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 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 Rao 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”.
You can install the development version of raoBust from GitHub with:
# install.packages("devtools")
devtools::install_github("statdivlab/raoBust")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- Creator, maintainer: Amy D Willis
- Author: Sarah Teichman
- Author: David S Clausen
- Author: Shirley Mathur
All errors are Amy’s fault.