R help - Jan 2008 - Logit Regressions, Clustering etc

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, I?ve 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 na?ve 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.

Hi

Sorry to bother the list again, but no-one has so far been able to suggest
any help for the query below.  As an added incentive, I have been asked
"why
don't you do this in Stata? It's just a case of adding a flag in the
regression..."

I'm loathe to start learning another stats package, so if anyone is able to
help...!

Thanks

Paul

>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, I?ve 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 na?ve 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.
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