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Dear users of the R mailing list, I am a ph.d. student in biology working on red deer in Norway, who would like to conduct an analysis with random factor where the response is binomially distributed. This cannot be conducted in S-plus, and I was told by others that it may be possible in R. However, I soon got into trouble which I hope you can help me to solve. My model is on this form:

I've been poking around with GLMs (on which I am *not* an expert) on behalf of a student, particularly binomial (standard logit link) nested models with overdispersion. I have one possible bug to report (but I'm not confident enough to be *sure* it's a bug); one comment on the general inconsistency that seems to afflict the various functions for dealing with overdispersion in GLMs

Hi Are there any functions that de-convolute data into a given number of clusters, rather like the NPMLE GLIM macros from Murray Aitkin and Brian Francis? Basically I would like to code into R the same approach but include the possiblility of some data being censored. In principle the formulae are the same (just replace the likelihood function) but I haven't managed to get my head round the model building problems. I am look...

Un bon mot s'il vous plait. I have coded an R routine to decompose messy data following Murray Aitkin and Brian Francis's NPMLE GLIM macros for the normal distribution only but extended to incorporate censoring and variance heterogeneity. Essentially it is a wrapper for nlm() in the M-part while the E-part re-estimates the weights in the same way as the GLIM macros. The big problem is that it...

Hello, I am try to perform a modeling which is relevant in a strongly heteroscedastic context. So I perform a dual modeling (modeling of both mean and variance of a response) in using the following loop: jointmod <- function(formula, data, itercrit=10,devcrit=0.0001) { # # Init step # init <- glm(formula=formula,family=gaussian, data=data) response <-