similar to: hclust and cutree: identifying branches as classes

Dear R-Help, I have a clustering problem with hclust that I hope someone can help me with. Consider the classic hclust example: hc <- hclust(dist(USArrests), "ave") plot(hc) I would like to cut the tree up in such a way so as to avoid small clusters, so that we get a minimum number of items in each cluster, and therefore avoid singletons. e.g. in this example, you can see

Hello fellow R users, I am currently learning to use R, so please forgive me if there is an obvious explanation for the following problem. My goal is to perform WGCNA on a dataset of 19776 genes, so I opted to follow the block-wise network construction (Section 2c) in the WGCNA R Tutorial by Peter Langfelder and Steve Horvath. I ended up with 10 blocks and am able to plot the dendrograms and

Changes: - patch v2: - Adapt patch to work post KPTI and compiler changes - Redo all performance testing with latest configs and compilers - Simplify mov macro on PIE (MOVABS now) - Reduce GOT footprint - patch v1: - Simplify ftrace implementation. - Use gcc mstack-protector-guard-reg=%gs with PIE when possible. - rfc v3: - Use --emit-relocs instead of -pie to reduce

Changes: - patch v2: - Adapt patch to work post KPTI and compiler changes - Redo all performance testing with latest configs and compilers - Simplify mov macro on PIE (MOVABS now) - Reduce GOT footprint - patch v1: - Simplify ftrace implementation. - Use gcc mstack-protector-guard-reg=%gs with PIE when possible. - rfc v3: - Use --emit-relocs instead of -pie to reduce

Changes: - patch v3: - Update on message to describe longer term PIE goal. - Minor change on ftrace if condition. - Changed code using xchgq. - patch v2: - Adapt patch to work post KPTI and compiler changes - Redo all performance testing with latest configs and compilers - Simplify mov macro on PIE (MOVABS now) - Reduce GOT footprint - patch v1: - Simplify ftrace

Hi, I have produced a dendrogram of categorical data in R using the hclust function, although the input was a dissimilarity matrix produced in SAS, as I have defined my own distances. The dendrogram is fine and I can view and use this. However, I was wondering if there is a method by which I can find out the optimal place to place groups, rather than relying on my visual analysis? I don't

Hi there, I set up a Samba 3. Server on a SUSE 9.1 box, working as a PDC. There are Win9X and Win2K Clients connecting to the server. At a certain time (has been at 1 to 48 hours of uptime), the clients loose connection to the server. At this point, it is not possible to log into the server on a new terminal window, but running processes are still running and reacting. I disconnected the

How to get the name of the computer accessing the application? I mean the computer the user use to access the application. Thanks, Lantis. -- Posted via http://www.ruby-forum.com/.