But cutree does away with the indexes from the original input, which rect.hclust retains. I will have no other choice and match that input with the 'values' contained in the clusters ...
Joh Gabor Grothendieck wrote: > If we don't need any plotting we don't really need rect.hclust at > all. Split the output of cutree, instead. Continuing from the > prior code: > >> for(el in split(unname(vv), names(vv))) print(el) > [1] 0.00 0.45 > [1] 1 > [1] 2 > [1] 3.00 3.25 3.33 3.75 4.10 > [1] 5 > [1] 6.00 6.45 > [1] 7.0 7.1 > [1] 8 > > On Dec 21, 2007 3:24 PM, Johannes Graumann <[EMAIL PROTECTED]> > wrote: >> Hm, hm, rect.hclust doesn't accept "plot=FALSE" and cutree doesn't retain >> the indexes of membership ... anyway short of ripping out the guts of >> rect.hclust to achieve the same result without an active graphics device? >> >> Joh >> >> >> >> # cluster and plot >> >> hc <- hclust(dist(v), method = "single") >> >> plot(hc, lab = v) >> >> cl <- rect.hclust(hc, h = .5, border = "red") >> >> >> >> # each component of list cl is one cluster. Print them out. >> >> for(idx in cl) print(unname(v[idx])) >> > [1] 8 >> > [1] 7.0 7.1 >> > [1] 6.00 6.45 >> > [1] 5 >> > [1] 3.00 3.25 3.33 3.75 4.10 >> > [1] 2 >> > [1] 1 >> > [1] 0.00 0.45 >> > >> >> # a different representation of the clusters >> >> vv <- v >> >> names(vv) <- ct <- cutree(hc, h = .5) >> >> vv >> > 1 1 2 3 4 4 4 4 4 5 6 6 7 7 >> > 8 >> > 0.00 0.45 1.00 2.00 3.00 3.25 3.33 3.75 4.10 5.00 6.00 6.45 7.00 7.10 >> > 8.00 >> > >> > >> > On Dec 21, 2007 4:56 AM, Johannes Graumann <[EMAIL PROTECTED]> >> > wrote: >> >> <posted & mailed> >> >> >> >> Dear all, >> >> >> >> I'm trying to solve the problem, of how to find clusters of values in >> >> a vector that are closer than a given value. Illustrated this might >> >> look as follows: >> >> >> >> vector <- c(0,0.45,1,2,3,3.25,3.33,3.75,4.1,5,6,6.45,7,7.1,8) >> >> >> >> When using '0.5' as the proximity requirement, the following groups >> >> would result: >> >> 0,0.45 >> >> 3,3.25,3.33,3.75,4.1 >> >> 6,6.45 >> >> 7,7.1 >> >> >> >> Jim Holtman proposed a very elegant solution in >> >> http://tolstoy.newcastle.edu.au/R/e2/help/07/07/21286.html, which I >> >> have modified and perused since he wrote it to me. The beauty of this >> >> approach is that it will not only work for constant proximity >> >> requirements as above, but also for overlap-windows defined in terms >> >> of ppm around each value. Now I have an additional need and have found >> >> no way (short of iteratively step through all the groups returned) to >> >> figure out how to do that with Jim's approach: how to figure out that >> >> 6,6.45 and 7,7.1 are separate clusters? >> >> >> >> Thanks for any hints, Joh >> >> > > ______________________________________________ > 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. ______________________________________________ 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.
