I am using R to generate a pairwise distance matrix of a large dataset (3000 grid cells) and I am interested in grouping cells into ten clusters.
I have successfully used the "cutree" function in the CLUSTER package in R to get 10 clusters; but I got stuck on how to create a new matrix based on mean of all pairwise grid cell values for the new clusters defined by my cutree function. What I am interested in is to use a summary (i.e. mean) of the new clusters to get a new dendrogram based ONLY on the means. I only want to show a dendrogram of the means of the summarized clusters (i.e. the 10 clusters) which I retrieved using the cutree function so that I can further do some analyses e.g. metaMDS in vegan.
I have looked up different R help pages to see whether I can get a new "summary" dendrogram based on the "mean of all pairwise grid cells observed between the clusters", but unsuccessful.
I attached a picture as an example of what I hope to represent.
Any help especially in the form of R code will be highly appreciated.
The function meandist finds the mean within and between block dissimilarities.
With the function meandist(), there is the function mrpp (in vegan library) : Multiple Response Permutation Procedure provides a test of whether there is a significant difference between two or more groups of sampling units.
yes, I agree that the combination of mrpp() and meandist() in vegan will give you want you are looking for
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