问题
A newbie question: does anyone know how to run a logistic regression with clustered standard errors in R? In Stata it's just logit Y X1 X2 X3, vce(cluster Z), but unfortunately I haven't figured out how to do the same analysis in R. Thanks in advance!
回答1:
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.
回答2:
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!
回答3:
There is a command glm.cluster in the R package miceadds which seems to give the same results for logistic regression as Stata does with the option vce(cluster). See the documentation here.
In one of the examples on this page, the commands
mod2 <- miceadds::glm.cluster(data=dat, formula=highmath ~ hisei + female,
cluster="idschool", family="binomial")
summary(mod2)
give the same robust standard errors as the Stata command
logit highmath hisei female, vce(cluster idschool)
e.g. a standard error of 0.004038 for the variable hisei.
来源：https://stackoverflow.com/questions/16498849/logistic-regression-with-robust-clustered-standard-errors-in-r