/post-modeling/index.html 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. Hugo en-gb /post-modeling/diagnostics/index.html Mon, 01 Jan 0001 00:00:00 +0000 /post-modeling/diagnostics/index.html Abacus exposes diagnostics through mmm.diagnostics. Use this surface to check the design matrix, posterior sampling quality, and posterior predictive fit. For fitted-value plots and predictive sampling, see Posterior Predictive. Diagnostic surfaces mmm.diagnostics provides three groups of checks. Area Summary method Report method What it covers Raw input screening design_summary(X) design_report(X) Collinearity, constants, and near-constant regressors on raw input columns MCMC mcmc_summary() mcmc_report() r_hat, ESS, divergences, BFMI, tree depth, acceptance rate Predictive predictive_summary() predictive_report() RMSE, MAE, NRMSE, NMAE, CRPS, residual moments The summary methods return pandas DataFrames. The report methods return typed report objects with a to_dict() method for JSON-ready export. /post-modeling/posterior-predictive/index.html Mon, 01 Jan 0001 00:00:00 +0000 /post-modeling/posterior-predictive/index.html Use posterior predictive draws to check in-sample fit and to generate predictions for new rows that follow the fitted panel layout. For diagnostic metrics after sampling, see Diagnostics. For table export, see Summary and Export. Sample posterior predictive draws Use PanelMMM.sample_posterior_predictive(...) on a fitted model: posterior_predictive = mmm.sample_posterior_predictive( X=X, random_seed=42, progressbar=False, ) sample_posterior_predictive(...): requires X uses the fitted posterior stored on mmm.idata reshapes X into the model’s panel xarray layout runs pymc.sample_posterior_predictive(...) returns an extracted xarray.Dataset By default, combined=True, so the returned dataset uses a sample dimension. If you want separate chain and draw dimensions, set combined=False. /post-modeling/contributions-and-decomposition/index.html Mon, 01 Jan 0001 00:00:00 +0000 /post-modeling/contributions-and-decomposition/index.html Abacus stores additive contribution terms for fitted PanelMMM models and exposes them through the data wrapper, summary tables, and plotting suite. Use this page to inspect media, baseline, control, seasonality, and event effects. For channel efficiency ratios built from media contributions, see ROAS and Metrics. Contribution surfaces You can work with contributions at three levels. Surface Use it for mmm.data Raw xarray contribution samples mmm.summary DataFrames with posterior means, medians, and HDIs mmm.plot Time-series and waterfall visualisations Read raw contribution samples The lowest-level accessor is mmm.data.get_contributions(...): /post-modeling/response-curves/index.html Mon, 01 Jan 0001 00:00:00 +0000 /post-modeling/response-curves/index.html Use response curves to inspect the fitted media transformations directly. Abacus exposes posterior saturation and adstock curves through both the fitted model and mmm.summary. For decomposition of realised contributions over time, see Contributions and Decomposition. Sample saturation curves Use sample_saturation_curve(...) on a fitted PanelMMM: saturation_curve = mmm.sample_saturation_curve( max_value=1.0, num_points=100, num_samples=500, random_state=42, original_scale=True, ) The returned xarray.DataArray contains: /post-modeling/roas-and-metrics/index.html Mon, 01 Jan 0001 00:00:00 +0000 /post-modeling/roas-and-metrics/index.html Use this page for channel-efficiency outputs and aggregate predictive metrics. Abacus separates these into two surfaces: mmm.summary and mmm.data for ROAS and cost-per-target outputs mmm.diagnostics for RMSE, MAE, NRMSE, NMAE, and CRPS For contribution tables that feed these ratios, see Contributions and Decomposition. Element-wise ROAS and cost per target The lowest-level efficiency accessors live on mmm.data: roas_samples = mmm.data.get_elementwise_roas(original_scale=True) cost_per_target_samples = mmm.data.get_elementwise_cost_per_target( original_scale=True, ) These are direct ratios built from fitted media contributions and channel spend: /post-modeling/summary-and-export/index.html Mon, 01 Jan 0001 00:00:00 +0000 /post-modeling/summary-and-export/index.html mmm.summary is the retained tabular summary surface for fitted PanelMMM models. It is backed by MMMSummaryFactory and returns pandas or polars DataFrames that you can export with normal DataFrame methods. For predictive diagnostics and JSON-ready reports, see Diagnostics. Use mmm.summary The simplest path is the bound summary factory on the fitted model: posterior_df = mmm.summary.posterior_predictive() contributions_df = mmm.summary.contributions(component="channel") roas_df = mmm.summary.roas(frequency="monthly") mmm.summary already has access to: