PPDoverview {bayesplot}  R Documentation 
Plots of posterior or prior predictive distributions
Description
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).
PPD plotting functions
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:

PPDdistributions: Histograms, kernel density estimates, boxplots, and other plots of multiple simulated datasets (rows) in
ypred
. These are the same as the plots in PPCdistributions but without including any comparison toy
. 
PPDintervals: Interval estimates for each predicted observations (columns) in
ypred
. The xaxis 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 PPCintervals but without including any comparison toy
. 
PPDteststatistics: 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 PPCteststatistics but without including any comparison toy
.
References
Gabry, J. , Simpson, D. , Vehtari, A. , Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. J. R. Stat. Soc. A, 182: 389402. doi:10.1111/rssa.12378. (journal version, arXiv preprint, code on GitHub)
See Also
Other PPDs:
PPDdistributions
,
PPDintervals
,
PPDteststatistics