Computes the relative efficiency given the RMSE (default) or MSE values for multiple estimators.
RE(x, MSE = FALSE, percent = FALSE, unname = FALSE)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
logical; are the input value mean squared errors instead of root mean square errors?
logical; change returned result to percentage by multiplying by 100? Default is FALSE
logical; apply unname to the results to remove any variable
names?
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
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))
#> [1] 1.000000 4.171941
RE(c(RMSE1, RMSE2), percent = TRUE) # as a percentage
#> [1] 100.0000 417.1941
# using MSE instead
mse <- c(RMSE1, RMSE2)^2
RE(mse, MSE = TRUE)
#> [1] 1.000000 4.171941