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 <j...@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 <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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