Logistic regression with robust clustered standard errors in R

I have been banging my head against this problem for the past two days; I magically found what appears to be a new package which seems destined for great things--for example, I am also running in my analysis some cluster-robust Tobit models, and this package has that functionality built in as well. Not to mention the syntax is much cleaner than in all the other solutions I've seen (we're talking near-Stata levels of clean).

So for your toy example, I'd run:

library(Zelig)
logit<-zelig(Y~X1+X2+X3,data=data,model="logit",robust=T,cluster="Z")

Et voilà!

You might want to look at the rms (regression modelling strategies) package. So, lrm is logistic regression model, and if fit is the name of your output, you'd have something like this:

fit=lrm(disease ~ age + study + rcs(bmi,3), x=T, y=T, data=dataf)

fit

robcov(fit, cluster=dataf$id)

bootcov(fit,cluster=dataf$id)

You have to specify x=T, y=T in the model statement. rcs indicates restricted cubic splines with 3 knots.