MMMPlotSuite#
- class pymc_marketing.mmm.plot.MMMPlotSuite(idata)[source]#
Media Mix Model Plot Suite.
Provides methods for visualizing the posterior predictive distribution, contributions over time, and saturation curves for a Media Mix Model.
Methods
MMMPlotSuite.__init__(idata)MMMPlotSuite.allocated_contribution_by_channel_over_time(samples)Plot the allocated contribution by channel with uncertainty intervals.
MMMPlotSuite.budget_allocation(samples[, ...])Plot the budget allocation and channel contributions.
Plot the share of channel contributions in a forest plot.
MMMPlotSuite.contributions_over_time(var[, ...])Plot the time-series contributions for each variable in
var.MMMPlotSuite.marginal_curve([hdi_prob, ax, ...])Plot precomputed marginal effects stored under
idata.sensitivity_analysis['marginal_effects'].MMMPlotSuite.posterior_distribution(var[, ...])Plot the posterior distribution of a variable across a specified dimension.
MMMPlotSuite.posterior_predictive([var, ...])Plot time series from the posterior predictive distribution.
MMMPlotSuite.prior_predictive([var, idata, ...])Plot time series from the posterior predictive distribution.
MMMPlotSuite.residuals_over_time([hdi_prob])Plot residuals over time by taking the difference between true values and predicted.
Plot the posterior distribution of residuals.
MMMPlotSuite.saturation_curves(curve[, ...])Overlay saturation‑curve scatter‑plots with posterior‑predictive sample curves and HDI bands.
Plot scatter plots of channel contributions vs.
Plot the saturation curves for each channel.
Plot sensitivity analysis results.
MMMPlotSuite.uplift_curve([hdi_prob, ax, ...])Plot precomputed uplift curves stored under
idata.sensitivity_analysis['uplift_curve'].Create a waterfall plot showing the decomposition of the target into its components.