[R] Comparing two pairs of non-normal datasets in R?

[Spencer Graves]

      Have you considered "qqplot(A1, A2)" and "qqplot(C1, C2)"?  If A2, 
A2, C1, C2 are "more like Poisson", I might try "qqplot(sqrt(A1), 
sqrt(A2))", etc.:  Without the "sqrt", the image might be excessively 
distorted by largest values, at least in my experience. 

      hope this helps.  spencer graves

Peter Sebastian Masny wrote:

>Hi all,
>
>I'm using R to analyze some research and I'm not sure which test would be 
>appropriate for my data.  I was hoping someone here might be able to help.
>
>Short version:
>Evaluate null hypothesis that change A1->A2 is similar to change C1->C2, for 
>continuous, non-normal datasets.
>
>
>Long version:
>
>I have two populations A and C.  I take a measurement on samples of these 
>populations before and after a process.  So basically I have:
>A1 - sample of A before process
>A2 -  sample of A after process
>C1 - sample of C (control) before process
>C2 - sample of C (control) after process
>
>The data is continuous and I have about 100 measurements in each dataset.  
>Also, the data is not normally distributed (more like a Poisson).
>
>By Wilcoxon Rank Sum, A1 is significantly different than A2 and C1 is 
>different than C2.
>
>Here is the problem:
>C1 is only slightly different than C2 (Wilcoxon, p<.02), while A1 is more 
>noticeably different than A2 (p<1E-22).  What I would like to do is assume 
>that the changes seen in C are typical, and evaluate the changes in A 
>relative to the changes in C (i.e. are the changes greater?).
>
>Any thoughts?
>
>
>
>Thanks,
>Peter Masny
>
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