Hi Tina, What's wrong with what you did?
The output object of som() contains the classification of each sample. You probably do need to read more about self-organizing maps, since you specified you wanted the samples classified into nine groups, and that's unlikely to be your actual intent. I have no idea what you thought your hierarchical clustering step was supposed to do, either. Here's one way to get 3 groups instead of 9: library(kohonen) iris.sc <- scale(iris[, 1:4]) iris.som <- som(iris.sc, grid=somgrid(xdim = 1, ydim=3, topo="rectangular"), rlen=100, alpha=c(0.05,0.01)) table(iris.som$unit.classif, iris$Species) plot(iris.som) Sarah On Wed, Oct 10, 2018 at 2:14 AM A DNA RNA <email2m...@gmail.com> wrote: > > Dear All, > > Who can I use Self Organizing Map (SOM) results to cluster samples? I have > tried following but this gives me only the clustering of grids, while I > want to cluster (150) samples: > > library(kohonen) > iris.sc <- scale(iris[, 1:4]) > iris.som <- som(iris.sc, grid=somgrid(xdim = 3, ydim=3, topo="hexagonal"), > rlen=100, alpha=c(0.05,0.01)) > ##hierarchical clustering > groups <- 3 > iris.hc <- cutree(hclust(dist(iris.som$codes[[1]])), groups) > iris.hc > #V1 V2 V3 V4 V5 V6 V7 V8 V9 > #1 1 2 1 1 2 3 3 2 > > > Can anyone help me with this please? > -- > Tina -- Sarah Goslee http://www.functionaldiversity.org ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
