[R] How to obtain individual log-likelihood value from glm?

[John Smith]

Thanks for very insightful thoughts. What I am trying to achieve with the
weights is actually not new, something like
https://stats.stackexchange.com/questions/44776/logistic-regression-with-weighted-instances.
I thought my inquiry was not too strange, and I could utilize some existing
codes. It is just an optimization problem at the end of day, or not? Thanks

On Sat, Aug 29, 2020 at 9:02 AM John Fox <jfox using mcmaster.ca> wrote:

> Dear John,
>
> On 2020-08-29 1:30 a.m., John Smith wrote:
> > Thanks Prof. Fox.
> >
> > I am curious: what is the model estimated below?
>
> Nonsense, as Peter explained in a subsequent response to your prior
> posting.
>
> >
> > 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
>
> What sense would that make? WLS is meant to account for non-constant
> error variance in a linear model, but in a binomial GLM, the variance is
> purely a function for the mean.
>
> If you had binomial (rather than binary 0/1) observations (i.e.,
> binomial trials exceeding 1), then you could account for overdispersion,
> e.g., by introducing a dispersion parameter via the quasibinomial
> family, but that isn't equivalent to variance weights in a LM, rather to
> the error-variance parameter in a LM.
>
> I guess the question is what are you trying to achieve with the weights?
>
> Best,
>   John
>
> >
> >> On Aug 28, 2020, at 10:51 PM, John Fox <jfox using 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 <pdalgd using 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 <jswhct using 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 using 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.mes using cbs.dk  Priv: PDalgd using gmail.com
> >>>>
> >>>>
> >>>>
> >>>>
> >>>>
> >>>>
> >>>>
> >>>>
> >>>>
> >>>>
> >>>     [[alternative HTML version deleted]]
> >>> ______________________________________________
> >>> R-help using 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.
>

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