Hay, im a newbie and still need more learn. I have several question, I'm trying to create a negative binomial regression model using the R and library(MASS). But i'm still confusing what sould I use glm (Y~X1+X2+X3+X4+X5+X6+X7, family=negative.binomial(theta), data, maxit) or glm.nb.
What is the difference between those two functions? [some one said if we don’t know overdispersion parameter we can't use glm(), so glm.nb() is the option; and other person said, in glm.nb() theta is assumed theta=1].
Anotherhand, i am still confused about theta, in some discussion forum it said theta is an overdispersion parameter, but others said theta is a shape parameter for distribution and overdispersion is the same as k, as discussed in The R Book (Crawley 2007).
I have read a tutorial to negative binomial regression with R (but still for me not look that corect). In that tutorial, suggest to trial and error what theta value until Residual deviance equal to degrees of freedom with glm (Y~X1+X2+X3+X4+X5+X6+X7, family=negative.binomial(theta), data, maxit)
sorry for my bad english