[R] How to compute p-Values

[David Winsemius]

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.rolshausen at biologie.uni-freiburg.de>  
> schrieb am Mi, 14.1.2009:
>
>> Von: gregor rolshausen <gregor.rolshausen at biologie.uni-freiburg.de>
>> Betreff: Re: [R] How to compute p-Values
>> An: "r help" <r-help at 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
>>
>> ______________________________________________
>> R-help at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained,
>> reproducible code.
>
>
>
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.