Principal Component Analysis

Knowledge graph centered on Principal Component Analysis with 72 nodes and 166 connections. Top connected: Gothic, Plant functional trait change across a warming tun, Marmota, Yellow-bellied Marmot, Barbey's Larkspur.

Method synopsis

Statistical analysis of stream chemistry data transformed to compositional units using PCA to evaluate spatial and temporal trends while removing effects of dilution.

Synthesized from method descriptions across 12 papers using this protocol.

Procedure from a recent peer-reviewed implementation

Steps below were extracted from the most recent peer-reviewed implementation of this protocol in the corpus — Hidden features: How subsurface and landscape heterogeneity govern hydrologic connectivity and stream chemistry in a montane watershed (2025), Hydrological Processes. Implementations in other papers (listed below) may differ.

1. Perform compositional PCA and end member mixing analysis
   Principal components analysis (PCA) was used to evaluate spatial and temporal trends in geochemical data. Data were transformed into compositional units by converting all sample concentrations to milliequivalents and then dividing each solute concentration by the sum of all solute concentrations for a given sample. PCA-based end member mixing analysis (EMMA) was used to quantify end member contribution to Coal Creek samples. Stream samples not constrained by end members were removed from analysis.
   Quantities: Analysis conducted on all stream chemistry data with three end members identifiedDuration: Data analysis phase following laboratory resultsConditions: Statistical analysis using R software
   Equipment: R statistical software, robCompositions package

Typical Equipment

• statistical software

Output Measurements

• factor loadings
• rotated factor scores

Papers Using This Protocol (12)

Species Studied With This Protocol

Places Where Used