Michael <comtech.usa <at> gmail.com> writes: > > Hi all, > > As you can see from below, the result is strange... > > I would imagined that the bb result should be much higher and close to 1, > any way to improve the fit? > > Any other classification methods? > > Thank you! > > data=data.frame(y=rep(c(0, 1), times=100), x=1:200) > aa=glm(y~x, data=data, family=binomial(link="logit")) > > newdata=data.frame(x=6, y=100) > bb=predict(aa, newdata=newdata, type="response") > bb > > > bb > > 1 > > 0.4929125 >
I have a feeling you meant to say data <- data.frame(y=rep(c(0,1), each=100), x=1:200) instead. Try with(data,plot(y~x)) for each data set to see what you actually got as opposed to what you thought you were getting it. You may still have a little bit of a problem fitting such an extreme data set -- this is what is called "complete separation", and leads to an infinite estimate of the slope -- if you want to pursue this, take a look at the brglm package. ______________________________________________ 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.
