James,
I assume that you use package diptest.
Look up
?qDiptab
for a table of quantiles from which you can obtain your p-value.
> data(qDiptab)
> qDiptab
You then look up your statistic value (0.074) for a suitable n
(n=30, say), and you take one minus the Pr value you find on top of the
table.
Using n=30 gives you Pr between 0.8 and 0.9, so 0.2>p>0.1.
However, the next largest value of n (50) would lead to 0.05>p>0.02
leading to possibly different conclusions. Unfortunately this means that
you are in a kind of ambiguous borderline situation for the table, though
33 is so much closer to 30 than to 50 that it seems that your result is
at least not significant at 0.05 level.
By the way, you can simulate a p-value yourself by repeating
dip(runif(33)) lots of times.
Hope this helps,
Christian
On Mon, 6 Jul 2009, James Allsopp wrote:
> Hi,
> I just got a value for the dip test out of my data of 0.074 for a sample
> size of 33. I'm trying to work out what this actually means though?
> Could someone help me relate this to a p-value?
>
> Thanks
> James
>
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*** --- ***
Christian Hennig
University College London, Department of Statistical Science
Gower St., London WC1E 6BT, phone +44 207 679 1698
chrish at stats.ucl.ac.uk, www.homepages.ucl.ac.uk/~ucakche