name: statsmodels description: | Runs regression analysis, ANOVA, and repeated measures statistical models. Use when: fitting logistic/OLS regression, running ANOVA, GEE models, mixed effects models, McNemar tests, power analysis, or handling convergence failures in statistical models. allowed-tools: Read, Edit, Write, Glob, Grep, Bash

Statsmodels Skill

Statistical modeling in this project uses formula-based and matrix-based APIs with robust fallback chains for convergence failures. Models cluster on complaint_id for within-subject repeated measures.

Quick Start

Logistic Regression with Fallback

import statsmodels.api as sm
from statsmodels.tools import add_constant

X = add_constant(pd.get_dummies(df[predictor_cols], drop_first=True, dtype=float))
y = df["outcome"]

try:
    fit = sm.Logit(y, X).fit(disp=0, maxiter=100)
    if not fit.mle_retvals["converged"]:
        raise RuntimeError("Did not converge")
except Exception:
    # Fallback to sklearn ridge
    from sklearn.linear_model import LogisticRegression
    lr = LogisticRegression(penalty="l2", C=1.0, solver="lbfgs", max_iter=500)
    lr.fit(X.values, y.values)

Formula-Based ANOVA

import statsmodels.formula.api as smf
from statsmodels.stats.anova import anova_lm

fit = smf.ols("outcome ~ C(persona) + C(severity) + C(persona):C(severity)", data=df).fit()
table = anova_lm(fit, typ=2)  # Type II SS
f_val = float(table.loc["C(persona)", "F"])
p_val = float(table.loc["C(persona)", "PR(>F)"])

GEE for Clustered Binary Outcomes

import statsmodels.formula.api as smf
from statsmodels.genmod.families import Binomial
from statsmodels.genmod.cov_struct import Exchangeable, Independence

gee = smf.gee(
    "favourable ~ C(persona)",
    groups="complaint_id",
    data=df,
    family=Binomial(),
    cov_struct=Exchangeable(),
)
fit = gee.fit(maxiter=100)

Key Concepts

Result Extraction

fit.params            # Coefficients (Series)
fit.pvalues           # P-values (Series)
fit.bse               # Standard errors (Series)
fit.conf_int()        # CI DataFrame [lower, upper]
fit.prsquared         # Pseudo R² (logit only)
fit.aic               # AIC
fit.mle_retvals["converged"]  # Convergence status
np.exp(fit.params)    # Odds ratios for logit

See Also

• patterns - Model specification, fallback chains, error handling
• workflows - End-to-end analysis workflows

Related Skills

• See the pandas skill for DataFrame preparation and groupby operations
• See the scipy skill for chi-squared tests and FDR correction
• See the scikit-learn skill for regularized fallback models