calc_absolute: Calculate absolute performance criteria and MCSE

In meghapsimatrix/SimHelpers: Helper Functions for Simulation Studies

Calculate absolute performance criteria and MCSE

Description

Calculates absolute bias, variance, mean squared error (mse) and root mean squared error (rmse). The function also calculates the associated Monte Carlo standard errors.

Usage

calc_absolute(
  data,
  estimates,
  true_param,
  criteria = c("bias", "variance", "stddev", "mse", "rmse"),
  winz = Inf
)

Arguments

data	data frame or tibble containing the simulation results.
estimates	vector or name of column from data containing point estimates.
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 "bias", "variance", "stddev", "mse", and "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.

Value

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

Examples

calc_absolute(data = t_res, estimates = est, true_param = true_param)

meghapsimatrix/SimHelpers documentation built on Jan. 14, 2025, 5:16 a.m.