calc_rmse: Calculate Root Mean Squared Error and its Monte Carlo...

In mcstatsim: Monte Carlo Statistical Simulation Tools Using a Functional Approach

Calculate Root Mean Squared Error and its Monte Carlo Standard Error

Description

Computes the Root Mean Squared Error (RMSE) of a set of estimates relative to a true parameter value, along with the Monte Carlo Standard Error (MCSE) for the RMSE. The RMSE is a measure of the accuracy of the estimates, representing the square root of the average squared differences between the estimated values and the true parameter. The MCSE for the RMSE is calculated using jackknife estimates, providing an assessment of the uncertainty associated with the RMSE value.

Usage

calc_rmse(estimates, true_param)

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.

Value

A list with two components: 'rmse', the calculated Root Mean Squared Error of the estimates, and 'rmse_mcse', the Monte Carlo Standard Error of the RMSE. This MCSE is derived from jackknife estimates, offering insight into the reliability of the RMSE calculation.

Examples

estimates <- rnorm(100, mean = 50, sd = 10)
true_param <- 50
rmse_info <- calc_rmse(estimates, true_param)
print(rmse_info)

mcstatsim documentation built on Sept. 11, 2024, 5:35 p.m.