Thanks Peter for a very promising tip. On Tue, Aug 25, 2020 at 11:40 AM peter dalgaard <pda...@gmail.com> wrote:
> If you don't worry too much about an additive constant, then half the > negative squared deviance residuals should do. (Not quite sure how weights > factor in. Looks like they are accounted for.) > > -pd > > > On 25 Aug 2020, at 17:33 , John Smith <jsw...@gmail.com> wrote: > > > > Dear R-help, > > > > The function logLik can be used to obtain the maximum log-likelihood > value > > from a glm object. This is an aggregated value, a summation of individual > > log-likelihood values. How do I obtain individual values? In the > following > > example, I would expect 9 numbers since the response has length 9. I > could > > write a function to compute the values, but there are lots of > > family members in glm, and I am trying not to reinvent wheels. Thanks! > > > > counts <- c(18,17,15,20,10,20,25,13,12) > > outcome <- gl(3,1,9) > > treatment <- gl(3,3) > > data.frame(treatment, outcome, counts) # showing data > > glm.D93 <- glm(counts ~ outcome + treatment, family = poisson()) > > (ll <- logLik(glm.D93)) > > > > [[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. > > -- > Peter Dalgaard, Professor, > Center for Statistics, Copenhagen Business School > Solbjerg Plads 3, 2000 Frederiksberg, Denmark > Phone: (+45)38153501 > Office: A 4.23 > Email: pd....@cbs.dk Priv: pda...@gmail.com > > > > > > > > > > [[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.