Ordination Result #49 — Analysis Report
Contents
- ordination (#49) completed
1. Ordination
2026-09-21 02:12:39 · ordination
The first two PCA axes capture 31.4% of the total variance in the data.
| n_components | 3 |
|---|---|
| method | pca |
| n_rows | 59 |
| n_dropped | 341 |
| color_col | RANCH |
explained variance
| Axis | Fraction | Cumulative |
|---|---|---|
| Axis 1 | 0.189909 | 0.189909 |
| Axis 2 | 0.123726 | 0.313636 |
| Axis 3 | 0.10144 | 0.415075 |
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 — cat_g_field_data_plots.csv
- Started
- 2026-09-21 02:12:39.328469
- Permanent link
- https://cat-g-omxpert.wishartlab.com/results/by-token/ba8cd7e8395a46ef8ddbbc2497cfc4f5
| Parameter | Value |
|---|---|
| ordination_method | pca |
| method | pca |
| feature_cols | LAT, LONG, ENV_SOILNH4, ENV_SOILNO3, ENV_SOILP, ENV_SOILOC, ENV_SOILTN, ENV_SOILTC, ENV_PH, ENV_SOILCLAY, ENV_SOILSILT, ENV_SOILSAND, ENV_SOILMOIS, RC_SHOOTMASS, RC_WOODMASS, RC_LITTERMASS, RC_LITTERWMASS, PC_SHOOTMASS, PC_WOODMASS, PC_LITTERMASS, PC_LITTERWMASS, PC_ROOTMASS, PC_HEIGHT, GH_COUNT, ENV_AIRTEMP, COVER_RICHNESS_JUN, COVER_TOTAL_JUN, COVER_SHANNON_JUN, COVER_RICHNESS_JUL, COVER_TOTAL_JUL, COVER_SHANNON_JUL, COVER_RICHNESS_AUG, COVER_TOTAL_AUG, COVER_SHANNON_AUG, PASTURE_TOTALAREA, SUBPASTURE_TOTALAREA, PASTURE_FORESTAREA, PASTURE_WETLANDAREA, SUBPASTURE_FORESTAREA, SUBPASTURE_WETLANDAREA |
| n_components | 3 |
| color_col | RANCH |
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)