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 ? [[alternative HTML version deleted]] ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.