Compute the relative efficiency of multiple estimators
Computes the relative efficiency given the RMSE (default) or MSE values for multiple estimators.
RE(x, MSE = FALSE, percent = FALSE, unname = FALSE)
x |
a |
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 |
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
Phil Chalmers rphilip.chalmers@gmail.com
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
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)) RE(c(RMSE1, RMSE2), percent = TRUE) # as a percentage # using MSE instead mse <- c(RMSE1, RMSE2)^2 RE(mse, MSE = TRUE)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.