HuraultMisc (version 1.1.1)

coverage: Coverage probability

Description

Compute and plot coverage of CI for different confidence level. Useful for fake data check.

Usage

compute_coverage(
  post_samples,
  truth,
  CI = seq(0, 1, 0.05),
  type = c("eti", "hdi")
)
plot_coverage(
  post_samples,
  truth,
  CI = seq(0, 1, 0.05),
  type = c("eti", "hdi")
)

Arguments

post_samples

Matrix of posterior samples. Rows represent a sample and columns represent variables.

truth

Vector of true parameter values (should be the same length as the number of columns in post_samples).

CI

Vector of confidence levels.

type

Type of confidence intervals: either "eti" (equal-tailed intervals) or "hdi" (highest density intervals).

Value

compute_coverage returns a Dataframe containing coverage (and 95% uncertainty interval for the coverage) for different confidence level (nominal coverage). plot_coverage returns a ggplot of the coverage as the function of the nominal coverage with 95% uncertainty interval.

Examples

# NOT RUN {
N <- 100
N_post <- 1e3
truth <- rep(0, N)
post_samples <- sapply(rnorm(N, 0, 1), function(x) {rnorm(N_post, x, 1)})

compute_coverage(post_samples, truth)
plot_coverage(post_samples, truth)
# }