I think you should have a look at svyglm() from the survey package.
My two cents Le mercredi 05 février 2014 à 14:41 +1300, Rolf Turner a écrit : > You should direct your inquiry to R-help, not to me personally. I am > taking the liberty of cc-ing my reply back to the list. > > I really haven't the time at the moment to think the issue through > thoroughly, but off the top of my head: If you are going to use > weighted log likelihoods then any comparison of models that you engage > in should involve the *same* weights, otherwise you doing the good old > apples-with-oranges thing. > > So yes, the weights will change the log-likelihood and the AIC. And so > they should. And if you use AIC to compare models which are > meaningfully comparable (id est, have the same weights) this is not a > problem. > > As I say, this is off the top of my head. Others older (???) and wiser > than I may correct me. > > cheers, > > Rolf Turner > > On 05/02/14 11:56, IamRandom wrote: > > I am trying to do weighted Poisson regression. I have count data. > > > > Simple example: > > set.seed(50) > > x=seq(0,1,length=100) > > y=numeric(100) > > y[seq(1,100,by=2)]=round(exp(1.5*x[seq(1,100,by=2)]+rnorm(50,0,.1)),0) > > y[seq(2,100,by=2)]=round(exp(1.5*x[seq(1,100,by=2)]+rnorm(50,0,1)),0) > > weigh1=numeric(100) > > weigh1[seq(1,100,by=2)]=rep(5,50) > > weigh1[seq(2,100,by=2)]=rep(1,50) > > > > > > The -2*loglikelihood of both of these regressions is the same with lm, > > which makes sense. The scaling of the weights does not affect the > > log-likelihood. > > >-2*logLik( lm(y~x, weights=weigh1))[1] > > >-2*logLik( lm(y~x, weights=weigh1/3))[1] > > > > The -2*loglikelihood of these two regressions are different with glm: > > > -2*logLik(glm(y~x, family="poisson", weights=weigh1))[1] > > > -2*logLik(glm(y~x, family="poisson", weights=weigh1/3))[1] > > > > This means that the AIC and other model comparison techniques with this > > weighted Poisson regression are dependent on the scaling of the > > weights. So I assume I misunderstand what the "weights" are doing in > > the glm function. > > > > -Tracy > > > > > > > > On 2/4/2014 12:56 PM, Rolf Turner wrote: > >> > >> On 04/02/14 20:12, IamRandom wrote: > >> > >>> I am running a simple example of GLM. If I include weights when > >>> family="poisson" then the weights are calculated iteratively and > >>> $weights and $prior.weights return different values. The $prior.weights > >>> are what I supplied and $weights are the "posterior" weights of the > >>> IWLS. If I include weights with family="gaussian" then the weights are > >>> static and $weights and $prior.weights return the same values; it seems > >>> to ignore IWLS algorithm procedure. I really want the family="poisson" > >>> to behave like the family="gaussian" and use the static weights. > >>> Thoughts? > >> > >> As far as I understand things, your desideratum makes no sense. The > >> prior weights and the just-plain-weights are very different creatures. > >> The reason they wind up being the same for the gaussian family is that > >> for the gaussian family the likelihood is maximized by least squares; > >> there is no need for iteration or for re-weighting. > >> > >> The poisson family cannot behave like the gaussian family because for > >> the poisson family (or any family *other* than gaussian) iteration is > >> necessary in order to maximize the likelihood. > >> > >> You might get some insight into what's going on if you were to read > >> Annette Dobson's book "An Introduction to Generalized Linear Models" > >> (Chapman and Hall, 1990). > >> > >> cheers, > >> > >> Rolf Turner > >> > >> > >> > > > > ______________________________________________ > R-help@r-project.org mailing list > 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. ______________________________________________ R-help@r-project.org mailing list 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.