similar to: tree class in R?

Hi there, Sorry for the double email. Does R have the packages for the following clustering methods? And if it does, what the commands for them? 1. SOM (Self-organization map) 2. Graph partitioning: 3. Neural network 4. Probability Binning Thank you very much! Linda [[alternative HTML version deleted]]

Hello, I have a question related to recursive partitioning, but I cannot find an answer, likely because I don't know how to properly word my Google search query. All recursive partitioning examples, which I can find, are used for either classification or regression trees like library(tree) data(iris) tree(Species ~ Sepal.Width + Petal.Width, data = iris) which implies building a

I would like to be able to exert certain types of control over the plotting of dendrograms (representing hierarchical clusterings) that I think is best achieved by modifying the dendrogram object prior to plotting. I am using the "dendrogram" class and associated methods. Define the cluster number of each cluster formed as the corresponding row of the merge object. So, if you are

Hi - I'm trying to append simple density histograms of a continuous variable to the leaves of an rpart tree. The splits in the tree are all levels of a factor and I'm hoping to make the histograms out of the subsets of the dataframe corresponding to the splits and for them to be attached to the appropriate leaf of the final tree. Any help would be much appreciated, thanks, Jon Zelner

Hi, How to plot the dendrogram or tree for kmeans, like we do for hclust ? [[alternative HTML version deleted]]

All, I have done a cluster analysis analysis of some spatial locations, based on variables other than lat-long, and want to show the results on a map to see if my clusters have some geographic meaning. For kmeans (etc.) I would just use different symbols (etc.) to distinguish the clusters, but I am doing hierarchical CA. I am looking for ideas or (better) code. One

Unfortunately that's not really an R question. I recommend that you read up on the statistical methods underneath. One that I'd wholeheartedly recommend is Prof. Harrell's `Regression Modeling Strategies'. [BTW, there are now two implementations of gam() in R: one in `mgcv', which is fairly different from that in `gam'. I'm guessing you're referring to the one

Hi Experts, I am new to R, using decision tree model for getting segmentation rules. A) Using behavioural data (attributes defining customer behaviour, ( example balances, number of accounts etc.) 1. Clustering: Cluster behavioural data to suitable number of clusters 2. Decision Tree: Using rpart classification tree for generating rules for segmentation using cluster number(cluster id) as target

Hi all, I have a bit of a newbie question here that I hope y'all can help with. I've run a cluster analysis using hclust on about 500 objects (using R1.3 under Win 2000). The problem is that the tips of the dendrogram are so close together on the plot that the labels overlap and are unreadable. I've used "cex" to reduce the label sizes but this isn't sufficient with so

I have a class with 732 members, so using rpart.plot is giving me a tiny plot in the middle of the window. Is there a good way to modify the plot, or replace the long list with something like "group1"? -- View this message in context: http://r.789695.n4.nabble.com/Plotting-rpart-trees-with-long-list-of-class-members-tp4635671.html Sent from the R help mailing list archive at

Dear R-helpers, I had a conversation with a guy working in a "business intelligence" department at a major Spanish bank. They rely on recursive partitioning methods to rank customers according to certain criteria. They use both SAS EM and Salford Systems' CART. I have used package R part in the past, but I could not provide any kind of feature comparison or the like as I have no

Hello, I've been clustering my data using hclust and cutting the resulting tree with cutree. Separately, I visualize the clusterings with heatmap. Is it possible to have the dendrogram on the heatmap reflect the cutree results? That is, instead of having one large dendrogram, it would have 4 or 25 in the example below. Any guidance on if that's possible or not, and what kinds of

Hi, hres <- hclust(smatr,method="single") hresd<-as.dendrogram(hres) as.dendrogram(hres) `dendrogram' with 2 branches and 380 members total, at height 2514.513 plot(hresd,leaflab="none") #<-error here. #the plotted dendrogram is incomplete. The x axis is not drawn. #The interested reader can download the save(hresd,file="hres.rda") #from the

I'd like to change the way plot.hclust displays an hclust object. Here's a description of how it's done now, from the R documentation of hclust: In hierarchical cluster displays, a decision is needed at each merge to specify which subtree should go on the left and which on the right. Since, for n observations there are n-1 merges, there are 2^{(n-1)} possible

Thanks a lot, Michael! I cc to R-help, where this question really belongs {as the 'Subject' suggests itself...} -- please drop 'bioconductor' from CC'ing further replies. >>>>> "michael" == michael watson (IAH-C) <michael.watson at bbsrc.ac.uk> >>>>> on Thu, 17 Jun 2004 09:16:59 +0100 writes: michael> OK, admittedly it

Hi, Being a novice this is my first usage of R. I am trying to use rpart for building a decision tree in R. And I have the following dataframe Outlook Temp Humidity Windy Class Sunny 75 70 Yes Play Sunny 80 90 Yes Don't Play Sunny 85 85 No Don't Play Sunny 72 95 No Don't Play Sunny 69 70 No Play Overcast 72 90 Yes Play Overcast 83 78 No Play Overcast 64 65 Yes Play Overcast 81 75

Hi, I am a beginner for R. I had use gplots to generate a heatmap as following: >heatmap.2(matrix, col=topo.colors(75), dendrogram="column", Rowv=FALSE, trace="none", key=TRUE, keysize=0.8, density.info="none", cexRow=0.2, cexCol=0.6) It work well. It generate heatmap whith rcolumn clustering dendrogram and I can export a very nice graph. But I don not know how

I have a data set with 6 variables and 251 cases. The people who supplied me with this data set believe that it falls naturally into three groups, and have given me a rule for determining group number from these 6 variables. If I do scaled.stuff <- scale(stuff, TRUE, c(...the design ranges...)) stuff.dist <- dist(scaled.stuff) stuff.hc <- hclust(stuff.dist)

Hello, I have been using the "cophenetic" function for objects of class "dendrogram" and I have realised that it gives different results when it is used with objects of class "hclust". For instance, running the first example in the help file of the "cophenetic" function, d1 <- dist(USArrests) hc <- hclust(d1, "ave") d2 <-

I am trying to create a classification tree using either tree or rpart but when it comes to plotting the results the formatting I get is different than what I see in all the tutorials. What I would like to see is the XX/XX format but all I get is a weird decimal value. I was also wondering how you know which is yes and which is no in each leaf of the tree? Is yes always on the left?