# RAB: Compute the relative absolute bias of multiple estimators In SimDesign: Structure for Organizing Monte Carlo Simulation Designs

## Description

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

## Usage

 `1` ```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 rphilip.chalmers@gmail.com

## 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

## Examples

 ```1 2 3 4 5 6 7 8``` ```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 Sept. 5, 2021, 5:23 p.m.