I'm not quite sure what you mean. If all you need is propensity scores to run an IPW analysis, the fitted values should work. Having many binary covariates shouldn't be a problem, the whole point of the propensity score is boiling down many dimensions to a single one. I use matchit() for my psm needs, but it may not be so useful for IPW.
Ben On Thu, Sep 18, 2008 at 11:00 AM, ran2 <[EMAIL PROTECTED]> wrote: > > > > Frank E Harrell Jr wrote: >> >> >> >> That is a high variance procedure as compared with covariate adjustment >> using the propensity score, or stratification. >> >> Frank Harrell >> > Ah, wait what if I got very high dimensional X ? Even with 20 binary > covariates i would end up with more than 1 million possibilities... > -- > View this message in context: > http://www.nabble.com/propensity-score-adjustment-using-R-tp19555722p19557409.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > 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. > ______________________________________________ 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.
