Thanks Prof. Fox. I am curious: what is the model estimated below?
I guess my inquiry seems more complicated than I thought: with y being 0/1, how to fit weighted logistic regression with weights <1, in the sense of weighted least squares? Thanks > On Aug 28, 2020, at 10:51 PM, John Fox <j...@mcmaster.ca> wrote: > > Dear John > > I think that you misunderstand the use of the weights argument to glm() for a > binomial GLM. From ?glm: "For a binomial GLM prior weights are used to give > the number of trials when the response is the proportion of successes." That > is, in this case y should be the observed proportion of successes (i.e., > between 0 and 1) and the weights are integers giving the number of trials for > each binomial observation. > > I hope this helps, > John > > John Fox, Professor Emeritus > McMaster University > Hamilton, Ontario, Canada > web: https://socialsciences.mcmaster.ca/jfox/ > >> On 2020-08-28 9:28 p.m., John Smith wrote: >> If the weights < 1, then we have different values! See an example below. >> How should I interpret logLik value then? >> set.seed(135) >> y <- c(rep(0, 50), rep(1, 50)) >> x <- rnorm(100) >> data <- data.frame(cbind(x, y)) >> weights <- c(rep(1, 50), rep(2, 50)) >> fit <- glm(y~x, data, family=binomial(), weights/10) >> res.dev <- residuals(fit, type="deviance") >> res2 <- -0.5*res.dev^2 >> cat("loglikelihood value", logLik(fit), sum(res2), "\n") >>> 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. ______________________________________________ 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.