posterior_prp: Posterior Predictive Replication p-value Calculation

In PRP: Bayesian Prior and Posterior Predictive Replication Assessment

Description

Posterior Predictive Replication p-value Calculation

Usage

posterior_prp(
  beta,
  se,
  L = 1000,
  r_vec = c(0, 8e-04, 0.006, 0.024),
  test = Q,
  print_test_dist = FALSE
)

Arguments

beta	A vector, containing the estimates in the original study and the replication study.
se	A vector, containing the standard errors of the estimates in the original study and the replication study.
L	A value, determining the times of repeating simulation.
r_vec	A vector, defining the prior reproducible model. Each r value corresponds to a probability of sign consistency.
test	A function designed to calculate the test quantity, the default one is the Cochran's Q test statistics.
print_test_dist	A boolean, determining whether the simulated test statistics value difference will be plot as a histogram or not. Default is False.

Value

A list with the following components:

grid	Detailed grid values for the hyperparameters.
test_statistics	The test statistics used in calculating the replication p-value.
n_sim	The L value.
test_stats_dif	The difference between the simulated test statistics quantity and the original value.
pvalue	The resulting posterior predictive replicaiton p-value.

Examples

data("mortality")
res = posterior_prp(beta = mortality$beta, se = mortality$se, test=Q)
names(res)
print(res$pvalue)

PRP documentation built on Dec. 13, 2021, 9:06 a.m.