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Hi, I am trying to fi a glm-poisson model to 400.000 records. I have tried biglm and glmulti but i have problems... can it really be the case that 400.000 are too many records??? I am thinking of using random samples of my dataset..... Many thanks, -- View this message in context: http://r.789695.n4.nabble.com/glm-poisson-fitting-400-000-records-tp3925100p3925100.html Sent from the R help

Hi userRs! I am trying to fit some GLM-poisson and neg.binomial. The neg. Binomial model is to account for over-dispersion. When I fit the poisson model i get: (Dispersion parameter for poisson family taken to be 1) However, if I estimate the dispersion coefficient by means of: sum(residuals(fit,type="pearson")^2)/fit$df.res I obtained 2.4. This is theory means over-dispersion since

Dear userRs, when applied the summary function to a glm fit (e.g Poisson) the parameter table provides the categorical variables assuming that the first level estimate (in alphabetical order) is 0. What is the standard error for that variable then? Are the standard errors calculated assuming a normal distribution? Many thanks, -- View this message in context:

Hi, I fit both Poisson and NB (negative binomial) models to some empirical data. Although models provide me with sensible parameters, in the case of the NB models i get three inconsistencites: - First, the total number of occurrences predicted by the model (i.e. fitted(fit)) is much greater than those of the data. I realise that poisson and NB models are different in the sense that

Hi there, I need to do the same thing as cumsum but with the variance and skewness. I have tried to do a loop for like this: var.value <- vector(mode = "numeric", length = length(daily)) for (i in (1:length(daily))) { var.value[i] <- var(daily[1:i]) } But because my dataset is so huge, I run out of memory..... Any ideas?!?! Much appreciate