ANOSIM with cutree groupings

Question

What i would like to do is an ANOSIM of defined groupings in some assemblage data to see whether the groupings are significantly different from one another, in a similar fashion to this example code:

data(dune)
data(dune.env)
dune.dist <- vegdist(dune)
attach(dune.env)
dune.ano <- anosim(dune.dist, Management)
summary(dune.ano)

However in my own data I have the species abundance in a bray-curtis matrices and after creating hclust() diagrams and creating my own groupings visually by looking at the dendrogram and setting the height. I can then through cutree() get these groupings which can be superimposed on MDS plots etc. but I would like to check the significance of the similarity between the groupings i have created - i.e are the groupings significantly different or just arbitrary groupings?

e.g.

data("dune")
dune.dist <- vegdist(dune)
clua <- hclust(dune.dist, "average")
plot(clua)
rect.hclust(clua, h =0.65)
c1 <- cutree(clua, h=0.65)

I then want to use the c1 defined category as the groupings, which in the example code given was the management factor, and test their similarities to see whether they are actually different via anosim().

I am pretty sure this is just a matter of my inept coding.... any advice would be appreciated.

Answer 1

cutree returns groups as integers: you must change these to factors if you want to use them in anosim: Try anosim(vegdist(dune), factor(c1)). You better contact a local statistician for using anosim to analyse dissimilarities using clusters created from these very same dissimilarities.