I read the problem a bit differently than Andreas. I thought you were
trying to create a *substitute* for the parametric t-test.
A p-value is not a statement about a group of tests. It is a statement
about one sample of data in comparison with the theoretical (in the
case of the parametric test), or on your case, with the bootstrap
distribution. You want to construct a CDF of your distribution of
means/s.d. values and package it up in a form that would allow you to
return the *proportion* of values (the "p-value") above one particular
new sample value.
?ecdf #will give you information on how to turn 1000 realizations
into a function, it's really pretty simple.
If your sample of potentially (but not necessarily) t-like statistics
is tt then ttCDF <- ecdf(tt) will return nothing, but result in ttCDF
becoming a function. Then with a sample value mean_a to test, you get
useful results with:
ttCDF(mean_a)
Turning this into a "test" requires a bit more packaging but it think
the road is clear ahead.
--
David Winsemius
On Jan 14, 2009, at 4:52 AM, Andreas Klein wrote:
Hello.
How can I compute the Bootstrap p-Value for a one- and two sided
test, when I have a bootstrap sample of a statistic of 1000 for
example?
My hypothesis are for example:
1. Two-Sided: H0: mean=0 vs. H1: mean!=0
2. One Sided: H0: mean>=0 vs. H1: mean<0
I hope you can help me
Thanks in advance
Regards,
Andreas
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