bayesassurance (version 0.1.0)

bayes_adcock: Bayesian Assurance Computation in the Precision-Based Setting

Description

Returns the Bayesian assurance of observing that the absolute difference between the true underlying population parameter and the sample estimate falls within a margin of error no greater than a fixed precision level, d.

Usage

bayes_adcock(
  n,
  d,
  mu_beta_a,
  mu_beta_d,
  n_a,
  n_d,
  sig_sq,
  alpha,
  mc_iter = 1000
)

Arguments

n

sample size (either vector or scalar).

d

fixed precision level

mu_beta_a

analysis stage mean

mu_beta_d

design stage mean

n_a

sample size at analysis stage. Also quantifies the amount of prior information we have for parameter \(\mu\).

n_d

sample size at design stage. Also quantifies the amount of prior information we have for where the data is being generated from.

sig_sq

known variance \(\sigma^2\).

alpha

significance level

mc_iter

number of MC samples evaluated under the analysis objective

Value

approximate Bayesian assurance under precision-based conditions

Examples

# NOT RUN {
n <- seq(20, 145, 5)
out <- bayes_adcock(n = n, d = 0.20, mu_beta_a = 0.64, mu_beta_d = 0.9,
                      n_a = 20, n_d = 10, sig_sq = 0.265,
                      alpha = 0.05, mc_iter = 1000)
head(out$assurance_table)
out$assurance_plot
# }