hbsaems (version 0.1.1)

hbpc: hbpc : Hierarchical Bayesian Prior Predictive Checking

Description

This function facilitates prior predictive checking for Bayesian models. It is primarily used to visualize and summarize the implications of the chosen priors by examining distributions generated from the priors alone.

Usage

hbpc(model, data, response_var, ndraws_ppc = 50)

Value

A list of class hbpc_results containing:

prior_summary

Summary of the priors used in the model (obtained from brms::prior_summary(model)).

prior_predictive_plot

A ggplot object from brms::pp_check comparing the observed data (if available and appropriate) with draws from the prior predictive distribution.

prior_draws_summary

(Optional) A summary of the parameter draws from the prior-only model, which can indicate the range and central tendency implied by the priors.

Arguments

model

A brmsfit object, which is the fitted Bayesian model from brms. Ideally, for prior predictive checks, this model should be fitted with the argument sample_prior = "only".

data

The data that was used (or would be used) to fit the model. Required by pp_check for comparing with observed data, though for sample_prior = "only", the focus is on y_rep.

response_var

A character string specifying the name of the response variable in the data. This is needed for pp_check to correctly identify the observed data y.

ndraws_ppc

An integer specifying the number of draws to use for the posterior predictive check (pp_check) plot. Default is 50.

Author

Achmad Syahrul Choir and Saniyyah Sri Nurhayati

Details

Hierarchical Bayesian Prior Predictive Checking

References

Bürkner, P. C. (2017). brms: An R package for Bayesian multilevel models using Stan. Journal of Statistical Software, 80(1), 1-28. Gabry,J., Simpson,D., Vehtari, A., Betancourt, M., Gelman, A., Visualization in Bayesian Workflow, Journal of the Royal Statistical Society Series A: Statistics in Society, Volume 182, Issue 2, February 2019, Pages 389–402

Examples

# \donttest{
# This is a conceptual example. Actual usage requires a brms model
# fitted with sample_prior = "only".

library(brms)
 # Assume 'data_df' is your data frame with 'response_variable' and 'predictor_variable'
 data_df <- data.frame(
 response_variable = rnorm(100),
 predictor_variable = rnorm(100)
 )

 # Define some priors
 example_priors <- c(prior(normal(0, 10), class = "b"),
 prior(cauchy(0, 1), class = "sigma"))
#
 # Fit a model with sample_prior = "only"
 # Note: For actual prior predictive checks, you'd use your actual model 
 # formula and data structure.
 # The data itself isn't used for fitting parameters when sample_prior = "only",
 # but its structure (like number of observations) can influence y_rep.
 fit_prior_only <- try(brm(
 bf(response_variable ~ predictor_variable),
 data = data_df,
 prior = example_priors,
 sample_prior = "only", # Crucial for prior predictive checks
 chains = 1, iter = 1000, warmup = 200, refresh = 0,
 control = list(adapt_delta = 0.8)
 ))
#
 if (!inherits(fit_prior_only, "try-error")) {
# # Perform prior predictive checking
 prior_check_output <- hbpc(
 model = fit_prior_only,
 data = data_df,
 response_var = "response_variable", # Specify the response variable name
 ndraws_ppc = 50
 )
#
 # Print the summary of priors
 print(prior_check_output$prior_summary)

 # Display the prior predictive plot
 print(prior_check_output$prior_predictive_plot)

 # Print summary of parameter draws from prior
 print(prior_check_output$prior_draws_summary)
 }
# }