How to obtain individual log-likelihood value from glm?

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 <jfox at 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 at 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 at 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))

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