Result Detail – Univariate Screening Help

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Result Information

Result ID: 163
Type: Univariate Screening
Status: completed
Dataset ID: 694
Created: 2026-09-21 02:13:46.233864

Pre-Modeling Statistical Screening

Method: univariate screening
Features Selected: 29
Target Column: PC_SHOOTMASS regression
FDR Threshold: 0.05

Univariate Statistics (Pearson, Spearman, ANOVA)

Feature	Pearson r	Pearson p	Pearson FDR	Spearman ρ	Spearman FDR	ANOVA F	ANOVA FDR	Significant?
PLOT	-0.3602	0.0000	0.0000	-0.2762	0.0000	58.3020	0.0000	✓ Yes
ENV_SOILNH4	0.0825	0.1025	0.1209	0.1837	0.0003	2.6781	0.1209	✓ Yes
ENV_SOILNO3	0.0367	0.4677	0.5144	0.0909	0.0791	0.5285	0.5144	✗ No
ENV_SOILP	0.2028	0.0001	0.0001	0.1248	0.0157	16.7772	0.0001	✓ Yes
ENV_SOILOC	0.4835	0.0000	0.0000	0.4601	0.0000	119.2943	0.0000	✓ Yes
ENV_SOILTN	0.4808	0.0000	0.0000	0.4570	0.0000	117.5512	0.0000	✓ Yes
ENV_SOILTC	0.4834	0.0000	0.0000	0.4595	0.0000	119.2008	0.0000	✓ Yes
ENV_PH	-0.0157	0.7558	0.7558	-0.0387	0.4727	0.0968	0.7558	✗ No
ENV_SOILCLAY	0.3548	0.0000	0.0000	0.3541	0.0000	56.2918	0.0000	✓ Yes
ENV_SOILSILT	0.4313	0.0000	0.0000	0.4004	0.0000	89.3405	0.0000	✓ Yes
ENV_SOILSAND	-0.4132	0.0000	0.0000	-0.3831	0.0000	80.5038	0.0000	✓ Yes
ENV_SOILMOIS	0.4618	0.0000	0.0000	0.4453	0.0000	105.9672	0.0000	✓ Yes
COVER_RICHNESS_JUN	-0.1236	0.0142	0.0180	-0.2224	0.0000	6.0689	0.0180	✓ Yes
COVER_TOTAL_JUN	0.3246	0.0000	0.0000	0.3628	0.0000	46.0578	0.0000	✓ Yes
COVER_SHANNON_JUN	-0.1289	0.0105	0.0139	-0.2211	0.0000	6.6112	0.0139	✓ Yes
COVER_RICHNESS_JUL	-0.0952	0.0593	0.0724	-0.1762	0.0005	3.5783	0.0724	✓ Yes
COVER_TOTAL_JUL	0.3040	0.0000	0.0000	0.4258	0.0000	39.8262	0.0000	✓ Yes
COVER_SHANNON_JUL	-0.0290	0.5667	0.6032	-0.1081	0.0366	0.3289	0.6032	✓ Yes
COVER_RICHNESS_AUG	-0.1788	0.0004	0.0005	-0.2615	0.0000	12.9125	0.0005	✓ Yes
COVER_TOTAL_AUG	0.3803	0.0000	0.0000	0.4278	0.0000	66.1066	0.0000	✓ Yes
COVER_SHANNON_AUG	-0.1623	0.0012	0.0017	-0.2305	0.0000	10.5809	0.0017	✓ Yes
LAT	0.3925	0.0000	0.0000	0.2620	0.0000	71.1924	0.0000	✓ Yes
LONG	0.3863	0.0000	0.0000	0.2380	0.0000	68.5667	0.0000	✓ Yes
PASTURE_TOTALAREA	-0.3975	0.0000	0.0000	-0.3756	0.0000	73.3916	0.0000	✓ Yes
SUBPASTURE_TOTALAREA	-0.3140	0.0000	0.0000	-0.3013	0.0000	42.7582	0.0000	✓ Yes
PASTURE_FORESTAREA	0.2970	0.0000	0.0000	0.2509	0.0000	37.8304	0.0000	✓ Yes
PASTURE_WETLANDAREA	-0.0718	0.1552	0.1766	0.0063	0.9004	2.0286	0.1766	✗ No
ENV_AIRTEMP	0.0248	0.6245	0.6440	-0.0134	0.8151	0.2400	0.6440	✗ No
GH_COUNT	-0.3009	0.0000	0.0000	-0.2971	0.0000	38.9156	0.0000	✓ Yes
PC_HEIGHT	0.4491	0.0000	0.0000	0.4438	0.0000	98.8059	0.0000	✓ Yes
ENV_PROBEMOIS_AUG	0.5002	0.0000	0.0000	0.4429	0.0000	130.4838	0.0000	✓ Yes
ENV_PROBEMOIS_JUL	0.3386	0.0000	0.0000	0.2924	0.0000	50.6438	0.0000	✓ Yes
ENV_PROBEMOIS_JUN	0.2328	0.0000	0.0000	0.2335	0.0000	22.4076	0.0000	✓ Yes

Figures

Screening volcano Download PNG

P-value QQ plot Download PNG

Pathway Analysis (same dataset) Help

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Method & Provenance

Method: Univariate feature screening
Method key: univariate_screening (result type: feature_selection, result #163)
Engine: Python · scikit-learn/scipy v1.8.0
Dataset(s): #694 gallery — catg_example_01_forage_biomass.csv
Status: completed
Permanent link: https://cat-g-omxpert.wishartlab.com/results/by-token/6464413f754e4693bdfa3aaf528d0baf Bookmark or share this URL — it does not depend on your session.
Created: 2026-09-21 02:13:46.233864

Parameters used

Parameter	Value
`method`	univariate_screening
`target_col`	PC_SHOOTMASS
`fdr_threshold`	0.05
`derived_dataset_id`	696

Execution trace

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

from scipy import stats

r, p = stats.pearsonr(x_feature, y_target)

stat, p = stats.kruskal(*groups)  # per feature

fdr = statsmodels.stats.multitest.multipletests(p_values, method='fdr_bh')