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rregressionstata

Logistic regression with robust clustered standard errors in R

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!

Solution

  • 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.