets of class `distributional`, `compositional`, `counts` or `varietal`

Description

Usage

Value

Arguments

Details

See Also

Examples