get.n

Returns the number of grains that need to be analysed to decrease
the likelihood of missing any fraction greater than a given size
below a given level.

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

get.n function

<dl><dt>p</dt>
<dd>the probability that all n grains in the sample have missed
at least one fraction of size <code>f</code></dd>
<dt>f</dt>
<dd>the size of the smallest resolvable fraction (0&lt;f&lt;1)</dd>
<dt>n, </dt>
<dd>the number of grains in the sample</dd></dl>

Arguments

Calculate the number of grains required to achieve a desired level of sampling resolution — get.n

<dl>

<dt>p</dt>
<dd>the probability that all n grains in the sample have missed
at least one fraction of size <code>f</code></dd>


<dt>f</dt>
<dd>the size of the smallest resolvable fraction (0&lt;f&lt;1)</dd>


<dt>n, </dt>
<dd>the number of grains in the sample</dd>

</dl>

get.n: Calculate the number of grains required to achieve a desired level of sampling resolution

Description

Usage

Value

Arguments

References

Examples