mcstatsim (version 0.5.0)

calc_relative_rmse: Calculate Relative Root Mean Squared Error and its Monte Carlo Standard Error

Description

Computes the Relative Root Mean Squared Error (Relative RMSE) of a set of estimates with respect to a true parameter value. The Relative RMSE is derived from the Relative Mean Squared Error (MSE), providing a scale-independent measure of error that facilitates comparisons across different scales of the true parameter. This function is especially useful for evaluating the accuracy of estimates when the magnitude of the true parameter varies significantly across different scenarios. The function gracefully handles cases where the true parameter is zero by returning `NA` for both Relative RMSE and its MCSE, to avoid division by zero. The MCSE for the Relative RMSE is not directly computed in this function and is marked as a placeholder for future implementation.

Usage

calc_relative_rmse(estimates, true_param)

Value

A list with two components: `rel_rmse`, the calculated Relative Root Mean Squared Error of the estimates, and `rel_rmse_mcse`, the Monte Carlo Standard Error of the Relative RMSE. The MCSE is currently not calculated and returned as `NA`. This is a placeholder for future implementation.

Arguments

estimates

A numeric vector of estimates from a simulation or sampling process.

true_param

The true parameter value that the estimates are intended to approximate. Note that `true_param` must not be zero, as the calculation involves division by the true parameter value.

Examples

estimates <- rnorm(100, mean = 50, sd = 10)
true_param <- 50 # Non-zero true parameter
relative_rmse_info <- calc_relative_rmse(estimates, true_param)
print(relative_rmse_info)