Thread: Analysing circular (angular) data - how to find significant differences?

1. Re: Analysing circular (angular) data - how to find significant differences?

The specific test you want is called the Kuiper test. It is a variant of the Kolmogorov-Smirnov test suitable for circular data.

2. The Following User Says Thank You to ichbin For This Useful Post:

DEW (03-11-2013)

3. Re: Analysing circular (angular) data - how to find significant differences?

Is the Null-Hypothesis that the data is uniformly distributed? And is therefore the Null-Hypothesis accepted if the p-value is significant?
SOrry for these dumb questions, but I can't find any references to the Kuiper Test

4. Re: Analysing circular (angular) data - how to find significant differences?

The null hypothesis is that the data is distributed according to whatever distribution you care to specify: a uniform distribution, a normal distribution or circular normal distribution, etc. You specify the CDF of the comparison distribution as an input to the test. There is also a two-sample variant that directly tests whether two samples have been drawn from the same distribution without you having to specify what that distribution is. The null hypothesis that the distributions match is rejected if the upper-tailed probability of the test statistic value is too large.

You can find more on the Kuiper test in Numerical Recipies (http://www.amazon.com/dp/0521880688/), a book which is less well-known among statisticians but which I would expect to be on every working scientist's shelf. There is a Wikipedia article on Kuiper's test (http://en.wikipedia.org/wiki/Kuiper%27s_test) which describes how to calculate the test statistic V. The circstats package for R implements the test (http://cran.r-project.org/web/packag...ats/index.html), as dpes the Meta.Numerics package for .NET (http://www.meta-numerics.net/), and there is code for it in the Numerical Reciepies book.

5. Re: Analysing circular (angular) data - how to find significant differences?

great. that helps a lot! I came across another test, the v-test, where I can test against a predicted mean.
now I think I can solve all issues.
thanks to everyone who replied to my thread!

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