# Re: [R] gamma distribution

From: Christoph Buser <buser_at_stat.math.ethz.ch>
Date: Thu 28 Jul 2005 - 19:19:06 EST

Hi

To do power calculations you should specify and alternative hypothesis H_A, e.g. if you have two populations you want to compare and we assume that they are normal distributed (equal unknown variance for simplicity). We are interested if there is a difference in the mean and want to use the t.test. Our Null hypothesis H_0: there is no difference in the means

To do a power calculation for our test, we first have to specify and alternative H_A: the mean difference is 1 (unit) Now for a fix number of observations we can calculate the power of our test, which is in that case the probability that (if the true unknown difference is 1, meaning that H_A is true) our test is significant, meaning if I repeat the test many times (always taking samples with mean difference of 1), the number of significant test divided by the total number of tests is an estimate for the power.

In you case the situation is a little bit more complicated. You need to specify an alternative hypothesis. In one of your first examples you draw samples from two gamma distributions with different shape parameter and the same scale. But by varying the shape parameter the two distributions not only differ in their mean but also in their form.

I got an email from Prof. Ripley in which he explained in details and very precise some examples of tests and what they are testing. It was in addition to the first posts about t tests and wilcoxon test.
I attached the email below and recommend to read it carefully. It might be helpful for you, too.

Regards,

Christoph Buser

Christoph Buser <buser@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/

From: Prof Brian Ripley <ripley@stats.ox.ac.uk> To: Christoph Buser <buser@stat.math.ethz.ch> cc: "Liaw, Andy" <andy_liaw@merck.com>
Subject: Re: [R] Alternatives to t-tests (was Code Verification) Date: Thu, 21 Jul 2005 10:33:28 +0100 (BST)

I believe there is a rather more to this than Christoph's account. The Wilcoxon test is not testing the same null hypothesis as the t-test, and that may very well matter in practice and it does in the example given.

The (default in R) Welch t-test tests a difference in means between two samples, not necessarily of the same variance or shape. A difference in means is simple to understand, and is unambiguously defined at least if the distributions have means, even for real-life long-tailed distributions. Inference from the t-test is quite accurate even a long way from normality and from equality of the shapes of the two distributions, except in very small sample sizes. (I point my beginning students at the simulation study in `The Statistical Sleuth' by Ramsey and Schafer, stressing that the unequal-variance t-test ought to be the default choice as it is in R. So I get them to redo the simulations.)

The Wilcoxon test tests a shift in location between two samples from distributions of the same shape differing only by location. Having the same shape is part of the null hypothesis, and so is an assumption that needs to be verified if you want to conclude there is a difference in location (e.g. in means). Even if you assume symmetric distributions (so the location is unambiguously defined) the level of the test depends on the shapes, tending to reject equality of location in the presence of difference of shape. So you really are testing equality of distribution, both location and shape, with power concentrated on location-shift alternatives.

Given samples from a gamma(shape=2) and gamma(shape=20) distributions, we know what the t-test is testing (equality of means). What is the Wilcoxon test testing? Something hard to describe and less interesting, I believe.

BTW, I don't see the value of the gamma simulation as this simultaneously changes mean and shape between the samples. How about checking holding the mean the same:

n <- 1000
z1 <- z2 <- numeric(n)
for (i in 1:n) {

x <- rgamma(40, 2.5, 0.1)
y <- rgamma(40, 10, 0.1*10/2.5)
z1[i] <- t.test(x, y)\$p.value
z2[i] <- wilcox.test(x, y)\$p.value
}
## Level
1 - sum(z1>0.05)/1000 ## 0.049
1 - sum(z2>0.05)/1000 ## 0.15

? -- the Wilcoxon test is shown to be a poor test of equality of means. Christoph's simulation shows that it is able to use difference in shape as well as location in the test of these two distributions, whereas the t-test is designed only to use the difference in means. Why compare the power of two tests testing different null hypotheses?

I would say a very good reason to use a t-test is if you are actually interested in the hypothesis it tests ....

> thanks for your response. btw i am calculating the power of the wilcoxon test. i
> divide the total no. of rejections by the no. of simulations. so for 1000
> simulations, at 0.05 level of significance if the no. of rejections are 50 then
> the power will be 50/1000 = 0.05. thats y im importing in excel the p values.
>
> is my approach correct??
>
> thanks n regards
> -dev
>
>

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