 
Since no reply has been posted yet I will give it a shot. runs.test uses the
normal approximation and in your case it returned a z score of -1.8732. This
z score has a cumulative probability of 
 
pnorm(-1.8732,0,1)
[1] 0.03052039
 
If you are concerned about having too many runs and too few runs you would
select the "two.sided" option for runs.test, which gives a p-value of 0.0610
(0.0305 in each tail of the normal distribution). If you are concerned only
with too few runs you would select the "less" option, which will give a
p-value of 0.0305. Finally, if you are concerned only with too many runs you
would select the "greater" option which will give a p-value of 1-0.0305 =
0.9693. If your significance level is 0.05, you would compare 0.05 to 0.0610
and not reject the null hypothesis for the two-sided case and compare 0.05
to 0.0305 in the one-sided case and reject the null hypothesis. Note that
the normal approximation is OK for large samples but may give unacceptable
results for small samples. I am unaware of any packages in R that perform an
exact runs test.
 
Tom

 

>I have used "runs.test" (Package tseries)  for computes the runs test
>for randomness , but I get this result:
> 
>Runs test
>-1.8732   P-value = 0.0610
> 
>Alternative Hypothesis : Two sided
> 
>How can I interpret this result ?
 
 

 


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