PPD test statistics

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 PPC-test-statistics has details on the individual plots.

Usage

ppd_stat(
  ypred,
  stat = "mean",
  ...,
  discrete = FALSE,
  binwidth = NULL,
  bins = NULL,
  breaks = NULL,
  freq = TRUE
)

ppd_stat_grouped(
  ypred,
  group,
  stat = "mean",
  ...,
  discrete = FALSE,
  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)

Arguments

ypred: An S by N matrix of draws from the posterior (or prior) predictive distribution. The number of rows, S, is the size of the posterior (or prior) sample used to generate ypred. The number of columns, N, is the number of predicted observations.
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.
discrete: For ppc_stat() and ppc_stat_grouped(), if TRUE then a bar chart is used instead of a histogram.
binwidth: Passed to ggplot2::geom_histogram() to override the default binwidth.
bins: Passed to ggplot2::geom_histogram() to override the default binwidth.
breaks: Passed to ggplot2::geom_histogram() as an alternative to binwidth.
freq: For histograms, freq=TRUE (the default) puts count on the y-axis. Setting freq=FALSE puts density on the y-axis. (For many plots the y-axis text is off by default. To view the count or density labels on the y-axis see the yaxis_text() convenience function.)
group: A grouping variable of the same length as y. Will be coerced to factor if not already a factor. Each value in group is interpreted as the group level pertaining to the corresponding observation.
facet_args: A named list of arguments (other than facets) passed to ggplot2::facet_wrap() or ggplot2::facet_grid() to control faceting. Note: if scales is not included in facet_args then bayesplot may use scales="free" as the default (depending on the plot) instead of the ggplot2 default of scales="fixed".
size, alpha: For the 2D plot only, arguments passed to ggplot2::geom_point() to control the appearance of scatterplot points.

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.

Details

For Binomial data, the plots may be more useful if the input contains the "success" proportions (not discrete "success" or "failure" counts).

References

Gabry, J. , Simpson, D. , Vehtari, A. , Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. J. R. Stat. Soc. A, 182: 389-402. doi:10.1111/rssa.12378. (journal version, arXiv preprint, code on GitHub)

Examples

yrep <- example_yrep_draws()
ppd_stat(yrep)
#> `stat_bin()` using `bins = 30`. Pick better value `binwidth`.

ppd_stat(yrep, stat = "sd") + legend_none()
#> `stat_bin()` using `bins = 30`. Pick better value `binwidth`.


# 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
#> `stat_bin()` using `bins = 30`. Pick better value `binwidth`.