| calc_mse {mcstatsim} | R Documentation |
Calculate Mean Squared Error and its Monte Carlo Standard Error
Description
Computes the Mean Squared Error (MSE) of a set of estimates relative to a true parameter value, along with the Monte Carlo Standard Error (MCSE) for the MSE. The MCSE takes into account the variance, skewness, and kurtosis of the estimates to provide a more accurate measure of uncertainty. This function is useful for assessing the accuracy of simulation or estimation methods by comparing the squared deviations of estimated values from a known parameter.
Usage
calc_mse(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: 'mse', the calculated Mean Squared Error of the estimates, and 'mse_mcse', the Monte Carlo Standard Error of the MSE, offering insight into the reliability of the MSE calculation.
Examples
estimates <- rnorm(100, mean = 50, sd = 10)
true_param <- 50
mse_info <- calc_mse(estimates, true_param)
print(mse_info)