mcp: pp_check – R documentation

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pp_check

Posterior Predictive Checks For Mcpfit Objects

Description

Plot posterior (default) or prior (prior = TRUE) predictive checks. This is convenience wrapper around the bayesplot::ppc_*() methods.

Usage

pp_check(
  object,
  type = "dens_overlay",
  facet_by = NULL,
  newdata = NULL,
  prior = FALSE,
  varying = TRUE,
  arma = TRUE,
  nsamples = 100,
  ...
)

Arguments

`object`	An `mcpfit` object.
`type`	One of `bayesplot::available_ppc("grouped", invert = TRUE) %>% stringr::str_remove("ppc_")`
`facet_by`	Name of a column in data modeled as varying effect(s).
`newdata`	A `tibble` or a `data.frame` containing predictors in the model. If `NULL` (default), the original data is used.
`prior`	TRUE/FALSE. Plot using prior samples? Useful for `mcp(..., sample = "both")`
`varying`	TRUE: All varying effects (`fit$pars$varying`). FALSE: No varying efects (`c()`). Character vector: Only include specified varying parameters - see `fit$pars$varying`.
`arma`	Whether to include autoregressive effects. `TRUE:` Compute autoregressive residuals. Requires the response variable in `newdata`. `FALSE:` Disregard the autoregressive effects. For `family = gaussian()`, `predict()` just use `sigma` for residuals.
`nsamples`	Number of draws. Note that you may want to use all data for summary geoms. e.g., `pp_check(fit, type = "ribbon", nsamples = NULL)`.
`...`	Further arguments passed to `bayesplot::ppc_type(y, yrep, ...)`

Value

A ggplot2 object for single plots. Enriched by patchwork for faceted plots.

Author(s)

Jonas Kristoffer Lindeløv jonas@lindeloev.dk

Examples

pp_check(ex_fit)
pp_check(ex_fit, type = "ecdf_overlay")
#pp_check(some_varying_fit, type = "loo_intervals", facet_by = "id")

mcp

Regression with Multiple Change Points

v0.3.0

GPL-2

Authors

Jonas Kristoffer Lindeløv [aut, cre] (<https://orcid.org/0000-0003-4565-0595>)

Initial release

2020-07-27