Count model prediction

Hello ;
I am doing a regression of count data (number of award and there are some
covariates)

I have estiamted the parameters of negative binomial distribuion (lambda is
a function of covaraites, GLM model) by glm.nb function and training
dataset.

Now I want to predict the number of award (for example y=0, y=1, y=2,) or
testing dataset. I dont know how to calculate this numbers?

Do you want the expected probabilities of observing 0, 1, 2, etc.? Then 
you can use the convenience function predprob() in package "pscl". For 
example:

## package and data
library("pscl")
data("bioChemists", package = "pscl")

## NB model
m <- glm.nb(art ~ ., data = bioChemists)

## predicted probabilities for counts 0, 1, ..., 19 per observation
p <- predprob(m)

## sums across all observations
colSums(p)

which yields:

           0           1           2           3           4           5
277.7900354 249.1239076 164.7450252  97.2999696  54.7265588  30.2456638
           6           7           8           9          10          11
  16.7394187   9.4002205   5.4081462   3.2096223   1.9731828   1.2584834
          12          13          14          15          16          17
   0.8320010   0.5686597   0.4003506   0.2891672   0.2134580   0.1604948
          18          19
   0.1225642   0.0948470

hth,
Z

I would be very grateful if anybody could help me.
thanks

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