[R] influence.measures, cooks.distance, and glm

[Henric Nilsson]

Dear list,

I've noticed that influence.measures and cooks.distance gives different 
results for non-gaussian GLMs. For example, using R-1.9.0 alpha 
(2003-03-17) under Windows:

 > ## Dobson (1990) Page 93: Randomized Controlled Trial :
 > counts <- c(18,17,15,20,10,20,25,13,12)
 > outcome <- gl(3,1,9)
 > treatment <- gl(3,3)
 > glm.D93 <- glm(counts ~ outcome + treatment, family=poisson())
 > influence.measures(glm.D93)$infmat[, 8]
           1           2           3           4           5           6
0.288294276 0.309131723 0.011614584 0.030963844 0.304525117 0.444410274
           7           8           9
0.459190432 0.002802907 0.377028535
 > cooks.distance(glm.D93)
          1          2          3          4          5          6          7
0.35162220 0.43125000 0.01468043 0.03906913 0.35640497 0.62024818 0.62510614
          8          9
0.00356405 0.44408301

After looking at the influence.measure code, it seems to me that this 
function always estimates the dispersion using Deviance/df. On the other 
hand, the cooks.distance function uses the Pearson residuals and extracts 
the dispersion from the fitted model using summary, which to me seems more 
sensible for a GLM.

Can someone please shed some light on this?

//Henric