'grouping' grouping variable
hi jakub,
I would suggest starting with standardizing your environmental variables
with scale(), then compute Euclidean distances with e.g. vegdist() in
{vegan} and run a cluster analysis on the distance matrix with hclust().
Choose a cutoff for minimum dissimilarity and group your sites
accordingly. If you happen to have an idea about the number of groups
you expect, then kmeans() may be an alternative.
cheers, gabriel
On 12/16/11 1:14 AM, Jakub Szymkowiak wrote:
Hello, I have a problem and I don't know how can I solve it. I have one grouping variable (16 regions in my country). Every region is described by several environmental variables, in example arable fields area, woodland area or meadows area. I want to group this regions to small number of groups so that, the similar regions (in terms of my environmental variables) will be in the same group. Any cues, how can I solve this? Cheers, Jakub
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