Hi Daniel, Thanks for your reply. The weight is dependent on the estimated E(Y). In other words, I need R to estimate the beta coefficients and weights simultaneously, like what is performed in gls(). However, the weight form allowed in gls() is different from what I want.
In SPSS, we can simply use the code of 'COMPUTE WGT = 1/(yhat * (1 - yhat))'. But I do not know how to do it in R. I tried yhat but R did not recognize it. Best Regards, Vivian On Tue, Jun 28, 2011 at 10:18 PM, Daniel Malter <dan...@umd.edu> wrote: > You can specify the weights=... argument in the lm() function as vector of > weights, one for each observation. Should that not do what your are trying > to do? > > HTH, > Daniel > > -- > View this message in context: > http://r.789695.n4.nabble.com/a-Weighted-Least-Square-Model-for-a-Binary-Outcome-tp3631551p3631834.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.
