Alternatively you might use log(p/1-p) as your dependent variable and use OLS with robust standard errors. Much of your inference would be analogous to a logistic regression
John C Frain 3 Aranleigh Park Rathfarnham Dublin 14 Ireland www.tcd.ie/Economics/staff/frainj/home.html mailto:fra...@tcd.ie mailto:fra...@gmail.com On 23 January 2016 at 20:46, David Winsemius <dwinsem...@comcast.net> wrote: > > > On Jan 23, 2016, at 12:41 PM, pari hesabi <statistic...@hotmail.com> > wrote: > > > > Hello everybody, > > > > I am trying to fit a logistic regression model by using glm() function > in R. My response variable is a sample proportion NOT binary numbers(0,1). > > So multiply the sample proportions (and 1-proportions) by the number of > samples, round to integers, you will have an appropriate response variable > and complements, and you can fit a binomial model. > > > > > Regarding glm() function, I receive this error: non integer # successes > in a binomial glm! > > > > I would appreciate if anybody conducts me. > > > > > > Regards, > > > > Pari > > > > [[alternative HTML version deleted]] > -- > > David Winsemius > Alameda, CA, USA > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > 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. > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.