KS.diss

KS.diss.default

KS.diss.distributional

Returns the Kolmogorov-Smirnov dissimilarity between two samples

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

KS.diss function

<dl><dt>x</dt>
<dd>the first sample as a vector</dd>
<dt>...</dt>
<dd>optional arguments</dd>
<dt>y</dt>
<dd>the second sample as a vector</dd></dl>

Arguments

Kolmogorov-Smirnov dissimilarity — KS.diss

<dl>

<dt>x</dt>
<dd>the first sample as a vector</dd>


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


<dt>y</dt>
<dd>the second sample as a vector</dd>

</dl>

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KS.diss: Kolmogorov-Smirnov dissimilarity

Description

Usage

Value

Arguments

Examples