PCA_plot

The function performs a principal components analysis on the given data matrix and displays a biplot using vqv.ggbiplot package of the results for each different source to help the user in the decision.

"This package quantifies the provenance of sediments in a catchment or study area. Based on a characterization of the sediment sources and the end sediment mixtures, a mixing model algorithm is applied to the sediment mixtures to estimate the relative contribution of each potential source. The package includes several graphs to help users in their data understanding, such as box plots, correlation, PCA, and LDA graphs. In addition, new developments such as the Consensus Ranking (CR), Consistent Tracer Selection (CTS), and Linear Variability Propagation (LVP) methods are included to correctly apply the fingerprinting technique and increase dataset and model understanding. A new method based on Conservative Balance (CB) method has also been included to enable the use of isotopic tracers."

Borja Latorre

fingerPro

A Comprehensive Package for Sediment Source Unmixing

Ivan Lizaga

Leticia Gaspar

Leticia Palazon

Ana Navas

Vince Q Vu

PCA_plot function

<dl><dt>data</dt>
<dd>Data frame containing source and mixtures data</dd>
<dt>components</dt>
<dd>Numeric vector containing the index of the two principal components in the chart</dd>
<dt>colors</dt>
<dd>Vector of colors to use for the groups in the plot</dd></dl>

Arguments

Principal component analysis chart — PCA_plot

<dl>

<dt>data</dt>
<dd>Data frame containing source and mixtures data</dd>


<dt>components</dt>
<dd>Numeric vector containing the index of the two principal components in the chart</dd>


<dt>colors</dt>
<dd>Vector of colors to use for the groups in the plot</dd>

</dl>

PCA_plot: Principal component analysis chart

Description

Usage

Arguments