As I made clear in my first contribution on this subject, I am talking
about non-normal data, especially output from glm, from a likelihood
and/or Bayesian viewpoint. I hope that I don't have to repeat it each time.
  From a likelihood point of view (probability of the observed data
for various models), deviance is exact and standard errors are a
quadratic approximation to the -log likelihood. Peter Dalgaard's
asymptotic arguments are frequentist and irrelevant to my point of
view. If you insist on using frequency tests, their distributions
would better be modified to conform to the likelihood, rather than
modifying the likelihood to conform to the distribution.
  I was not aware that drop1 was available in R and thank Brian Ripley
for making it so. It appears to be exactly what I meant.
  Jim
Jim
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