Hi Folks, This should have been simple to answer, but despite much chasing I don't seem able to catch this particular mouse!
Basically (somewhat simplified): I have a binary response variable Y (0/1) and a 2-level factor A (0/1). I want to assign a contrast to A such that, when I run summary(glm(Y~A, family=binomial))$coef the Intercept coefficient is the result that I would get from running glm(Y ~ 1), and the "A" coefficient is the log(odds ratio) of the "A=1" results relative to all the data. Explicitly: if p = sum(Y==1)/length(Y), and p1 = sum(Y[A==1]==1)/length(Y[A==1]) then: Intercept: log(p/(1-p)) Coeff of A: log((p1/(1-p1))/(p/(1-p))) (The objective is to assess whether a covariate that may result in only observing a subset of the data would produce a significant selection bias in the result). I know I could derive the result from manipulation of the output from a standard contrast function, but I would prefer to delegate it all (including calculation of SE and P-value) to a run of glm(). With thanks, Ted. -------------------------------------------------------------------- E-Mail: (Ted Harding) <[EMAIL PROTECTED]> Fax-to-email: +44 (0)870 094 0861 Date: 10-Jan-08 Time: 21:33:53 ------------------------------ XFMail ------------------------------ ______________________________________________ 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.
