RAB: Compute the relative absolute bias of multiple estimators

In SimDesign: Structure for Organizing Monte Carlo Simulation Designs

Compute the relative absolute bias of multiple estimators

Description

Computes the relative absolute bias given the bias estimates for multiple estimators.

Usage

RAB(x, percent = FALSE, unname = FALSE)

Arguments

x	a numeric vector of bias estimates (see bias), where the first element will be used as the reference
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 absolute bias ratios indicating the relative bias effects compared to the first estimator. Values less than 1 indicate better bias estimates than the first estimator, while values greater than 1 indicate worse bias 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(5000, 1)
bias1 <- bias(samp1, pop)
samp2 <- rnorm(5000, 1)
bias2 <- bias(samp2, pop)

RAB(c(bias1, bias2))
RAB(c(bias1, bias2), percent = TRUE) # as a percentage

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