Skip to contents

Compute the relative efficiency of multiple estimators

Source: R/summary_functions.R
RE.Rd

Computes the relative efficiency given the RMSE (default) or MSE values for multiple estimators.

Usage

RE(x, MSE = FALSE, percent = FALSE, unname = FALSE)

Arguments

x

a numeric vector of root mean square error values (see RMSE), where the first element will be used as the reference. Otherwise, the object could contain MSE values if the flag MSE = TRUE is also included

MSE

logical; are the input value mean squared errors instead of root mean square errors?

percent

logical; change returned result to percentage by multiplying by 100? Default is FALSE

unname

logical; apply unname to the results to remove any variable names?

Value

returns a vector of variance ratios indicating the relative efficiency compared to the first estimator. Values less than 1 indicate better efficiency than the first estimator, while values greater than 1 indicate worse efficiency than the first estimator

References

Chalmers, R. P., & Adkins, M. C. (2020). Writing Effective and Reliable Monte Carlo Simulations with the SimDesign Package. The Quantitative Methods for Psychology, 16(4), 248-280. doi:10.20982/tqmp.16.4.p248

Sigal, M. J., & Chalmers, R. P. (2016). Play it again: Teaching statistics with Monte Carlo simulation. Journal of Statistics Education, 24(3), 136-156. doi:10.1080/10691898.2016.1246953

Author

Phil Chalmers rphilip.chalmers@gmail.com

Examples


pop <- 1
samp1 <- rnorm(100, 1, sd = 0.5)
RMSE1 <- RMSE(samp1, pop)
samp2 <- rnorm(100, 1, sd = 1)
RMSE2 <- RMSE(samp2, pop)

RE(c(RMSE1, RMSE2))
#> [1] 1.000000 4.072193
RE(c(RMSE1, RMSE2), percent = TRUE) # as a percentage
#> [1] 100.0000 407.2193

# using MSE instead
mse <- c(RMSE1, RMSE2)^2
RE(mse, MSE = TRUE)
#> [1] 1.000000 4.072193