Ordination Result #50 — Analysis Report
Contents
- ordination (#50) completed
1. Ordination
2026-09-21 02:14:27 · ordination
The first two PCA axes capture 65.9% of the total variance in the data.
| n_components | 3 |
|---|---|
| method | pca |
| n_rows | 66 |
| n_dropped | 284 |
explained variance
| Axis | Fraction | Cumulative |
|---|---|---|
| Axis 1 | 0.541212 | 0.541212 |
| Axis 2 | 0.11776 | 0.658972 |
| Axis 3 | 0.090445 | 0.749417 |
ordination scatter
scree
loadings
Warnings
- Excluded identifier-like numeric column(s): PLOT. These look like bookkeeping fields rather than measurements. Select feature columns explicitly if you intended to include them.
Method & Provenance
- Method
- Ordination — PCA
- Engine
- Python · scikit-learn 1.8.0
- Input dataset
- gallery — catg_example_03_soil_nitrogen_availability.csv
- Started
- 2026-09-21 02:14:27.741981
- Permanent link
- https://cat-g-omxpert.wishartlab.com/results/by-token/aa8e993ca24f4dfb9083d77a2739eb34
| Parameter | Value |
|---|---|
| ordination_method | pca |
| method | pca |
| feature_cols | ENV_SOILOC, ENV_SOILTC, ENV_SOILTN, ENV_PH, ENV_SOILCLAY, ENV_SOILSILT, ENV_SOILSAND, ENV_SOILMOIS, ENV_SOILP, PC_SHOOTMASS, PC_LITTERMASS, COVER_TOTAL_JUL, COVER_RICHNESS_JUL, PASTURE_TOTALAREA, LAT, LONG, ENV_SOILNH4, ENV_SOILNO3, SOIL_EC_uS_cm, SOIL_CO2_mg_per_g |
| n_components | 3 |
Command history
# python from sklearn.decomposition import PCA # python from sklearn.preprocessing import StandardScaler # python X_scaled = StandardScaler().fit_transform(X) # python coords = PCA(n_components=3).fit_transform(X_scaled)