R help - May 2012 - Getting predicted values from a zero-inflated negative binomial using zeroinfl()

Hi,
I am a little confused at the output from predict() for a zeroinfl object.

Here's my confusion:

## From zeroinfl package
fm_zinb2 <- zeroinfl(art ~ . | ., data = bioChemists, dist =
"negbin")


## The raw zero-inflated overdispersed data
> table(bioChemists$art)
  0   1   2   3   4   5   6   7   8   9  10  11  12  16  19
275 246 178  84  67  27  17  12   1   2   1   1   2   1   1

## The default output from predict. It looks like it is doing a horrible
job. Does it really predict 7 zeros?
> table(round(predict(fm_zinb2)))
  0   1   2   3   4   5   6  10
  7 354 487  45  12   6   3   1

##  The output from predict using "count"
> table(round(predict(fm_zinb2,type="count")))
  1   2   3   4   5   6  10
312 536  45  12   6   3   1

## The output from predict using "zero", but here it predicts 24
"structural" zeros?
> table(round(predict(fm_zinb2,type="zero")))
  0   1
891  24


So my question is how do I interpret these different outputs from the
zeroinf object? What are the differences? The help page just left me
confused. I would expect that table(round(predict(fm_zinb2))) would be E(Y)
and would most accurately track table(bioChemists$art) but I am wrong. How
can I find the E(Y) that would most closely track the raw data?

Please cc me if you reply.
Thanks,
Chris

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