Glm run #182 · statistical_test
0
p value
poisson
family
2949
aic
1942
deviance
completed
status
2026-09-21 02:15
created
Warnings
- The data appear overdispersed relative to the Poisson model (Pearson phi = 9.47; values near 1 are expected). Coefficient standard errors are likely too small — refit with the Negative Binomial family.
- The deviance goodness-of-fit test rejects adequate fit (p = 3.402e-280); residual structure may remain unexplained.
Figures
Coefficient estimates (95% CI)
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Observed vs fitted
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Residuals vs fitted
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Coefficients 2 rows
| term | coef | se | z | p | ci_lower | ci_upper |
|---|---|---|---|---|---|---|
| const | 4.536948710118011 | 0.019579471700220362 | 231.71966943658387 | 0.0 | 4.498573650749258 | 4.575323769486764 |
| ENV_SOILTN | -5.442331821505843 | 0.11915404288412519 | -45.6747558855254 | 0.0 | -5.675869454171069 | -5.208794188840617 |
Method & Provenance
- Method
- Count GLM (Poisson / Negative Binomial)
- Method key
glm(result type:statistical_test, result #182)- Engine
- Python · statsmodels v0.14.6
- Dataset(s)
- #700 gallery — catg_example_05_grasshopper_pest_pressure.csv
- Status
- completed
- Permanent link
- https://cat-g-omxpert.wishartlab.com/results/by-token/e5dac942c0614cd5bc9baa1859614534 Bookmark or share this URL — it does not depend on your session.
- Created
- 2026-09-21 02:15:12.174965
Parameters used
| Parameter | Value |
|---|---|
test_type
| glm |
value_col
| GH_COUNT |
predictor_cols
| ENV_SOILTN |
Execution trace
Reconstructed from the parameters stored with this result — represents the Python/R calls the analysis service made.
import statsmodels.api as smmodel = sm.GLM(y_counts, X_predictors, family=sm.families.Poisson())fit = model.fit()