PPC-discrete {bayesplot} | R Documentation |
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.
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)
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 |
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 non-negative.
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.
ppc_bars()
Bar plot of y
with yrep
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 on yrep
overlaid as a line along with uncertainty
intervals. The y-axis represents the square roots of the counts to
approximately adjust for scale differences and thus ease comparison between
observed and expected counts. Using the style
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.
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.
Other PPCs:
PPC-censoring
,
PPC-distributions
,
PPC-errors
,
PPC-intervals
,
PPC-loo
,
PPC-overview
,
PPC-scatterplots
,
PPC-test-statistics
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("mix-pink-blue")
ppc_bars(y, yrep)
# split by group, change interval width, and display proportion
# instead of count on y-axis
color_scheme_set("mix-blue-pink")
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")