Feature selection, Random Forest, and SEM workflow for simulated stand dataset
<p dir="ltr">This dataset and R script provide the workflow for analyzing stand structure and productivity of Chinese fir (<i>Cunninghamia lanceolata</i>) stands using simulated data. The dataset includes key stand, site, and climate variables. The R script demonstrates t...
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2025
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| Summary: | <p dir="ltr">This dataset and R script provide the workflow for analyzing stand structure and productivity of Chinese fir (<i>Cunninghamia lanceolata</i>) stands using simulated data. The dataset includes key stand, site, and climate variables. The R script demonstrates the following steps:</p><ol><li>Feature selection with the Boruta algorithm</li><li>Random Forest modeling with cross-validation and stepwise variable elimination</li><li>Multicollinearity diagnostics using VIF</li><li>Structural Equation Modeling (SEM) and effect decomposition with <code>piecewiseSEM</code> and <code>semEff</code><b>Notes</b></li><li>The dataset is simulated for reproducibility and does not include raw inventory data.</li><li>Users may adapt the script to their own datasets for similar analyses in forestry and ecology.</li></ol><p></p> |
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