The bayesplot PPD module provides various plotting functions for creating graphical displays of simulated data from the posterior or prior predictive distribution. These plots are essentially the same as the corresponding PPC plots but without showing any observed data. Because these are not "checks" compared to data we use PPD (for prior/posterior predictive distribution) instead of PPC (for prior/posterior predictive check).
The functions for plotting prior and
posterior predictive distributions without observed data each have the
prefix ppd_ and all have a required argument ypred (a matrix of
predictions). The plots are organized into several categories, each with
its own documentation:
PPD-distributions: Histograms, kernel density estimates, boxplots, and
other plots of multiple simulated datasets (rows) in ypred. These are the
same as the plots in PPC-distributions but without including any
comparison to y.
PPD-intervals: Interval estimates for each predicted observations
(columns) in ypred. The x-axis variable can be optionally specified by
the user (e.g. to plot against against a predictor variable or over
time).These are the same as the plots in PPC-intervals but without
including any comparison to y.
PPD-test-statistics: The distribution of a statistic, or a pair of
statistics, over the simulated datasets (rows) in ypred. These are the
same as the plots in PPC-test-statistics but without including any
comparison to y.
Gabry, J. , Simpson, D. , Vehtari, A. , Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. J. R. Stat. Soc. A, 182: 389-402. doi:10.1111/rssa.12378. (journal version, arXiv preprint, code on GitHub)
Other PPDs:
PPD-distributions,
PPD-intervals,
PPD-test-statistics