Hi, Thanks for the replies - they have helped shaped my thinking and are starting to push me in a better direction. Maybe I should explain a little more about what I'm trying to achieve.
I am analysing satellite data across the global ocean, and am interested in trying to classify areas of the ocean according to the similarity between the pixels. Singletons in this case therefore represent individual pixels that are different to the rest in terms of the similarity metric, but aren't really all that interesting in terms of the broad picture - I consider them "outliers" or "noise". However, they are annoying when it comes to splitting up the dendrogram, because I'm mainly interested in the reclassification of large areas of ocean at each step, rather than changes in the similarity. The dynamic tree-cut approach looks like a promising and sensible solution to the problem - I'll see if I can get something out of it. However, this discussion has started me wondering how I can use the spatial proximity of the pixels in the analysis - does anyone have any insights? Can the WGCNA approach be used in such a context? Best wishes, Mark Payne ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
