RE: Compute the relative efficiency of multiple estimators

In SimDesign: Structure for Organizing Monte Carlo Simulation Designs

Compute the relative efficiency of multiple estimators

Description

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

Author(s)

Phil Chalmers [e-mail]

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. \Sexpr[results=rd]{tools:::Rd_expr_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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10691898.2016.1246953")}

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))
RE(c(RMSE1, RMSE2), percent = TRUE) # as a percentage

# using MSE instead
mse <- c(RMSE1, RMSE2)^2
RE(mse, MSE = TRUE)

SimDesign documentation built on Oct. 17, 2023, 5:07 p.m.