R: PPCs for discrete outcomes

PPC-discrete {bayesplot}

R Documentation

PPCs for discrete outcomes

Description

This page documents the PPC functions that can only be used if y and yrep are discrete. 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 = 3,
  freq = TRUE)

ppc_bars_grouped(y, yrep, group, facet_args = list(), ..., prob = 0.9,
  width = 0.9, size = 1, fatten = 3, freq = TRUE)

ppc_rootogram(y, yrep, style = c("standing", "hanging", "suspended"), ...,
  prob = 0.9, size = 1)

Arguments

`y`	A vector of observations. See Details.
`yrep`	An S by N matrix of draws from the posterior predictive distribution, where S is the size of the posterior sample (or subset of the posterior sample used to generate `yrep`) and N is the number of observations (the length of `y`). The columns of `yrep` should be in the same order as the data points in `y` for the plots to make sense. See Details for additional instructions.
`...`	Currently unused.
`prob`	A value between 0 and 1 indicating the desired probability mass to include in the `yrep` intervals. Set `prob=0` to remove the intervals. For `ppc_rootogram` these are intervals of the square roots of the expected counts.
`width`	For `ppc_bars` and `ppc_bars_grouped`, passed to `geom_bar` to control the bar width.
`size, fatten`	For `ppc_bars` and `ppc_bars_grouped`, `size` and `fatten` are passed to `geom_pointrange` to control the appearance of the `yrep` points and intervals. For `ppc_rootogram` `size` is passed to `geom_line`.
`freq`	For `ppc_bars` and `ppc_bars_grouped`, if `TRUE` (the default) the y-axis will display counts. Setting `freq=FALSE` will put proportions on the y-axis.
`group`	A grouping variable (a vector or factor) the same length as `y`. Each value in `group` is interpreted as the group level pertaining to the corresponding value of `y`.
`facet_args`	An optional list of arguments (other than `facets`) passed to `facet_wrap` to control faceting.
`style`	For `ppc_rootogram`, a string specifying the rootogram style. The options are `"standing"`, `"hanging"`, and `"suspended"`. See the Plot Descriptions section, below, for details on the different styles.

Details

For all of these plots y and yrep must be non-negative integers, although they need not be integers in the strict sense of R's integer type.

Value

A ggplot object that can be further customized using the ggplot2 package.

Plot Descriptions

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 these 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.

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("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)

# 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")

[Package bayesplot version 1.5.0 Index]