Post-Modeling

Use this section after fitting PanelMMM.

It covers posterior predictive checks, diagnostics, contribution analysis, response curves, efficiency metrics, and the tabular summary surfaces that Abacus exposes from fitted InferenceData.

Pages

• Posterior Predictive: Sample fitted or future predictions and compare them with observed data where available.
• Diagnostics: Run design-matrix, MCMC, and predictive diagnostics and export machine-readable reports.
• Contributions and Decomposition: Inspect channel, baseline, control, seasonality, and event contributions.
• Response Curves: Sample and summarise posterior saturation and adstock curves, and understand the runner’s forward-pass direct contribution artefacts.
• ROAS and Metrics: Calculate ROAS, CPA-style metrics, spend tables, and predictive error metrics.
• Summary and Export: Work with MMMSummaryFactory, HDI settings, time aggregation, and DataFrame export.