plot_pp_check: Perform Posterior Predictive Check

In maomlab/BayesPharma: Tools for Bayesian Analysis of Non-Linear Pharmacology Models

Perform Posterior Predictive Check

Description

Given a brms model, perform a graphical posterior predictive check (PPC).

brms::pp_check() has different plot types to analyze the model fit by comparing the observed data with generated data from the model.

Here is the documentation for brms::pp_check: http://paul-buerkner.github.io/brms/reference/pp_check.brmsfit.html

Here is the documentation for bayesplot::pp_check plot types under PPC plotting functions: https://mc-stan.org/bayesplot/reference/PPC-overview.html

Usage

plot_pp_check(model, plot_type = "dens_overlay", n = 50, ...)

Arguments

model	brms::brmsfit model.
plot_type	character for the plot type.
n	numeric number of posterior draws to be used.
...	additional arguments to brms::pp_check().

Value

ggplot2::ggplot object.

Examples

## Not run: 
# Consider a dose response model with the plot type being 10 box plots
BayesPharma::plot_pp_check(
  model = my_dose_response_model,
  plot_type = "box_plot",
  n = 10)

## End(Not run)

maomlab/BayesPharma documentation built on Aug. 24, 2024, 8:45 a.m.