Ordination Result #52 — Analysis Report
Contents
- ordination (#52) completed
1. Ordination
2026-09-21 02:17:00 · ordination
The first two PCA axes capture 84.5% of the total variance in the data.
| n_components | 3 |
|---|---|
| method | pca |
| n_rows | 80 |
| n_dropped | 0 |
| color_col | gradient |
explained variance
| Axis | Fraction | Cumulative |
|---|---|---|
| Axis 1 | 0.675887 | 0.675887 |
| Axis 2 | 0.169285 | 0.845172 |
| Axis 3 | 0.074281 | 0.919453 |
ordination scatter
scree
loadings
Method & Provenance
- Method
- Ordination — PCA
- Engine
- Python · scikit-learn 1.8.0
- Input dataset
- gallery — example_ordination.csv
- Started
- 2026-09-21 02:17:00.624418
- Permanent link
- https://cat-g-omxpert.wishartlab.com/results/by-token/8a2cf4821c2941d4856886bb933d8044
| Parameter | Value |
|---|---|
| ordination_method | pca |
| method | pca |
| feature_cols | gradient, f1, f2, f3, f4, f5, f6, f7, f8 |
| n_components | 3 |
| color_col | gradient |
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)