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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