raoBust: Robust Rao (score) tests for Generalized Linear Models

raoBust, at its core, gives you all the important information from glm(), but also with model misspecification-robust Rao tests (also called score tests) and Wald tests.

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’s shocking to me (Amy) how well they perform. They have a reputation for being conservative in small samples, but I would argue that this is a very good thing.

For now (because it’s what I need for my work) I only have implementations for Poisson GLMs with log links. If you have another specific case you’d like to request, please let me know at Issues and label it as a “feature request”.

Installation

You can install the development version of raoBust from GitHub with:

# install.packages("devtools")
devtools::install_github("statdivlab/raoBust")

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)
glm_test(dist ~ speed, data = cars, family=poisson(link="log"))
#>               Estimate Non-robust Std Error Robust Std Error Non-robust Wald p
#> (Intercept) 2.15096109          0.081774352      0.180014289     1.743527e-152
#> speed       0.09650242          0.004404885      0.009234056     2.177435e-106
#>             Robust Wald p Robust Score p
#> (Intercept)             0   0.0406316810
#> speed                   0   0.0000472766

People

This software project was started by Amy D Willis, with input and insights from former lab member David S Clausen. Sarah Teichman is an amazing collaborator and coauthor.

All errors are Amy’s fault.