Hi I am carrying out some logit regressions and want to (a) make sure I'm taking the right approach and (b) work out how to carry out some additional analysis. So, to carry out a logit regression where the dependent variable is a factor db, I use something like:
res1_l <- glm(formula = db ~ y1 + + y5, family = binomial(link = "logit")) summary(res1_l) ...which is, I hope correct. I also need to carry out an ordered logit regression. Is this as simple as: res1_l <- polr(formula = db ~ y1 + + y5) summary(res1_l) ..with db being a factor which has more levels than just "0" and "1"? Assuming it is, the part I am really struggling with is the calculation of robust standard errors to allow for clustering. In an "ordinary" regression, Ive used survreg, where the data has also been censored, e.g.: res1 <- survreg(formula = Surv(ip, db_Censor) ~ y1 + y5 + cluster(db_ID), dist = "gaussian") summary(res1) This has the benefit of giving a nice clear display of the naïve standard error as well as the robust one - is there any way of getting similar output for a logit and an ordered logit regression Thanks in advance for your help. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
