Do you have the sample sizes that the sample proportions were computed from (e.g. 0.5 could be 1 out of 2 or 100 out of 200)?
If you do then you can specify the model with the proportions as the y variable and the corresponding sample sizes as the weights argument to glm. If you only have proportions without an integer sample size then you may want to switch to using beta regression instead of logistic regression. On Sat, Jan 23, 2016 at 1:41 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). > > 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]] > > ______________________________________________ > 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. -- Gregory (Greg) L. Snow Ph.D. 538...@gmail.com ______________________________________________ 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.