[R] propensity score adjustment using R

[Bunny, lautloscrew.com]

Hi all,

i am looking to built a simple example of a very basic propensity  
score adjustment, just using the estimated propensity scores as  
inverse probability weights (respectively 1-estimated weights for the  
non-treated). As far as i understood, MLE predictions of a logit model  
can directly be used as to estimates of the propensity score.
I already considered the twang package and the several matching  
approaches and i am basically not trying to reinvent the wheel. Often  
i could not understand what was going, and why some iterative process  
like k.stat.max were taking so long.
Anyway i´d really like to something really simple apart from all this  
focus on some iterative algorithm thats beyond my scope.

And here is where the problem starts. Most textbooks i considered  
proposed to estimate a simple logit model by ML Estimation. Obviously  
the standard approach to do it using R is glm. The zelig package  
provides an alternative.  My logit model is as simple at its gets:  
Y~X, where Y is a treament vector and X is matrix of some covariates.

I wonder right now if te glm respectively summary(glm(...)) puts out  
something comparable to ML estimates that can be used as the estimated  
pscores, in such a way that there is one value for every observation.


Thanks for any help in advance