DFA_test

Performs a stepwise forward variable selection using the Wilk's Lambda criterion to identify the most discriminant tracers in a dataset.

"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

DFA_test function

<dl><dt>data</dt>
<dd>A data frame containing the characteristics of sediment sources and mixtures.</dd>
<dt>niveau</dt>
<dd>A numeric value specifying the significance level for the approximate F-test decision.</dd></dl>

Arguments

Discriminant Function Analysis (DFA) Test — DFA_test

<dl>

<dt>data</dt>
<dd>A data frame containing the characteristics of sediment sources and mixtures.</dd>


<dt>niveau</dt>
<dd>A numeric value specifying the significance level for the approximate F-test decision.</dd>

</dl>

DFA_test: Discriminant Function Analysis (DFA) Test

Description

Usage

Value

Arguments