Hello,

I have a question concerning the R-function chisq.test. 

For example, I have some count data which can be categorized as follows
class1: 15 observations
class2:  0 observations
class3:  3 observations
class4:  4 observations

I would like to test the hypothesis whether the population probabilities are all equal (=> Test for discrete uniform distribution)
If you have a small sample size and therefore a sparse (1xr)-table, then assumptions for chisquare-goodness-of-fit test are violated (the numbers expected are less than 5 in more than 75% of the entries.)

#######  R-Program: Chisquare-Test :#########

mydata <- c(15,0,3,4)
chisq.test(mydata, correct=TRUE, rescale.p = TRUE, simulate.p.value = TRUE, B = 2000)


As you cannot ignore the small sample size, I use 'simulate.p.value' is 'TRUE'  and therefore the p-value is computed by Monte Carlo simulation with 'B' replicates.
But is it also the possible to use an EXACT version of a chisquare goodness-of-fit test without a Monte-Carlo-simulation? How can I calculate this in R?



Any hint would be appreciated,
Regards,
Christine Adrion
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