Result Detail – Ordination

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Notebook

Method: PCA
Components: 3
Rows ordinated: 66 (284 rows dropped for missing values)
Feature columns: 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
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.

Ordination scores (axes 1–2) Download PNG

Scree plot Download PNG

Top feature loadings Download PNG

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Method & Provenance

Method: Ordination — PCA
Method key: pca (result type: ordination, result #50)
Engine: Python · scikit-learn v1.8.0
Dataset(s): #698 gallery — catg_example_03_soil_nitrogen_availability.csv
Status: completed
Permanent link: https://cat-g-omxpert.wishartlab.com/results/by-token/aa8e993ca24f4dfb9083d77a2739eb34 Bookmark or share this URL — it does not depend on your session.
Created: 2026-09-21 02:14:27.741981

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

Reconstructed from the parameters stored with this result — represents the Python/R calls the analysis service made.

from sklearn.decomposition import PCA

from sklearn.preprocessing import StandardScaler

X_scaled = StandardScaler().fit_transform(X)

coords = PCA(n_components=3).fit_transform(X_scaled)