PPDteststatistics {bayesplot}  R Documentation 
The distribution of a (test) statistic T(ypred)
, or a pair of (test)
statistics, over the simulations from the posterior or prior predictive
distribution. Each of these functions makes the same plot as the
corresponding ppc_
function but without comparing to
any observed data y
. The Plot Descriptions section at
PPCteststatistics has details on the individual plots.
ppd_stat(
ypred,
stat = "mean",
...,
binwidth = NULL,
bins = NULL,
breaks = NULL,
freq = TRUE
)
ppd_stat_grouped(
ypred,
group,
stat = "mean",
...,
facet_args = list(),
binwidth = NULL,
bins = NULL,
breaks = NULL,
freq = TRUE
)
ppd_stat_freqpoly(
ypred,
stat = "mean",
...,
facet_args = list(),
binwidth = NULL,
bins = NULL,
freq = TRUE
)
ppd_stat_freqpoly_grouped(
ypred,
group,
stat = "mean",
...,
facet_args = list(),
binwidth = NULL,
bins = NULL,
freq = TRUE
)
ppd_stat_2d(ypred, stat = c("mean", "sd"), ..., size = 2.5, alpha = 0.7)
ppd_stat_data(ypred, group = NULL, stat)
ypred 
An 
stat 
A single function or a string naming a function, except for the 2D plot which requires a vector of exactly two names or functions. In all cases the function(s) should take a vector input and return a scalar statistic. If specified as a string (or strings) then the legend will display the function name(s). If specified as a function (or functions) then generic naming is used in the legend. 
... 
Currently unused. 
binwidth 
Passed to 
bins 
Passed to 
breaks 
Passed to 
freq 
For histograms, 
group 
A grouping variable of the same length as 
facet_args 
A named list of arguments (other than 
size , alpha 
For the 2D plot only, arguments passed to

For Binomial data, the plots may be more useful if the input contains the "success" proportions (not discrete "success" or "failure" counts).
The plotting functions return a ggplot object that can be further
customized using the ggplot2 package. The functions with suffix
_data()
return the data that would have been drawn by the plotting
function.
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)
Other PPDs:
PPDdistributions
,
PPDintervals
,
PPDoverview
yrep < example_yrep_draws()
ppd_stat(yrep)
ppd_stat(yrep, stat = "sd") + legend_none()
# use your own function for the 'stat' argument
color_scheme_set("brightblue")
q25 < function(y) quantile(y, 0.25)
ppd_stat(yrep, stat = "q25") # legend includes function name