Dear R-Users,
I have a question about the dglm() function from the dglm Package (V
    1.6.2). The dglm() function fits double-generalized linear models as
    described in
    
    Smyth, G. K. (1989). Generalized linear models with varying
    dispersion. J. R. Statist. Soc. B, 51, 47-60. I use dglm() to estimate a simple logit (i.e. the dependent variable is a binary indicator) with varying dispersion. Code looks like this:

example <- data.frame(
牋牋牋 x1 = rnorm(1000,.5,1),
牋牋牋 x2 = rnorm(1000,0,1),
牋牋牋 y=rbinom(1000,1,.5))
model=dglm(y~x1,~x2,data=example,family=binomial("logit"))

While the model converges, it keeps telling me the following
    warning:
"In eval(expr, envir, enclos) : non-integer #successes in a binomial
    glm!"

I interpret the warning as urging me that I should not apply dglm()
    to binary data. I am puzzled because 1) the standard glm() function
    works fine with binary data and 2) my reading of Smyth's paper
    suggests that the double-generalized linear model can be applied to
    binary data as well. 

So I guess my question is: should I ignore this warning or is there
    a statistical reason why the results might be biased in one way or
    the other?

Thanks for any clarifying thoughts,
Chris
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