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

John Smith j@whct @end|ng |rom gm@||@com
Sat Aug 29 03:28:12 CEST 2020


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]]



More information about the R-help mailing list