calculating goodness-of-fit statistics

Hi R users

I have a simple data for calculating goodness-of-fit statistics (e.g., X2 by 
Pearson, G2 by Wilks)

#################################################
observed<-c(424,174,0,402)
expected<-c(282.7174, 314.2972, 142.3142, 260.6712)
2*sum(observed*log(observed/expected)) # for X2
sum((observed-expected)^2/expected)  # for G2
#################################################

(note. expected ones were calculating by a model I used, not by marginal of 
observed ones.)

The third element of the observed vector is zero.

For third element, 0 * log(0/142.3142) is NaN. That is why I got NaN for G2.

I think 0 multiplied by anything should be zero. Am I wrong ?

Is there any R functions to correct zero cells for calculating G2? If there 
is, I like to know some

references justifying the correction.

Thank you in advance

TM