Hi Peter, Thank you very much for your help! However, for my dataset, it may not asymptotically work. May I ask whether you know how to define a new family?
Thank you very much again! Best, Amanda 2014-10-27 18:27 GMT-04:00 peter dalgaard <pda...@gmail.com>: > The likelihood for the geometric distribution is the same as for the > binomial distribution, except for the constant term, so estimates and LRT > will be the same. The properties of the estimator will be different, e.g. > the estimate of p is not unbiased, but asymptotically the likelihood > procedures should work (asymptotic in this case means a reasonably large > total number of both successes and failures, I suppose.) > > So, if your geometric variate is called y, with the R convention of > counting the number of failures (not number of experiments), it should work > with > > glm(cbind(1,y) ~ whatever, family="binomial") > > [The likelihood equivalence is fairly well-known in statistical theory as > a counterargument to the strong likelihood principle that all inference > should be based solely on the likelihood function.] > > - Peter D. > > > On 27 Oct 2014, at 22:29 , Amanda Li <amand...@uchicago.edu> wrote: > > > > Hello, > > > > I was trying to apply "glm" to a dataset that assumes geometric > > distribution. I cannot use "glm.nb" in MASS package (negative.binomial > (1)) > > because it tries to estimate this "1" while I am interested in "p", the > > probability of success. Does anyone know how I can define a geometric > > distribution within "family" so that I can use glm assuming geometric > > distribution to estimate "p"? > > > > I am not sure how "quasi" within the family works in this case and I am > not > > sure whether it can be used to assume geometric distribution. > > > > Thanks in advance for your help! I really appreciate it! > > Best regards, > > Amanda > > > > [[alternative HTML version deleted]] > > > > ______________________________________________ > > 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. > > -- > Peter Dalgaard, Professor, > Center for Statistics, Copenhagen Business School > Solbjerg Plads 3, 2000 Frederiksberg, Denmark > Phone: (+45)38153501 > Email: pd....@cbs.dk Priv: pda...@gmail.com > > > > > > > > > [[alternative HTML version deleted]] ______________________________________________ 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.
