You may get a response here, but as this is primarily a statistical
question, not a question about R programming, so it is off topic here. I
would suggest that you post this on stats.stackexchange.com or other
statistics site instead. There is a large literature on this sort of thing .
Cheers,
Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along and
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Thu, Aug 2, 2018 at 12:00 PM, Andras Farkas via R-help <
r-help at r-project.org> wrote:
> Dear All,
>
> once we run the following code, the results of the test will give us the
> expected obvious, samples are from the common distribution...
>
>
> library(kSamples)
>
> u1 <- sample(rnorm(500,10,1),20,replace = TRUE)
> u2 <- sample(rnorm(500,10,1),20,replace = TRUE)
> u3 <- sample(rnorm(500,10,1),20,replace = TRUE)
> u4 <- sample(rnorm(500,10,1),20,replace = TRUE)
> u5 <- sample(rnorm(500,10,1),20,replace = TRUE)
>
> ad.test(u1, u2, u3,u4,u5, method = "exact", dist = FALSE, Nsim =
1000)
>
> next, if I change "u5" to:
>
> u5 <- sample(rnorm(500,20,1),20,replace = TRUE)
>
> the results of the test again gives us what we expect, ie samples are not
> from the common distribution.... my question is: would you know of a way to
> be able to automatically select out or identify "u5", the
distribution
> that is "responsible" for the results generated showing that the
samples
> are not from the common distribution?
>
> much appreciate your help,
>
>
> Andras
>
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