quasi-likelihood in glm (PR#284)
Wed, 22 Sep 1999 11:42:18 +0200 (MET DST)
I have been using quasi-likelihood to analyze some overdispersed lesion
count data which requires the variance function "mu^2" (specifically,
I am using glm with the family quasi(var="mu^2", link="log")). Some of
the counts are zero which led to problems fitting the model in R.
1) glm was unable to find starting values
2) after I supplied initial values from the fit of a Poisson
model, glm could not proceed because the deviance was infinite.
Splus 5.0 had no problems fitting the model. I was able to
reproduce the Splus results by editing the quasi() family
generator and using the unnormalized quasi-likelihood
- log(mu) - y/mu (*)
in place of the normalized version
log(y/mu) - (y - mu)/mu (**)
Further investigation of Splus shows how it overcomes these
1) A fudge factor (in this case 0.167) is added to zero observations
so that they can be used as starting values.
2) The contribution to the deviance is (*) for zero observations
and (**) for the rest.
I suggest that R also uses this approach for quasi-likelihood
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