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Calculate confidence interval coverage, width and MCSE

Source: R/calc_coverage.R
calc_coverage.Rd

Calculates confidence interval coverage and width. The function also calculates the associated Monte Carlo standard errors. The confidence interval percentage is based on how you calculated the lower and upper bounds.

Usage

calc_coverage(
  data,
  lower_bound,
  upper_bound,
  true_param,
  criteria = c("coverage", "width"),
  winz = Inf
)

Arguments

data

data frame or tibble containing the simulation results.

lower_bound

vector or name of column from data containing lower bounds of confidence intervals.

upper_bound

vector or name of column from data containing upper bounds of confidence intervals.

true_param

vector or name of column from data containing corresponding true parameters.

criteria

character or character vector indicating the performance criteria to be calculated, with possible options "coverage" and "width".

winz

numeric value for winsorization constant. If set to a finite value, estimates will be winsorized at the constant multiple of the inter-quartile range below the 25th percentile or above the 75th percentile of the distribution. For instance, setting winz = 3 will truncate estimates that fall below P25 - 3 * IQR or above P75 + 3 * IQR.

Value

A tibble containing the number of simulation iterations, performance criteria estimate(s) and the associated MCSE.

Examples

calc_coverage(data = t_res, lower_bound = lower_bound,
              upper_bound = upper_bound, true_param = true_param)
#> # A tibble: 1 × 5
#>   K_coverage coverage coverage_mcse width width_mcse
#>        <int>    <dbl>         <dbl> <dbl>      <dbl>
#> 1       1000    0.951       0.00683 0.791    0.00179