How to obtain individual log-likelihood value from glm?

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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