Implement principal component analysis for dimension reduction

A framework used to detect and handle outliers during data analysis workflows. Outlier detection is a statistical concept with applications in data analysis workflows, highlighting records that are suspiciously high or low. Outlier detection in distribution models was initiated by Chapman (1991) (available at <https://www.researchgate.net/publication/332537800_Quality_control_and_validation_of_point-sourced_environmental_resource_data>), who developed the reverse jackknifing method. The concept was further developed and incorporated into different R packages, including 'flexsdm' (Velazco et al., 2022, <doi:10.1111/2041-210X.13874>) and 'biogeo' (Robertson et al., 2016 <doi:10.1111/ecog.02118>). We compiled various outlier detection methods obtained from the literature, including those elaborated in Dastjerdy et al. (2023) <doi:10.3390/geotechnics3020022> and Liu et al. (2008) <doi:10.1109/ICDM.2008.17>. In this package, we introduced the ensembling aspect, where multiple outlier detection methods are used to flag the record as either an absolute outlier. The concept can also be applied in general data analysis, as well as during the development of species distribution models.

Anthony Basooma

specleanr

Detecting Environmental Outliers in Data Analysis Pipelines

Thomas Hein

Astrid Schmidt-Kloiber

Merret Buurman

Sami Domisch

Martin Tschikof

Florian Borgwardt

pca function

<dl><dt>data</dt>
<dd>Environmental dataframe</dd>
<dt>npc</dt>
<dd>Number of principal components to be retained. Default is 2</dd>
<dt>q</dt>
<dd>To show the cumulative total variance explained by the <code>npc</code> selected.</dd></dl>

Arguments

Implement principal component analysis for dimension reduction — pca

<dl>

<dt>data</dt>
<dd>Environmental dataframe</dd>


<dt>npc</dt>
<dd>Number of principal components to be retained. Default is 2</dd>


<dt>q</dt>
<dd>To show the cumulative total variance explained by the <code>npc</code> selected.</dd>

</dl>

pca: Implement principal component analysis for dimension reduction

Description

Usage

Arguments