I tried lrm in library(Design) but there is always some error message. Is this function really doing the weighted logistic regression as maximizing the following likelihood:
\sum w_i*(y_i*\beta*x_i-log(1+exp(\beta*x_i)))
Does anybody know a better way to fit this kind of model in R?
FYI: one example of getting error message is like:
x=runif(10,0,3) y=c(rep(0,5),rep(1,5)) w=rep(1/10,10) fit=lrm(y~x,weights=w)
Warning message: currently weights are ignored in model validation and
bootstrapping lrm fits in: lrm(y ~ x, weights = w)
although the model can be fit, the above output warning makes me uncomfortable. Can anybody explain about it a little bit?
The message means exactly what it says. Model validation in Design currently cannot incorporate weights for lrm. Everything else is OK.
Frank Harrell
Best wishes, Feixia
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Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
