PPCdiscrete {bayesplot}  R Documentation 
PPCs for discrete outcomes
Description
Many of the PPC functions in bayesplot can
be used with discrete data. The small subset of these functions that can
only be used if y
and yrep
are discrete are documented
on this page. Currently these include rootograms for count outcomes and bar
plots for ordinal, categorical, and multinomial outcomes. See the
Plot Descriptions section below.
Usage
ppc_bars(
y,
yrep,
...,
prob = 0.9,
width = 0.9,
size = 1,
fatten = 2.5,
linewidth = 1,
freq = TRUE
)
ppc_bars_grouped(
y,
yrep,
group,
...,
facet_args = list(),
prob = 0.9,
width = 0.9,
size = 1,
fatten = 2.5,
linewidth = 1,
freq = TRUE
)
ppc_rootogram(
y,
yrep,
style = c("standing", "hanging", "suspended"),
...,
prob = 0.9,
size = 1
)
ppc_bars_data(y, yrep, group = NULL, prob = 0.9, freq = TRUE)
Arguments
y 
A vector of observations. See Details. 
yrep 
An 
... 
Currently unused. 
prob 
A value between 
width 
For bar plots only, passed to 
size , fatten , linewidth 
For bar plots, 
freq 
For bar plots only, if 
group 
A grouping variable of the same length as 
facet_args 
An optional list of arguments (other than 
style 
For 
Details
For all of these plots y
and yrep
must be integers, although
they need not be integers in the strict sense of R's
integer type. For rootogram plots y
and yrep
must also
be nonnegative.
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.
Plot Descriptions
ppc_bars()

Bar plot of
y
withyrep
medians and uncertainty intervals superimposed on the bars. ppc_bars_grouped()

Same as
ppc_bars()
but a separate plot (facet) is generated for each level of a grouping variable. ppc_rootogram()

Rootograms allow for diagnosing problems in count data models such as overdispersion or excess zeros. They consist of a histogram of
y
with the expected counts based onyrep
overlaid as a line along with uncertainty intervals. The yaxis represents the square roots of the counts to approximately adjust for scale differences and thus ease comparison between observed and expected counts. Using thestyle
argument, the histogram style can be adjusted to focus on different aspects of the data:
Standing: basic histogram of observed counts with curve showing expected counts.

Hanging: observed counts counts hanging from the curve representing expected counts.

Suspended: histogram of the differences between expected and observed counts.
All of the rootograms are plotted on the square root scale. See Kleiber and Zeileis (2016) for advice on interpreting rootograms and selecting among the different styles.

References
Kleiber, C. and Zeileis, A. (2016). Visualizing count data regressions using rootograms. The American Statistician. 70(3): 296–303. https://arxiv.org/abs/1605.01311.
See Also
Other PPCs:
PPCcensoring
,
PPCdistributions
,
PPCerrors
,
PPCintervals
,
PPCloo
,
PPCoverview
,
PPCscatterplots
,
PPCteststatistics
Examples
set.seed(9222017)
# bar plots
f < function(N) {
sample(1:4, size = N, replace = TRUE, prob = c(0.25, 0.4, 0.1, 0.25))
}
y < f(100)
yrep < t(replicate(500, f(100)))
dim(yrep)
group < gl(2, 50, length = 100, labels = c("GroupA", "GroupB"))
color_scheme_set("mixpinkblue")
ppc_bars(y, yrep)
# split by group, change interval width, and display proportion
# instead of count on yaxis
color_scheme_set("mixbluepink")
ppc_bars_grouped(y, yrep, group, prob = 0.5, freq = FALSE)
## Not run:
# example for ordinal regression using rstanarm
library(rstanarm)
fit < stan_polr(
tobgp ~ agegp,
data = esoph,
method = "probit",
prior = R2(0.2, "mean"),
init_r = 0.1,
seed = 12345,
# cores = 4,
refresh = 0
)
# coded as character, so convert to integer
yrep_char < posterior_predict(fit)
print(yrep_char[1, 1:4])
yrep_int < sapply(data.frame(yrep_char, stringsAsFactors = TRUE), as.integer)
y_int < as.integer(esoph$tobgp)
ppc_bars(y_int, yrep_int)
ppc_bars_grouped(
y = y_int,
yrep = yrep_int,
group = esoph$agegp,
freq=FALSE,
prob = 0.5,
fatten = 1,
size = 1.5
)
## End(Not run)
# rootograms for counts
y < rpois(100, 20)
yrep < matrix(rpois(10000, 20), ncol = 100)
color_scheme_set("brightblue")
ppc_rootogram(y, yrep)
ppc_rootogram(y, yrep, prob = 0)
ppc_rootogram(y, yrep, style = "hanging", prob = 0.8)
ppc_rootogram(y, yrep, style = "suspended")