[R] p-values for the dip test
maechler at stat.math.ethz.ch
Fri Oct 22 09:23:10 CEST 2004
Hmm, this is rather about reading the (Hartigan)^2 paper ...
>>>>> "Kylie" == Kylie Lange <kylie.lange at flinders.edu.au>
>>>>> on Fri, 22 Oct 2004 11:17:34 +0930 writes:
Kylie> Hi all,
Kylie> I am using Hartigan & Hartigan's  "dip test" of
Kylie> unimodality via the diptest package in R. The
Kylie> function dip() returns the value of the test
Kylie> statistic but I am having problems calculating the
Kylie> p-value associated with that value. I'm hoping
Kylie> someone here is familiar with this process and can
Kylie> explain it.
Kylie> In the original article there is an example using
Kylie> n=63 and a dip value of 0.059 - which I am able to
Kylie> replicate using dip(). However the article then
Kylie> "The dip illustrated in Figure 1 is 0.059 which has a
Kylie> tail probability about 10% from Table 1. (
Kylie> sqrt(63)*D*F(63) is distributed approximately as
Kylie> sqrt(100)*D*F(100) which has 90% point 0.474. Thus
Kylie> D*F(63) has 90% point 0.060. )"
note: sqrt(100) * D(F_100) = 10 * D(F_100)
i.e. D(F_100) would have 90* point 0.0474
but the second "4" is a simple typo, (not the only one in the
paper, IIRC!); "s/4/1/" --> you get the correct 0.0471
Kylie> It is the value of 0.474 that I am unable to
Kylie> get. Table 1 is a table of percentage points for
Kylie> various sample sizes. The same table is provided in
Kylie> the diptest package as 'qDiptab' (but at greater
Kylie> accuracy). n=63 is not tabled but n=50 and 100
Kylie> are. In the table for n=100 the value given for 90%
Kylie> is 0.0471 so where does the 0.474 come from?
Kylie> Any help appreciated!
Martin Maechler, ETH Zurich ('diptest' maintainer)
Kylie>  Hartigan JA & Hartigan PM. "The Dip Test of
Kylie> Unimodality", The Annals of Statistics, v13(1):70-84, 1985.
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