glm

I'm attempting to write my own function to understand how the Poisson distribution behaves within a Maximum Likelihood Estimation framework (as it applies to GLM). I'm familiar with R's handy glm function, but wanted to try and hand-roll some code to understand what's going on: n <- 10000 # sample size b0 <- 1.0 # intercept b1 <- 0.2 # coefficient x <- runif(n=n, min=0, max=1.5) # generate covariate values lp <- b0+b1*x # linear predictor lambda <- exp(lp) # compute lamda y <- rpois(n=n, lambda=lambda) # generate y-values dta <- data.frame(y=y, x=x) # generate dataset negloglike <- function

I would like to create a procedure that will add per each loop a new variable (from a pool of variables) to a glm model that allready contains few of the variables that need to be part of the final model.I than would like to have the results of the loop in a list that will contain the glm formula and results.I know how to do it manually (code is written below) but I would be happy to know how to do it automaticaly. Here is a toy dataset and the relevant code to do the task manually: dat <- read.table(text = "target birds wolfs Country 0 21 7 a 0 8 4 b 1 2 5 c 1 2 4 a 0 8 3 a 1 1 12 a 1 7 10 b

I used the glmulti function in the glmulti package to obtain the best glm model for poisson error distributed data. No problems there. Once I had obtained the best model, I used the Chi-square test to obtain p-values and test statistics for each of the variables entered into the model. The only problem I am encountering is that the data is overdispersed and the Zuur book and Crawley both suggest using the quasi family function to correct for overdispersion. This in itself is not a problem except that the glmulti function does not allow fits to quasi functions. The question I have, is whether