Result Detail – Univariate Screening Help

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Result Information

Result ID: 160
Type: Univariate Screening
Status: completed
Dataset ID: 690
Created: 2026-09-21 02:12:36.563461

Pre-Modeling Statistical Screening

Method: univariate screening
Features Selected: 35
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	-0.3566	0.0000	0.0000	-0.2676	0.0000	57.9998	0.0000	✓ Yes
LAT	0.3911	0.0000	0.0000	0.2634	0.0000	71.8502	0.0000	✓ Yes
LONG	0.3848	0.0000	0.0000	0.2388	0.0000	69.1789	0.0000	✓ Yes
ENV_SOILNH4	0.0824	0.0996	0.1208	0.1870	0.0002	2.7239	0.1208	✓ Yes
ENV_SOILNO3	0.0367	0.4639	0.5015	0.0933	0.0691	0.5376	0.5015	✗ No
ENV_SOILP	0.2028	0.0000	0.0001	0.1271	0.0133	17.0726	0.0001	✓ Yes
ENV_SOILOC	0.4830	0.0000	0.0000	0.4605	0.0000	121.1279	0.0000	✓ Yes
ENV_SOILTN	0.4801	0.0000	0.0000	0.4567	0.0000	119.2385	0.0000	✓ Yes
ENV_SOILTC	0.4829	0.0000	0.0000	0.4597	0.0000	121.0029	0.0000	✓ Yes
ENV_PH	-0.0157	0.7540	0.7540	-0.0332	0.5344	0.0983	0.7540	✗ No
ENV_SOILCLAY	0.3541	0.0000	0.0000	0.3538	0.0000	57.0470	0.0000	✓ Yes
ENV_SOILSILT	0.4306	0.0000	0.0000	0.4009	0.0000	90.5771	0.0000	✓ Yes
ENV_SOILSAND	-0.4124	0.0000	0.0000	-0.3838	0.0000	81.5668	0.0000	✓ Yes
ENV_SOILMOIS	0.4614	0.0000	0.0000	0.4451	0.0000	107.6683	0.0000	✓ Yes
RC_SHOOTMASS	0.5332	0.0000	0.0000	0.6073	0.0000	158.0640	0.0000	✓ Yes
RC_WOODMASS	0.1152	0.0212	0.0283	0.1585	0.0019	5.3525	0.0283	✓ Yes
RC_LITTERMASS	0.4564	0.0000	0.0000	0.3922	0.0000	104.7397	0.0000	✓ Yes
RC_LITTERWMASS	0.0591	0.2383	0.2724	0.1293	0.0120	1.3947	0.2724	✓ Yes
PC_WOODMASS	0.2348	0.0000	0.0000	0.2313	0.0000	23.2218	0.0000	✓ Yes
PC_LITTERMASS	0.4327	0.0000	0.0000	0.4261	0.0000	91.6693	0.0000	✓ Yes
PC_LITTERWMASS	0.1439	0.0039	0.0058	0.1252	0.0144	8.4115	0.0058	✓ Yes
PC_ROOTMASS	0.1007	0.0442	0.0571	0.2809	0.0000	4.0741	0.0571	✓ Yes
PC_HEIGHT	0.4465	0.0000	0.0000	0.4329	0.0000	99.0837	0.0000	✓ Yes
GH_COUNT	-0.2974	0.0000	0.0000	-0.2940	0.0000	38.6160	0.0000	✓ Yes
ENV_AIRTEMP	0.0247	0.6220	0.6379	-0.0142	0.7971	0.2435	0.6379	✗ No
COVER_RICHNESS_JUN	-0.1236	0.0134	0.0185	-0.2189	0.0000	6.1702	0.0185	✓ Yes
COVER_TOTAL_JUN	0.3239	0.0000	0.0000	0.3605	0.0000	46.6550	0.0000	✓ Yes
COVER_SHANNON_JUN	-0.1286	0.0100	0.0143	-0.2196	0.0000	6.6913	0.0143	✓ Yes
COVER_RICHNESS_JUL	-0.0938	0.0609	0.0761	-0.1713	0.0008	3.5336	0.0761	✓ Yes
COVER_TOTAL_JUL	0.3024	0.0000	0.0000	0.4263	0.0000	40.0461	0.0000	✓ Yes
COVER_SHANNON_JUL	-0.0286	0.5682	0.5981	-0.1044	0.0421	0.3263	0.5981	✓ Yes
COVER_RICHNESS_AUG	-0.1785	0.0003	0.0005	-0.2616	0.0000	13.0987	0.0005	✓ Yes
COVER_TOTAL_AUG	0.3798	0.0000	0.0000	0.4245	0.0000	67.0752	0.0000	✓ Yes
COVER_SHANNON_AUG	-0.1615	0.0012	0.0018	-0.2277	0.0000	10.6585	0.0018	✓ Yes
PASTURE_TOTALAREA	-0.3966	0.0000	0.0000	-0.3771	0.0000	74.3077	0.0000	✓ Yes
SUBPASTURE_TOTALAREA	-0.3128	0.0000	0.0000	-0.3020	0.0000	43.1698	0.0000	✓ Yes
PASTURE_FORESTAREA	0.2953	0.0000	0.0000	0.2456	0.0000	38.0232	0.0000	✓ Yes
PASTURE_WETLANDAREA	-0.0716	0.1531	0.1802	0.0093	0.8533	2.0486	0.1802	✗ No
SUBPASTURE_FORESTAREA	0.2507	0.0000	0.0000	0.2206	0.0000	26.6878	0.0000	✓ Yes
SUBPASTURE_WETLANDAREA	0.0440	0.3797	0.4219	0.0704	0.1730	0.7734	0.4219	✗ No

Figures

Screening volcano Download PNG

P-value QQ plot Download PNG

Pathway Analysis (same dataset) Help

Run Pathway Analysis

Method & Provenance

Method: Univariate feature screening
Method key: univariate_screening (result type: feature_selection, result #160)
Engine: Python · scikit-learn/scipy 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/ddc9cb78b42f4d9c87d80959e89650af Bookmark or share this URL — it does not depend on your session.
Created: 2026-09-21 02:12:36.563461

Parameters used

Parameter	Value
`method`	univariate_screening
`target_col`	PC_SHOOTMASS
`fdr_threshold`	0.05
`derived_dataset_id`	692

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')