How to obtain individual log-likelihood value from glm?

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 at 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 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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