R help - May 2007 - testing difference (or similarities) between two distance matrices (not independent)

Hi,

i'm looking to test if two distance matrices are statistically  
different from each others.

These two matrices have been computed on the same set of data (several  
population samples)

1. using a particular genetic distance
2. weighting that genetic distance with an extra factor

(we can look at this as one set is computed before applying a  
treatment and the second one after applying that particular treatment  
... kind of a similar situation)

both these matrices are obviously not independent from each others, so  
Mantel test and others correlation tests do not apply here.

I thought of testing the order of values between these matrices (if  
distances are ordered the same way for both matrices, we have very  
similar matrices and the additional factor has quite no effect on the  
calculation). Is there any package or function in R allowing to do  
that and statistically test it (with permutations or another approach)?

I did check the mailing lists but did not find anything on that  
problem i'm trying to solve

thanks for your help and insights on that problem

St?phane Buhler

On 2007-05-25, Stephane.Buhler at anthro.unige.ch
<Stephane.Buhler at anthro.unige.ch> wrote:
> i'm looking to test if two distance matrices are statistically  
> different from each others.
>
>
> both these matrices are obviously not independent from each others, so  
> Mantel test and others correlation tests do not apply here.
>
> I thought of testing the order of values between these matrices (if  
> distances are ordered the same way for both matrices, we have very  
> similar matrices and the additional factor has quite no effect on the  
> calculation). Is there any package or function in R allowing to do  
> that and statistically test it (with permutations or another approach)?
The mantel() function in the vegan library will do exactly this, with
the method="spearman" option. However, I don't think there is any
possible permutation test that will provide you with a valid test of
significance if the samples themselves are not independent.

Tyler