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 <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))
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--
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
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______________________________________________
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