probably Hmisc:::inverseFuntion as well. For p-values you need to go
from the observed value back to the proportion that exceed that value.
Regards,
Andreas.
--- David Winsemius schrieb am Mi, 14.1.2009:
Von: David Winsemius
Betreff: Re: [R] How to compute p-Values
An: klein82...@yaho
x27;t get the
idea behind it. Maybe someone can explain it, if it is the solution to the
problem.
Regards,
Andreas.
--- David Winsemius schrieb am Mi, 14.1.2009:
> Von: David Winsemius
> Betreff: Re: [R] How to compute p-Values
> An: klein82...@yahoo.de
> CC: "r help&q
:
Von: gregor rolshausen
Betreff: Re: [R] How to compute p-Values
An: "r help"
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 exa
-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
two sided test like
described above?
Regards,
Andreas
--- gregor rolshausen schrieb am
Mi, 14.1.2009:
> Von: gregor rolshausen
> Betreff: Re: [R] How to compute p-Values
> An: "r help"
> Datum: Mittwoch, 14. Januar 2009, 11:31
> Andreas Klein wrote:
> >
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
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 y
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 i
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