Briefly, you shouldn't. One way of seeing it is if you switch the model to y~1,
you still get logLik==0.
The root cause is the rounding in binomial()$aic:
> binomial()$aic
function (y, n, mu, wt, dev)
{
m <- if (any(n > 1))
n
else wt
-2 * sum(ifelse(m > 0, (wt/m), 0) * dbinom(round(m * y),
round(m), mu, log = TRUE))
}
which, if wt is small enough ends up calculating dbinom(0, 0, p, log=TRUE)
which is zero.
(Not rounding gives you NaN, because you're trying to fit a model with a
non-integer number of observations.)
-pd
> On 29 Aug 2020, at 03:28 , 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
>
>
>
>
>
>
>
>
>
--
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
______________________________________________
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.
