PPDdistributions {bayesplot}  R Documentation 
PPD distributions
Description
Plot posterior or prior predictive distributions. Each of these functions
makes the same plot as the corresponding ppc_
function
but without plotting any observed data y
. The Plot Descriptions section
at PPCdistributions has details on the individual plots.
Usage
ppd_data(ypred, group = NULL)
ppd_dens_overlay(
ypred,
...,
size = 0.25,
alpha = 0.7,
trim = FALSE,
bw = "nrd0",
adjust = 1,
kernel = "gaussian",
n_dens = 1024
)
ppd_ecdf_overlay(
ypred,
...,
discrete = FALSE,
pad = TRUE,
size = 0.25,
alpha = 0.7
)
ppd_dens(ypred, ..., trim = FALSE, size = 0.5, alpha = 1)
ppd_hist(ypred, ..., binwidth = NULL, bins = NULL, breaks = NULL, freq = TRUE)
ppd_freqpoly(
ypred,
...,
binwidth = NULL,
bins = NULL,
freq = TRUE,
size = 0.5,
alpha = 1
)
ppd_freqpoly_grouped(
ypred,
group,
...,
binwidth = NULL,
bins = NULL,
freq = TRUE,
size = 0.5,
alpha = 1
)
ppd_boxplot(ypred, ..., notch = TRUE, size = 0.5, alpha = 1)
Arguments
ypred 
An 
group 
A grouping variable of the same length as 
... 
Currently unused. 
size , alpha 
Passed to the appropriate geom to control the appearance of the predictive distributions. 
trim 
A logical scalar passed to 
bw , adjust , kernel , n_dens 
Optional arguments passed to

discrete 
For 
pad 
A logical scalar passed to 
binwidth 
Passed to 
bins 
Passed to 
breaks 
Passed to 
freq 
For histograms, 
notch 
For the box plot, a logical scalar passed to

Details
For Binomial data, the plots may be more useful if the input contains the "success" proportions (not discrete "success" or "failure" counts).
Value
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.
See Also
Other PPDs:
PPDintervals
,
PPDoverview
,
PPDteststatistics
Examples
# difference between ppd_dens_overlay() and ppc_dens_overlay()
color_scheme_set("brightblue")
preds < example_yrep_draws()
ppd_dens_overlay(ypred = preds[1:50, ])
ppc_dens_overlay(y = example_y_data(), yrep = preds[1:50, ])