similar to: clustering of binary data

Hi, Im new to llvm and I'm using llvm-2.6 and I know how to extract the metadata in the stoppoints but what I'm interested in is the meta data regarding a local variable. I know that the @llvm.dgb.declare such as: call void @llvm.dbg.declare({ }* %2, { }* bitcast (%llvm.dbg.variable.type* @llvm.dbg.variable9 to { }*)) links to meta data for @llvm.dbg.variable9 which is:

hello all, for my diploma-thesis i want to statitically analyze near-infrared-spectra. a spectrum is given by the y-values of 1038 equi-distant x-points. in nature, a spectrum is a continuous curve. for analysis, every x-point is seen as a statistical variable. now my problem: first, i read a csv-table in a data.frame called sTable via read.table. besides some meta-data there are 1038 variables

Hi, I am new to clustering and was wondering why pvclust using "maximum" as distance measure nearly always results in p-values above 95%. I wrote an example programme which demonstrates this effect. I uploaded a PDF showing the results Here is the code which produces the PDF file: ------------------------------------------------------------------------------------- s <-

Hi, if you look at the first image (Image1) you see that there are 2 main clusters 7 and 8 I wanted to use pvclust to calculate a p-value whether these clusters are due to chance or statistically significant. Unfortunately pvclust does not provide a p-value for the first brunch (7 and 8). So I added a row to my matrix which is very different to the rest of the data to create an additional

Hi all, I'm new to R and I'm struggling to decipher an error message. Briefly, I am trying to use the pvclust package to do hierarchical clustering of some CGH data. The data is from the Progenetix CGH database. It is arranged as a table where each column is a single case and each row is a single chromosome band. The value in each cell is either 0, 1, 2, or -1. Corresponding to no change,

Hi all I am writing to you with a question regarding the pvclust package. And yes, before the usual people produce their usual contact-the-package-maintainers line, ye, I tried that but the emails one can find on the web either bounce or are not responded to. Also, yes, this error has already been reported as a bug but been shot down as not reproducible

Dear All I'm using PVClust to perform hierarchical clustering, for the output plot I can control most of the graphical I need, however the plot is large and I would like to split it vertically into two panels one above the other. Is there a way to plot only part of a PVClust plot, I tried to convert it to a dendrogram with result2 = as.dendrogram(result) however I get the error message

Is there a way to assign color to nodes as with hclust/as.dendrogram/dendrapply when using pvclust? The problem is that as.dendrogram isn't working on the pvclust objects. library(pvclust) pvc <- pvclust(matrix, nboot=1000) plot(pvc) -- View this message in context: http://www.nabble.com/Node-colors-in-pvclust-tp24405329p24405329.html Sent from the R help mailing list archive at

You should reply to the list, not just me, and even more because I cannot really help you!! My guess (but I don't know this package, and even less this function) is that pvclust() is expecting a matrix as the first argument. However, "cluster" is no data, it is a function. Why, I don't know. Take a closer look at ?pvclust, especially the "usage",

hello, i'd be happy if someone could provide help with the following problem: i have a dist.matrix that comes from vegdist() function of the vegan package. the used method = "horn" is not accepted as argument in hvcluster(...,dist.method="..."). is there a way to incorporate the method "horn" in hvcluster()? thanks in advance! yours, kay -- View this

Hello all, I posted a question to this list last week and received no response. I am unsure if this means no-one knows the answer or if I posed the question badly. I'm going to assume I posed the question badly and try again. I am new to R so it is quite likely it's a very naive question, however if there is something blindingly obvious that I am missing or if there is another resource I

Hi I have a large dataset, containing a wide range of binary variables. I would like first of all to compute a jaccard matrix, then do a PCA on this matrix, so that I finally can do a hierarchical clustering on the principal components. My problem is, that I don't know how to compute the jaccard dissimilarity matrix in R? Which package to use, and so on... Can anybody help me? Alternatively

I realize questions about packages should go to the package maintainer, but perhaps I have an old email address (suzuki3 at is.titech.ac.jp) Also I have both a general, and a specific, question. 1) General question: i've used pvclust before to assess significance of clusters and got reasonable results. However, on a new data set (see below) the results seem odd. I wonder if pvclust is a

Hi all I am trying to perform the follwing: fit<-pvclust(wq, method.hclust="ward", method.dist="euclidean") but get a strange error message that I just cant figure out. Has anyone come across this? Any help would be most appricieated Error in hclust(distance, method = method.hclust) : NA/NaN/Inf in foreign function call (arg 11) In addition: Warning message: NAs

Hello, I am attempting to assign significance levels to a UPGMA cluster analysis as part my doctoral research. The pvclust function works well but doesn't include the similarity index I need (morisita's) as an option for computing a distance matrix. Morisita's is available in vegdist in the VEGAN library but I am having a hard time getting the vegdist function to "direct

Dear All, I would like to use the simprof function (clustsig package) but the available distances do not include Jaccard distance, which is the most appropriate for pres/abs community data. Here is the core of the function: > simprof function (data, num.expected = 1000, num.simulated = 999, method.cluster = "average", method.distance = "euclidean", method.transform =

Hello there, Here's a question regarding p-values on clusters produced by hierarchical cluster analysis. A web search led me to the program pvclust to tackle this problem. But when I run the problem I get strange results. The 'AU' (approximately unbiased) p-values are very different from the 'BP' values (ordinary boot-strap) p-values. The AUs commonly are in the 80-100

Dear List, I have another question to bother you about how to do clustering. My data consists of 49 columns (49 variables) and 238804 rows. I would like to do hierarchical clustering (unsupervised clustering and PCA). So far I tried pvclust (www.is.titech.ac.jp/~shimo/prog/*pvclust* /) but I always had the problem like for R like "cannot allocate the memory". I am curious about what

Does anyone have a good method for calculating Jaccard coefficients now that the dissimilarity() function is no longer an option? Wen Gu John Jay College of Criminal Justice445 West 59 StreetNew York, NY 10029 wgu@gc.cuny.edu _________________________________________________________________ Express your personality in color! Preview and select themes for Hotmail®.

Hi there, I have been trying to use the "pvclust" package but have been having some difficulties. This is the first time I have used R so I am sure the mistake I am making is a basic one. The data I have is a distance matrix and I have been using the command; fit <- pvclust(cluster, nboot=1000, method.dist="euclidean") to try and perform hierarchical clustering with