Hii: It's been a long time but John Fox's "Companion to Appied Regression"
book has the expressions
for the likelihood of the binomial glm. ( and probably the others also ).
Just running logLik is not so useful
because it could be leaving out multiplicative factors. If you can get your
hands on any edition of John's book,
I remember it being quite helpful in terms of providing all of the gory
details and for understanding GLM's in
general.
On Fri, Aug 28, 2020 at 9:28 PM John Smith <jsw...@gmail.com> 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
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> 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]]
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