I think what you need is just count. For example, if you want to know the p value of the mean bigger than 0 and you have 5 such cases in your draws then the p value is 5/1000=0.005, right?
HTH YHDENG -----Original Message----- From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On Behalf Of Andreas Klein Sent: January 14, 2009 9:23 AM To: r help Subject: Re: [R] How to compute p-Values 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 > > ______________________________________________ > R-help@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@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@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.
