I think we are at the stage where it is your responsibility to provide
some code to set up the problem.
--
David Winsemius
On Jan 14, 2009, at 9:23 AM, Andreas Klein wrote:
Hello.
What I wanted was:
I have a sample of 100 relizations of a random variable and I want a
p-Value for the hypothesis, that the the mean of the sample equals
zero (H0) or not (H1). That is for a two sampled test.
The same question holds for a one sided version, where I want to
know if the mean is bigger than zero (H0) or smaller or equal than
zero (H1).
Therfore I draw a bootstrap sample with replacement from the
original sample and compute the mean of that bootstrap sample. I
repeat this 1000 times and obtain 1000 means.
Now: How can I compute the p-Value for an one sided and two sided
test like described above?
Regards,
Andreas
--- gregor rolshausen <gregor.rolshau...@biologie.uni-freiburg.de>
schrieb am Mi, 14.1.2009:
Von: gregor rolshausen <gregor.rolshau...@biologie.uni-freiburg.de>
Betreff: Re: [R] How to compute p-Values
An: "r help" <r-help@r-project.org>
Datum: Mittwoch, 14. Januar 2009, 11:31
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
hi,
do you want to test your original t.test against t.tests of
bootstrapped samples from you data?
if so, you can just write a function creating a vector with
the statistics (t) of the single t.tests (in your case 1000
t.tests each with a bootstrapped sample of your original
data -> 1000 simulated t-values).
you extract them by:
tvalue=t.test(a~factor)$statistic
then just calculate the proportion of t-values from you
bootstrapped tests that are bigger than your original
t-value.
p=sum(simualted_tvalue>original_tvalue)/1000
(or did I get the question wrong?)
cheers,
gregor
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