Re: [R] gamma distribution don't allow negative value in GLMs?
I think the 0 values for snail are hurting you. Kees On Sunday 15 October 2006 13:10, zhijie zhang wrote: Dear friends, when i use glm() to fit my data, i use glm(formula = snail ~ vegtype + mhveg + humidity + elevation + soiltem, *family = Gamma(link = inverse),* data =a,)) It shows: error in eval(expr, envir, enclos) : *gamma distribution don't allow negative value*. But i use result-glm(formula = snail ~ vegtype + mhveg + humidity + elevation + soiltem, family = poisson, data =a) #this works In fact , there isn't any negative value in my dataset, who can tell me the reason? Thanks very much! I copy my data here so you can check it: vegtype mhveg humidity soiltem elevation snail 1 diluo 35.0 0.27985121.1 low 162 2 diluo 25.0 0.31609223.1 low 113 3 yuhao 35.0 0.29723821.7 low 105 4 huanghuacai 1.5 0.31068723.1 low 5 5 huanghuacai 2.0 0.26786828.3 low 1 6 yuhao 25.0 0.29013521.9 low10 7 huanghuacai 1.0 0.28520727.7 low 6 8 huanghuacai 2.0 0.25297328.3 low 1 9 huanghuacai 1.5 0.2728.1 low 1 10 huanghuacai 2.5 0.3029.1 low 1 11 huanghuacai 2.0 0.29615429.1 low 0 12 huanghuacai 2.0 0.30287427.5 low 3 13 huanghuacai 1.5 0.30149928.9 low 0 14 huanghuacai 3.0 0.29151330.3 low 1 15 huanghuacai 1.0 0.27343831.1 low 3 16 huanghuacai 1.5 0.29011627.9 low19 17 huanghuacai 2.5 0.19893231.9 low 0 18 huanghuacai 2.0 0.3930.5 high 4 19 huanghuacai 2.5 0.28259530.7 high 0 20 huanghuacai 1.0 0.26609724.7 high14 21yuhao 30.0 0.24051626.9 high51 22yuhao 35.0 0.22754126.7 high84 23yuhao 20.0 0.25283328.3 low30 24diluo 40.0 0.30303027.9 low91 25hucao 80.0 0.30386724.5 low 114 26diluo 25.0 0.33494826.7 low 115 27hucao 60.0 0.30689726.5 low23 28hucao 75.0 0.31446525.7 low43 29yuhao 30.0 0.25178326.1 low77 30diluo 10.0 0.2826.1 low62 31yuhao 25.0 0.29171626.1 low78 32hucao 90.0 0.28880024.5 low35 33diluo 25.0 0.33783026.3 high75 34yuhao 13.0 0.29659927.7 high23 35hucao 70.0 0.27949826.3 high 116 36diluo 3.0 0.28148128.1 high25 37hucao 70.0 0.29600023.7 high83 38diluo 10.0 0.27266227.7 low56 39hucao 70.0 0.28979625.3 high 112 40diluo 5.0 0.33971627.9 high84 41yuhao 35.0 0.23142724.9 high88 42hucao 80.0 0.27381024.1 high 134 43yuhao 40.0 0.27278925.1 high53 44yuhao 45.0 0.22603625.1 high88 45yuhao 55.0 0.28549523.9 high76 46hucao 80.0 0.25218523.9 high 106 47diluo 15.0 0.28993324.5 high 194 48hucao 95.0 0.26175623.1 high35 49hucao 55.0 0.23981924.7 high21 50hucao 75.0 0.25430723.9 high41 51 huanghuacai 1.0 0.28643223.7 low18 52 huanghuacai 2.0 0.30134223.1 low 2 53 huanghuacai 2.0 0.36956523.3 low 5 54 huanghuacai 1.5 0.24583324.3 low 4 55 huanghuacai 1.0 0.31567924.1 low 4 56 huanghuacai 2.5 0.29612423.7 low 4 57 huanghuacai 2.0 0.31266725.7 low 3 58 huanghuacai 3.0 0.30087025.7 low 0 59 huanghuacai 2.0 0.30374326.5 low 2 60 huanghuacai 1.0 0.26979925.3 low 7 61hucao 75.0 0.28125022.5 low14 62yuhao 35.0 0.35035023.3 low63 63hucao 65.0 0.30454522.7 low17 64diluo 7.0 0.31005624.9 low45 65hucao 80.0 0.28800022.9 low27 66hucao 80.0 0.28421122.7 low46 67diluo 25.0 0.28137923.5 low 161 68hucao 80.0 0.29053323.3 low 117 69yuhao 27.0 0.31656824.1 low 106 70yuhao 28.0 0.28515625.1 low82 71yuhao 30.0 0.2724.5 low55 72hucao 85.0 0.29034523.9 low54 73yuhao 35.0 0.31578924.1 low81 74diluo 15.0 0.28659828.3 low 102 75yuhao 45.0 0.31421124.1 low85 76yuhao 25.0 0.26879425.1 low63 77hucao 80.0
Re: [R] gamma distribution
Answering both messges here:
1. [EMAIL PROTECTED] wrote:
Hi I appreciate your response. This is what I observed..taking
the log transform of the raw gamma does change the p value of
the test. That is what I am importing into excel (the p - values)
Well, so you made a mistake! And I still do not know why anybody realy
want to import data to Excel, if the data is already in R.
For me, the results are identical (and there is no reason why not).
and then calculating the power of the test (both raw and
transformed).
can you tell me what exactly your code is doing?
See below.
2. [EMAIL PROTECTED] wrote:
Hi
I ran your code. I think it should give me the number of p values below 0.05
significance level (thats what i could understand from your code), but after
running your code there is neither any error that shows up nor any value that
the console displays.
You are right in the point what the code I sent does:
erg - replicate(1000, {
x-rgamma(10, 2.5, scale = 10)
y-rgamma(10, 2.5, scale = 10)
wilcox.test(x, y, var.equal = FALSE)$p.value
})
sum(erg 0.05) # 45
and it works for me. It results in a random number close to 50, hopefully.
Since both points above seem to be very strange on your machine: Which
version of R are you using? We assume the most recent one which is R-2.1.1.
Uwe Ligges
__
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Re: [R] gamma distribution
thanks for your response. btw i am calculating the power of the wilcoxon test. i
divide the total no. of rejections by the no. of simulations. so for 1000
simulations, at 0.05 level of significance if the no. of rejections are 50 then
the power will be 50/1000 = 0.05. thats y im importing in excel the p values.
is my approach correct??
thanks n regards
-dev
Quoting Uwe Ligges [EMAIL PROTECTED]:
Answering both messges here:
1. [EMAIL PROTECTED] wrote:
Hi I appreciate your response. This is what I observed..taking
the log transform of the raw gamma does change the p value of
the test. That is what I am importing into excel (the p - values)
Well, so you made a mistake! And I still do not know why anybody realy
want to import data to Excel, if the data is already in R.
For me, the results are identical (and there is no reason why not).
and then calculating the power of the test (both raw and
transformed).
can you tell me what exactly your code is doing?
See below.
2. [EMAIL PROTECTED] wrote:
Hi
I ran your code. I think it should give me the number of p values below
0.05
significance level (thats what i could understand from your code), but
after
running your code there is neither any error that shows up nor any value
that
the console displays.
You are right in the point what the code I sent does:
erg - replicate(1000, {
x-rgamma(10, 2.5, scale = 10)
y-rgamma(10, 2.5, scale = 10)
wilcox.test(x, y, var.equal = FALSE)$p.value
})
sum(erg 0.05) # 45
and it works for me. It results in a random number close to 50, hopefully.
Since both points above seem to be very strange on your machine: Which
version of R are you using? We assume the most recent one which is R-2.1.1.
Uwe Ligges
__
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] gamma distribution
[EMAIL PROTECTED] wrote:
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.
No, since H1 is NOT true in your case (the power is undefined under H0).
In this case it is an estimator for the alpha error, but not the power.
You might want to reread some basic textbook on testing theory.
BTW: Why do you think R cannot calculate 50/1000 and Excel does better?
is my approach correct??
No.
Uwe Ligges
thanks n regards
-dev
Quoting Uwe Ligges [EMAIL PROTECTED]:
Answering both messges here:
1. [EMAIL PROTECTED] wrote:
Hi I appreciate your response. This is what I observed..taking
the log transform of the raw gamma does change the p value of
the test. That is what I am importing into excel (the p - values)
Well, so you made a mistake! And I still do not know why anybody realy
want to import data to Excel, if the data is already in R.
For me, the results are identical (and there is no reason why not).
and then calculating the power of the test (both raw and
transformed).
can you tell me what exactly your code is doing?
See below.
2. [EMAIL PROTECTED] wrote:
Hi
I ran your code. I think it should give me the number of p values below
0.05
significance level (thats what i could understand from your code), but
after
running your code there is neither any error that shows up nor any value
that
the console displays.
You are right in the point what the code I sent does:
erg - replicate(1000, {
x-rgamma(10, 2.5, scale = 10)
y-rgamma(10, 2.5, scale = 10)
wilcox.test(x, y, var.equal = FALSE)$p.value
})
sum(erg 0.05) # 45
and it works for me. It results in a random number close to 50, hopefully.
Since both points above seem to be very strange on your machine: Which
version of R are you using? We assume the most recent one which is R-2.1.1.
Uwe Ligges
__
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
__
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Re: [R] gamma distribution
Hi
Again to come back on the question why you don't get identical
p.values for the untransformed and the transformed data.
I ran your script below and I get always 2 identical test per
loop. In your text you are talking about the first 1000 values
for the untransformed and the next 1000 values for the
transformed.
But did you consider that in each loop there is a test for the
untransformed and the transformed, so the tests are printed
alternating.
This might be a reason why you did not get equal results.
Hope this helps,
Christoph
--
Christoph Buser [EMAIL PROTECTED]
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/
--
[EMAIL PROTECTED] writes:
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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__
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Re: [R] gamma distribution
Hi
As Uwe mentioned be careful about the difference the
significance level alpha and the power of a test.
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 [EMAIL PROTECTED]
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 [EMAIL PROTECTED]
To: Christoph Buser [EMAIL PROTECTED]
cc: Liaw, Andy [EMAIL PROTECTED]
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(z10.05)/1000 ## 0.049
1 - sum(z20.05)/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
Re: [R] gamma distribution
Hi Christopher and Uwe. thanks for your time and guidance.
I deeply appreciate it.
-dev
Quoting Christoph Buser [EMAIL PROTECTED]:
Hi
As Uwe mentioned be careful about the difference the
significance level alpha and the power of a test.
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 [EMAIL PROTECTED]
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 [EMAIL PROTECTED]
To: Christoph Buser [EMAIL PROTECTED]
cc: Liaw, Andy [EMAIL PROTECTED]
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(z10.05)/1000 ## 0.049
1 - sum(z20.05)/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
Re: [R] gamma distribution
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 [EMAIL PROTECTED]
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/
--
[EMAIL PROTECTED] writes:
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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PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
__
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Re: [R] gamma distribution
[EMAIL PROTECTED] wrote:
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.
He?
- log() transforming the data does not change the Wilcoxon statistics
(based on ranks!)!
- Why is this related to Excel?
- What are you going to show?
I get
erg - replicate(1000, {
x-rgamma(10, 2.5, scale = 10)
y-rgamma(10, 2.5, scale = 10)
wilcox.test(x, y, var.equal = FALSE)$p.value
})
sum(erg 0.05) # 45
which seems plausible to me.
Uwe Ligges
--
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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Re: [R] gamma distribution
Hi
You are right but here I am taking into account the p values I get from the
tests on the raw and the transformed samples. And then I calculate the power of
the tests based on the # of rejections of the p values.
DO you think its a good way to determine the power of a test?
thanks
-dev
Quoting Christoph Buser [EMAIL PROTECTED]:
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 [EMAIL PROTECTED]
Seminar fuer Statistik, LEO C13
ETH (Federal Inst. Technology)8092 Zurich SWITZERLAND
phone: x-41-44-632-4673 fax: 632-1228
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--
[EMAIL PROTECTED] writes:
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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Re: [R] gamma distribution
Hi
I ran your code. I think it should give me the number of p values below 0.05
significance level (thats what i could understand from your code), but after
running your code there is neither any error that shows up nor any value that
the console displays.
thanks in advance
-dev.
Quoting Uwe Ligges [EMAIL PROTECTED]:
[EMAIL PROTECTED] wrote:
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.
He?
- log() transforming the data does not change the Wilcoxon statistics
(based on ranks!)!
- Why is this related to Excel?
- What are you going to show?
I get
erg - replicate(1000, {
x-rgamma(10, 2.5, scale = 10)
y-rgamma(10, 2.5, scale = 10)
wilcox.test(x, y, var.equal = FALSE)$p.value
})
sum(erg 0.05) # 45
which seems plausible to me.
Uwe Ligges
--
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
__
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide!
http://www.R-project.org/posting-guide.html
__
R-help@stat.math.ethz.ch mailing list
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