Posterior (or prior) predictive checks (S3 generic and default method)

S3 generic with simple default method. The intent is to provide a generic so authors of other R packages who wish to provide interfaces to the functions in bayesplot will be encouraged to include pp_check() methods in their package, preserving the same naming conventions for posterior (and prior) predictive checking across many R packages for Bayesian inference. This is for the convenience of both users and developers. See the Details and Examples sections, below, and the package vignettes for examples of defining pp_check() methods.

pp_check(object, ...)

# S3 method for default
pp_check(object, yrep, fun, ...)

Arguments

object: Typically a fitted model object. The default method, however, takes object to be a y (outcome) vector.
...: For the generic, arguments passed to individual methods. For the default method, these are additional arguments to pass to fun.
yrep: For the default method, a yrep matrix passed to fun.
fun: For the default method, the plotting function to call. Can be any of the PPC functions. The "ppc_" prefix can optionally be dropped if fun is specified as a string.

Value

The exact form of the value returned by pp_check() may vary by the class of object, but for consistency we encourage authors of methods to return the ggplot object created by one of bayesplot's plotting functions. The default method returns the object returned by fun.

Details

A package that creates fitted model objects of class "foo" can include a method pp_check.foo() that prepares the appropriate inputs (y, yrep, etc.) for the bayesplot functions. The pp_check.foo() method may, for example, let the user choose between various plots, calling the functions from bayesplot internally as needed. See Examples, below, and the package vignettes.

Examples

# default method
y <- example_y_data()
yrep <- example_yrep_draws()
pp_check(y, yrep[1:50,], ppc_dens_overlay)


g <- example_group_data()
pp_check(y, yrep, fun = "stat_grouped", group = g, stat = "median")
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.


# defining a method
x <- list(y = rnorm(50), yrep = matrix(rnorm(5000), nrow = 100, ncol = 50))
class(x) <- "foo"
pp_check.foo <- function(object, ..., type = c("multiple", "overlaid")) {
  y <- object[["y"]]
  yrep <- object[["yrep"]]
  switch(match.arg(type),
         multiple = ppc_hist(y, yrep[1:min(8, nrow(yrep)),, drop = FALSE]),
         overlaid = ppc_dens_overlay(y, yrep))
}
pp_check(x)
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

pp_check(x, type = "overlaid")