pp_check_ordbeta – ordbetareg

Accurate Posterior Predictive Plots for Ordbetareg Models

Description

The standard brms::pp_check plot available via brms is not accurate for ordbetareg models because an ordered beta regression has both continuous and discrete components. This function implements a bar plot and a density plot for the continuous and discrete elements separately, and will return accurate posterior predictive plots relative to the data.

Usage

pp_check_ordbeta(
  model = NULL,
  dv = NULL,
  type = "both",
  ndraws = 10,
  cores = NULL,
  group = NULL,
  new_theme = NULL,
  outcome_label = NULL,
  animate = FALSE,
  reverse_bounds = TRUE,
  facet_scales = "fixed"
)

Arguments

`model`	A fitted ordbetareg model.
`dv`	If you fit a model with multiple DVs/responses, pass the name of the DV as a character value. Note: this must be the same as the name of the column in the data used to fit the model.
`type`	Default is "both" for creating both a discrete (bar) and continuous (density) plot. Can also be "discrete" for only the bar plot for discrete values (0/1) or "continuous" for continuous values (density plot).
`ndraws`	Number of posterior draws to use to calculate estimates and show in plot. Defaults to 10.
`cores`	Number of cores to use to produce posterior predictive distribution. Defaults to NULL or 1 core.
`group`	A factor variable of the same number of rows as the data that is used to broduce grouped (faceted) plots of the posterior distribution.
`new_theme`	Any additional themes to be added to ggplot2 (default is NULL).
`outcome_label`	A character value that will replace the name of the outcome in the plot (default is the name of the response variable in the data frame).
`animate`	Whether to animate each posterior draw for continuous distributions (defaults to FALSE). Requires installation of the `gganimate` and `transformr` R packages.
`reverse_bounds`	Whether to plot data using the original bounds in the data (i.e. not 0 and 1).
`facet_scales`	The option passed on to the `facet_wrap` function in `ggplot2` for the type of scale for facetting if passing a variable for `group`. Defaults to `“fixed”` scales but can be set to `“free_y”` to allow probability density/bar count scales to vary or `“free”` to allow both x and y axes to vary (i.e., also outcome axis ticks).

Value

If "both", prints both plots and returns a list of both plots as ggplot2 objects. Otherwise, prints and returnst the specific plot as a ggplot2 object.

Examples

library(ordbetareg)


# need a fitted ordbetareg model

data("ord_fit_mean")

out_plots <- pp_check_ordbeta(ord_fit_mean)

# view discrete bar plot

out_plots$discrete

# view continuous density plot

out_plots$continuous

# change title using ggplot2 ggtitle function

out_plots$discrete + ggplot2::ggtitle("New title")