[R-sig-eco] clustering communities by an environmental variable

[Irene Adamo]

Hi all! Thanks a lot for all the suggestions! I will try with multivariate
regression!

Il giorno sab 25 apr 2020 alle 22:17 Pierre Thiriet <
pierre.d.thiriet using gmail.com> ha scritto:

> Hi
> You might have a look to multivariate regression tree, formerly mvpart
> packagé
> Cheers
> Le 24 avr. 2020, à 18:30, Salvador SANCHEZ COLON <
> salvadorsanchezcolon using prodigy.net.mx> a écrit:
>>
>> Hi Irene,
>>
>>
>> The approach that you are using -i.e., calculating the between-sample dissimilarity with the Bray-Curtis distance, followed by the UPGMA clustering technique- will group your samples based on the values of all the variables contained in your data matrix (which, in ecological studies, usually are species abundance or presence/absence data).
>>
>>
>> I hope this helps but feel free to ask further details.
>>
>>
>> Salvador SÁNCHEZ-COLÓN
>> Independent consultant
>>
>>
>> En Vie, 24 Abril, 2020 en 10:49, Irene Adamo <i.adamo90 using gmail.com> escribió:
>>
>>
>> Para: r-sig-ecology using r-project.org
>> Dear all,
>> I am trying to do a dendrogram by trying to see if my communities by
>> altitude but I am not sure on how to do it.
>> lets say I have this example
>>
>> # calculate Bray-Curtis distance among samples comm.bc.dist <- vegdist(comm,
>> method = "bray") # cluster communities using average-linkage algorithm
>> comm.bc.clust <- hclust(comm.bc.dist, method = "average") # plot cluster
>> diagram plot(comm.bc.clust, ylab = "Bray-Curtis dissimilarity")
>>
>> Now, I would like to cluster my communities by Altitude always using the
>> distance matrix and the dendrogram.
>> Is it possible?
>>
>> Thanks a lot for any help!
>>
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