Aiste Aistike <aiste.aistike <at> gmail.com> writes: > > Hello R-users, > > I do not have much knowledge about generalized linear models therefore my > question maybe quite stupid. > > I have data from 20 towns with their population and number of people with an > illness from those towns. I would like to use glm function in R so I > can calculate proportions of ill people (and later on produce confidence > intervals). I also want to compare those with original proportions of ill > people. > > If I use: > > model1 <- glm(ill ~ offset(log(total)), family = poisson) > # ill - number of people with illness > #total - total number of people > > with predict.glm I could get number of people (count data), but not the > proportions. If the obtained number I divide by 'total', I get the same > proportion for everyone. But what I want is a way of modeling proportions. > This probably requires to fit a different model but my lack of knowledge > isn't helping here. >
Not stupid -- but -- wouldn't a binomial model glm(cbind(ill,total-ill) ~ 1, family=binomial) make more sense? Read ?predict.glm carefully to determine whether you are predicting responses on the linear predictor (=log-odds) scale or the original scale Ben Bolker ______________________________________________ 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.
