Jan,
It looks like you did not understand the line "For a binomial GLM prior
weights are used to give the number of trials when the response is the
proportion of successes."
Weights must be a number of trials (hence integer). Not a proportion of
a population. Here is an example that clarifies the use of weights.
library(boot)
library(reshape)
dataset <- data.frame(Person = c(rep("A", 20), rep("B", 10)), Success =
c(rbinom(20, 1, 0.25), rbinom(10, 1, 0.75)))
Aggregated <- cast(Person ~ ., data = dataset, value = "Success", fun =
list(mean, length))
m0 <- glm(Success ~ 1, data = dataset, family = binomial)
m1 <- glm(mean ~ 1, data = Aggregated, family = binomial, weights =
length)
inv.logit(coef(m0))
inv.logit(coef(m1))
Have a look at the survey package is you want to analyse stratified
data.
Thierry
------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek
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Research Institute for Nature and Forest
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tel. + 32 54/436 185
thierry.onkel...@inbo.be
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> -----Oorspronkelijk bericht-----
> Van: r-help-boun...@r-project.org
> [mailto:r-help-boun...@r-project.org] Namens Jan van der Laan
> Verzonden: vrijdag 16 april 2010 14:11
> Aan: r-help@r-project.org
> Onderwerp: [R] Weights in binomial glm
>
> I have some questions about the use of weights in binomial
> glm as I am not getting the results I would expect. In my
> case the weights I have can be seen as 'replicate weights';
> one respondent i in my dataset corresponds to w[i] persons in
> the population. From the documentation of the glm method, I
> understand that the weights can indeed be used for this: "For
> a binomial GLM prior weights are used to give the number of
> trials when the response is the proportion of successes."
> >From "Modern applied statistics with S-Plus 3rd ed." I understand the
> same.
>
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