simhelpers (version 0.3.1)

calc_relative_var: Calculate jack-knife Monte Carlo SE for variance estimators

Description

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"),
  winz = Inf,
  var_winz = winz
)

Value

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

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, with possible options "relative bias", "relative mse", and "relative rmse".

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.

var_winz

numeric value for winsorization constant for the variance estimates. If set to a finite value, variance 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 var_winz = 3 will truncate variance estimates that fall below P25 - 3 * IQR or above P75 + 3 * IQR. By default var_winz is set to the same constant as winsorize.

Examples

calc_relative_var(data = alpha_res, estimates = A, var_estimates = Var_A)