Calculate jack-knife Monte Carlo SE for variance estimators

Calculates relative bias, mean squared error (relative mse), and root mean squared error (relative rmse) of variance estimators. The function also calculates the associated jack-knife Monte Carlo standard errors.

Usage

calc_relative_var(
  data,
  estimates,
  var_estimates,
  criteria = c("relative bias", "relative mse", "relative rmse")
)

Arguments

data

data frame or tibble containing the simulation results.

estimates

Vector or name of column from data containing point estimates.

var_estimates

Vector or name of column from data containing variance estimates for point estimator in estimates.

criteria

character or character vector indicating the performance criteria to be calculated.

Value

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

Examples

calc_relative_var(data = alpha_res, estimates = A, var_estimates = Var_A)
#> # A tibble: 1 × 7
#>   K_relvar rel_bias_var rel_bias_var_mcse rel_mse_var rel_mse_var_mcse
#>      <int>        <dbl>             <dbl>       <dbl>            <dbl>
#> 1     1000        0.440             0.101       0.726            0.337
#> # ℹ 2 more variables: rel_rmse_var <dbl>, rel_rmse_var_mcse <dbl>