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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