R codes for Sedimentary OM analyses
Description
This R script contains the full workflow and analysis used in the study titled "Biogeochemical Characterization and Source Prediction of Organic Matter in a Land to Ocean Sediment Transect: A Multivariate Machine Learning Approach".
Files
Steps to reproduce
The code includes: Principal Component Analysis (PCA): Dimensionality reduction, correlation analysis, and visualization using scree plots, scatterplots, and biplots. Partial Least Squares Regression (PLSR): Model fitting, evaluation, and Variable Importance in Projection (VIP) score calculations. Random Forest Analysis: Training a Random Forest model, predicting outcomes, and extracting variable importance measures (Mean Decrease Gini). Data Visualization: Generating heatmaps, scatterplots, and other visualizations to illustrate the results.
Institutions
- Concordia University