# [R] gamma distribution

Christoph Buser buser at stat.math.ethz.ch
Wed Jul 27 09:01:50 CEST 2005

```Hi

I am a little bit confused. You create two sample (from a gamma
distribution) and you do a wilcoxon test with this two samples.
Then you use the same monotone transformation (log) for both
samples and redo the wilcoxon test.
But since the transformations keeps the order of your samples
the second wilcoxon test is identical to the first one:

x<-rgamma(10, 2.5, scale = 10)
y<-rgamma(10, 2.5, scale = 10)
wilcox.test(x, y, var.equal = FALSE)
x1<-log(x)
y1<-log(y)
wilcox.test(x1, y1, var.equal = FALSE)

Maybe you can give some more details about the hypothesis you'd
like to test.

Regards,

Christoph Buser

--------------------------------------------------------------
Christoph Buser <buser at stat.math.ethz.ch>
Seminar fuer Statistik, LEO C13
ETH (Federal Inst. Technology)	8092 Zurich	 SWITZERLAND
phone: x-41-44-632-4673		fax: 632-1228
http://stat.ethz.ch/~buser/
--------------------------------------------------------------

> Hi R Users
>
>
> This is a code I wrote and just want to confirm if the first 1000 values are raw
> gamma (z) and the next 1000 values are transformed gamma (k) or not. As I get
> 2000 rows once I import into excel, the p - values beyond 1000 dont look that
> good, they are very high.
>
>
> --
> sink("a1.txt");
>
> for (i in 1:1000)
> {
> x<-rgamma(10, 2.5, scale = 10)
> y<-rgamma(10, 2.5, scale = 10)
> z<-wilcox.test(x, y, var.equal = FALSE)
> print(z)
> x1<-log(x)
> y1<-log(y)
> k<-wilcox.test(x1, y1, var.equal = FALSE)
> print(k)
> }
>
> ---
> any suggestions are welcome
>
> thanks
>
> -devarshi
>
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