Compute summary of posterior predictive distribution for the data given posterior distributions of eta and cutpoints

Usage

posterior_predictions_for_response_counts(
  df,
  formula,
  eta,
  cutpoints,
  ci_variable = "P",
  R = NULL,
  CI = 0.97,
  .progress = TRUE
)

Arguments

df: Table with data
formula: Formula that specifies outcome variable, fixed and random effects.
eta: Matrix of samples_n x nrow(df), each row is a single posterior sample
cutpoints: Matrix of samples_n x nlevels(df$outcome) - 1, each row is a single posterior sample
ci_variable: Which variable to use for percentile confidence intervals, either "N" or "P" (default).
R: Number of samples to process, NULL (default) means all samples are used.
CI: Percentile confidence interval, default is 0.97
.progress: Logical, whether to show progress bar during bootstrapping. Default is TRUE.

Value

data.frame or tibble with fixed effects columns, PosteriorMean, PosteriorMedian, PosteriorLowerCI, and PosteriorUpperCI.

Examples

data(aiq)
data(aiq_draws)
eta_draws <- extract_stan_posterior_matrix(aiq_draws, eta)
cutpoints_draws <- extract_stan_posterior_matrix(aiq_draws, cutpoints)
aiq_posterior_avg <-
  posterior_predictions_for_response_counts(aiq,
                                            Response ~ Group + Question,
                                            eta_draws,
                                            cutpoints_draws,
                                            R=10)