Pg regression mediation

Skill Exekiel179/pingouin-psych-stats/skills/pg-regression-mediation

Run or generate Pingouin code for linear regression, binary logistic regression, and mediation analysis with reproducible reporting.From its SKILL.md

Install

npx -y skills add Exekiel179/pingouin-psych-stats --skill pg-regression-mediation

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SKILL.md

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PG Regression Mediation

Use for regression-style questions that Pingouin supports directly.

Load

Read:

../../references/supervision-gates.md
../../references/pingouin-api-quickref.md
../../references/apa-output-template.md if writing results.

Function Choice

Continuous outcome, additive linear predictors -> pg.linear_regression.
Binary outcome -> pg.logistic_regression with X, y.
Single or multiple mediator path model -> pg.mediation_analysis.

Required Inputs

Outcome variable and scale.
Predictor list.
Covariates and whether they are theoretical controls.
Binary coding for logistic regression.
Mediation paths: x, m, y.
Bootstrap count and seed for mediation.

Code Patterns

Linear regression:

vars_needed = ["outcome", "x1", "x2"]
tmp = df.dropna(subset=vars_needed)
res = pg.linear_regression(tmp[["x1", "x2"]], tmp["outcome"],
                           add_intercept=True).round(3)
pg.print_table(res)

Logistic regression:

vars_needed = ["binary_outcome", "x1", "x2"]
tmp = df.dropna(subset=vars_needed)
res = pg.logistic_regression(tmp[["x1", "x2"]], tmp["binary_outcome"],
                             remove_na=False).round(3)
pg.print_table(res)

Mediation:

res = pg.mediation_analysis(data=df, x="x", m="mediator", y="outcome",
                            covar=["age"], n_boot=5000,
                            seed=42).round(3)
pg.print_table(res)

Reporting

Regression: report coefficient, SE, statistic, p, CI, and model R-squared/adjusted R-squared if returned.
Logistic: report coefficient and convert to odds ratio when useful with np.exp(coef).
Mediation: report total, direct, indirect paths, bootstrap CI, seed, and bootstrap count.

Guardrails

Pingouin linear regression is OLS; it is not a mixed model or SEM package.
For Pingouin 0.6.1, logistic_regression takes X, y; verify the signature before using examples from older wrappers.
Check residual assumptions and influential cases when substantive conclusions depend on them.
Do not present mediation as causal unless temporal order, design, and assumptions support it.
Do not hide automatic missing-data removal; report effective n.
Mediation bootstrap p-values vary with seed and bootstrap count.
End result-bearing answers with one compact S0-S5 audit line.

What ships with it: 1 file

267 B alongside SKILL.md

agents/

openai.yaml267 B