raoBust, at its core, gives you all the important information from glm(), but also with robust score tests. Robust score tests are robust (in error rate control) to many forms of model misspecification.
Robust score tests have outstanding error rate performance in small samples, and when your data is not drawn from a parametric family (i.e., always). It is shocking how well they perform. They are generally conservative in small samples, which is a very good thing. You should err on conservative when you have few samples. Most other tests are anti-conservative in small samples.
We currently have score tests for coefficients in Poisson GLMs (log link), Binomial GLMs (logit link), and Multinomial GLMs (log link), including for linear combinations of parameters and simultaneous testing (“ANOVA”). If you have another specific case you’d like to request, please let us know at Issues and label it as a “feature request”.
This package also implements robust Wald tests. Many other packages implement robust Wald tests – it is general methodology for robust score tests that are the unique contribution of this package.
Installation
You can install the development version of raoBust from GitHub with:
# install.packages("devtools")
devtools::install_github("statdivlab/raoBust")If it would be helpful for you for this package to be on CRAN, please let us know.
Example
This is a really silly example to show you the syntax. It looks just like glm() on the input side, but outputs a summary table that includes robust Wald and Rao tests as well as others.
library(raoBust)
#> Registered S3 methods overwritten by 'geeasy':
#> method from
#> drop1.geeglm MESS
#> drop1.geem MESS
#> plot.geeglm geepack
glm_test(dist ~ speed, data = cars, family=poisson(link="log"))
#>
#> Call:
#> glm(dist ~ speed, data = cars, family = poisson(link = "log"))
#>
#>
#> Coefficient estimates:
#> Estimate Non-robust Std Error Robust Std Error Lower 95% CI
#> (Intercept) 2.15096109 0.081774352 0.180014289 1.798140
#> speed 0.09650242 0.004404885 0.009234056 0.078404
#> Upper 95% CI Non-robust Wald p Robust Wald p Robust Score p
#> (Intercept) 2.5037826 1.743527e-152 0 4.945013e-07
#> speed 0.1146008 2.177435e-106 0 3.956496e-05Documentation
We additionally have a pkgdown website that contains pre-built versions of our function documentation and an introductory vignette.
People
- Creator, maintainer: Amy D Willis
- Author: Sarah Teichman
- Author: David S Clausen
- Author: Shirley Mathur
All errors are Amy’s fault.
Naming
Score tests were pioneered by C.R. Rao, an outstanding statistician and methodologist. Score tests are sometimes, but increasingly rarely, called Rao tests. While Rao’s work focused on non-robust score tests (not robust score tests) it is in tribute to Rao that this package is named. Many thanks to David Clausen for proposing and sharing the clever portmanteau `raoBust’ for a package implementing robust score tests.
Comment from Amy People of Color are consistently undervalued in science and mathematics, including in statistical methodology. The foundations of statistical methodology were built on the desire to quantitatively show the inferiority of People of Color and Jews. I believe that white supremacy continues to manifest in what names we give methods: we are more likely to name a method after a person if they are White, and more likely to name a method after its purpose, details or another acronym if the developer was a Person of Color. For this reason, I try to call score tests “Rao tests”. I invite you to join me.