On Jul 14, 2012, at 19:58 , Schaber, Jörg wrote:
> Dear Peter,
>
> thanks for your clarifications. Sample size is around 200 in each group.
> Would that justify your approach?
It's certainly better than 10...
I did a small check on the IgM data from the ISwR package (298 obs.) and found
som
not knowledgeable statistician enough to judge
that.
best,
joerg
Von: Prof Brian Ripley [rip...@stats.ox.ac.uk]
Gesendet: Samstag, 14. Juli 2012 08:16
Bis: Greg Snow
Cc: Schaber, Jörg; R-help
Betreff: Re: [R] significance test interquartile ranges
On 13
Betreff: Re: [R] significance test interquartile ranges
On Jul 14, 2012, at 08:16 , Prof Brian Ripley wrote:
> On 13/07/2012 21:37, Greg Snow wrote:
>> A permutation test may be appropriate:
>
> Yes, it may, but precisely which one is unclear. You are testing whether the
> t
Hello,
Em 14-07-2012 13:08, peter dalgaard escreveu:
On Jul 14, 2012, at 12:25 , Rui Barradas wrote:
Hello,
There's a test for iqr equality, of Westenberg (1948), that can be found
on-line if one really looks. It starts creating a 1 sample pool from the two
samples and computing the 1st an
On Jul 14, 2012, at 12:25 , Rui Barradas wrote:
> Hello,
>
> There's a test for iqr equality, of Westenberg (1948), that can be found
> on-line if one really looks. It starts creating a 1 sample pool from the two
> samples and computing the 1st and 3rd quartiles. Then a three column table
> w
Hello,
There's a test for iqr equality, of Westenberg (1948), that can be found
on-line if one really looks. It starts creating a 1 sample pool from the
two samples and computing the 1st and 3rd quartiles. Then a three column
table where the rows correspond to the samples is built. The middle
On Jul 14, 2012, at 08:16 , Prof Brian Ripley wrote:
> On 13/07/2012 21:37, Greg Snow wrote:
>> A permutation test may be appropriate:
>
> Yes, it may, but precisely which one is unclear. You are testing whether the
> two samples have an identical distribution, whereas I took the question to b
On 13/07/2012 21:37, Greg Snow wrote:
A permutation test may be appropriate:
Yes, it may, but precisely which one is unclear. You are testing
whether the two samples have an identical distribution, whereas I took
the question to be a test of differences in dispersion, with differences
in lo
Sorry, I meant Kolmogorov-Smirnov test.
Thanks Peter for correction.
Weidong
On Fri, Jul 13, 2012 at 4:56 PM, Peter Ehlers wrote:
> On 2012-07-13 13:33, Weidong Gu wrote:
>>
>> Hi Joerg,
>>
>> Seems Mann-Whitney-Wilcoxon test (ks.test in R) would do the work
>> which tests differences anywhere
On 2012-07-13 13:33, Weidong Gu wrote:
Hi Joerg,
Seems Mann-Whitney-Wilcoxon test (ks.test in R) would do the work
which tests differences anywhere in two distributions, e.g. tails,
interquartiles and center.
The ks.test() function refers to the Kolmogorov-Smirnov test,
not the Wilcoxon test.
A permutation test may be appropriate:
1. compute the ratio of the 2 IQR values (or other comparison of interest)
2. combine the data from the 2 samples into 1 pool, then randomly
split into 2 groups (matching sample sizes of original) and compute
the ratio of the IQR values for the 2 new samples.
Hi Joerg,
Seems Mann-Whitney-Wilcoxon test (ks.test in R) would do the work
which tests differences anywhere in two distributions, e.g. tails,
interquartiles and center.
Weidong Gu
On Fri, Jul 13, 2012 at 7:32 AM, Schaber, Jörg
wrote:
> Hi,
>
> I have two non-normal distributions and use interq
Hi,
I have two non-normal distributions and use interquartile ranges as a
dispersion measure.
Now I am looking for a test, which tests whether the interquartile ranges from
the two distributions are significantly different.
Any idea?
Thanks,
joerg
[[alternative HTML version deleted]]
Yuta,
Thanks for the response.
Yuta wrote:
>
> You've got to state the problem little bit more clear.
>
> What do you mean by "set"? Is it a list of certain possible values,
> available as outcomes of each single measurement (variate)? Or is it
> something else?
> How many variates do you have
You've got to state the problem little bit more clear.
What do you mean by "set"? Is it a list of certain possible values,
available as outcomes of each single measurement (variate)? Or is it
something else?
How many variates do you have inside each sample?
What is it exactly that you want to find
I have a bunch of benchmark measurements that look something like this:
sample.10.000.0625000.0583300.058330
0.058330
sample.20.0583300.0583300.0583300.058330
0.058330
sample.30.0625000.062500
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