[R-sig-eco] Dissimilarity ranking

[francois Guilhaumon]

Dear all,

I would like to point out three small mistakes in Dave's code, which of
course are certainly typos.

If one wants to not get only NAs in the result vector, one should
specify na.rm=TRUE in the apply statement, furthermore I guess that
the mean function has to be applied to 'demodis2' and not 'dimodis'
which is an object of class 'dist', finally there is no need to
transform 'demodis' again using the as.matrix function in the apply
statement:

demodis2 <- as.matrix(demodis)   # make a full matrix copy
is.na(diag(demodis2)) <- TRUE    # ignore the dissimilarity of a
                                  # plot to itself
apply(demodis2,1,mean,na.rm=TRUE) # calculate the mean dissimilarity
                                  # of every plot to the others

I checked the code, it's now working.

Cheers,

François.

2010/12/9 Dave Roberts <dvrbts at ecology.msu.montana.edu>:
> Burak,
>
>    I think your question is simpler than the suggestions of NMDS.  One
> approach, let's say you dissimilarity matrix is called demodis
>
>> demodis2 <- as.matrix(demodis)   # make a full matrix copy
>> is.na(diag(demodis2)) <- TRUE    # ignore the dissimilarity of a
>                                   # plot to itself
>> apply(as.matrix(demodis),1,mean) # calcualte the mean dissimilarity
>                                   # of every plot to the others
>
>
> This is all done as part of function disana() in package labdsv along with
> some other simple dissimilarity analyses.
>
> Dave Roberts
>
>
>
> On 11/23/2010 01:09 PM, Pekin, Burak K wrote:
>>
>> Hello, I want to rank the dissimilarity of sites based on their species
>> composition. For example, I would like to be able to say that site A is less
>> similar in composition to the other sites than site B is similar to the
>> other sites. I could do a cluster analysis and look at which sites are less
>> closely clustered.
>>
>> It would be even better if I could come up with a quantitative scale
>> rather than a relative ranking that would give a value for each site based
>> on its relative dissimilarity to the rest of the sites. So site A might
>> receive a 90 out of 100, whereas site B and C might receive a 60 and a 50
>> indicating the rank as well as 'relative quantity' of dissimilarity for each
>> site.
>>
>> Thanks,
>> Burak
>>
>> --------------------------
>>
>> Burak K. Pekin, PhD
>> Postdoctoral Research Associate
>> Purdue University
>>
>>
>>        [[alternative HTML version deleted]]
>>
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