logistic regression: wls and unbalanced samples

Greetings from Rio de Janeiro, Brazil.

I am looking for advice / references on binary logistic regression
with weighted least squares (using lrm & weights), on the following
context:

1) unbalanced sample (n0=10000, n1=700);
2) sampling weights used to rebalance the sample (w0=1, w1=14.29); e
3) after modelling, adjust the intercept in order to reflect the
expected % of 1?s in the population (e.g., circa 7%, as opposed to
50%).

??

If the proportion of 1 in the population is about 7%, how exactly is the sample "unbalanced". I don't see a reason to use weights at all if the sample is representative of the population. The opposite situation, where the sample is balanced (e.g. case-control), the population not, and you are interested in the population values, _that_ might require weighting, with some care because case weighting and sample weighting are two different things so the s.e. will be wrong. That sort of stuff handled by the survey package. 

However what you seem to be doing is to create results for an artificial 50/50 population, then project back to the population you were sampling from all along. I don't think this makes sense at all.

Peter Dalgaard
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Email: pd.mes at cbs.dk  Priv: PDalgd at gmail.com