Result Detail – Ordination Help

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Result Information
Result ID
49
Type
Ordination
Status
completed
Dataset ID
690
Created
2026-09-21 02:12:39.328469
Ordination Result – PCA Help
Method
PCA
Components
3
Rows ordinated
59 (341 rows dropped for missing values)
Feature columns
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
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.
Explained variance Help
AxisFractionCumulative
Axis 1 0.1899 0.1899
Axis 2 0.1237 0.3136
Axis 3 0.1014 0.4151
Ordination scatter Help
Figures
Ordination scores (axes 1–2) Download PNG
Ordination scores (axes 1–2)
Scree plot Download PNG
Scree plot
Top feature loadings Download PNG
Top feature loadings
Pathway Analysis (same dataset) Help
Run Pathway Analysis

Method & Provenance

Method
Ordination — PCA
Method key
pca (result type: ordination, result #49)
Engine
Python · scikit-learn v1.8.0
Dataset(s)
#690 gallery — cat_g_field_data_plots.csv
Status
completed
Permanent link
https://cat-g-omxpert.wishartlab.com/results/by-token/ba8cd7e8395a46ef8ddbbc2497cfc4f5 Bookmark or share this URL — it does not depend on your session.
Created
2026-09-21 02:12:39.328469

Parameters used

ParameterValue
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

Execution trace

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)