similar to: AW: estimating number of clusters ("Null or more")

Hi all, once more about the old subj :-) My data has too much various distribution families and for every particular experiment I need just to decide whether the data is "quite homogeneous" or it has two or more clusters. I've revisited the following libraries: amap, clust, cclust, mclust, multiv, normix, survey. And I didn't find any ready-to-use general

Hi I have two questions, the first perhaps dumber than the second. Firstly, I have a data set, and when I plot a histogram it looks like a normal distribution. So I want to overlay a bell-shaped normal distribution on top of it, to demonstrate how similar it is to the normal distribution. I have read the help on dnorm(), rnorm(), pnorm() etc but still can't figure out how to plot a normal

Hi, After problem described in http://oss.oracle.com/pipermail/ocfs2-users/2010- December/004854.html we've upgraded kernels and ocfs2-tools on every node. The present versions are: kernel 2.6.32-bpo.5-amd64 (from debian lenny-backports) ocfs2-tolls 1.4.4-3 (from debian squeeze) We didn't noticed any problems in logs untill last friday, when the whole ocfs2 cluster crashed. We know

Dear group, I want to use the mclust package on large data, and therefore I want to use a subset in the initial clustering phase. From help(mclust): k: If `k' is specified, the hierarchical clustering phase will use a sample of size `k' of the data in the initial hierarchical clustering phase. The default is to use the entire data set. m2 is a

Hi All, I am trying to cluster a one-dimensional data (see the file attached) using Mclust() but got an error message like: >Mclust(x) Error in rep(1, n) : Object "n" not found When I do a simulation sometimes it works sometimes doesn't. >Mclust(c(rnorm(50),rnorm(56,-0.5))) Error in rep(1, n) : Object "n" not found >Mclust(c(rnorm(56),rnorm(56,-0.5))) best

Dear R-users, I have a two - dimensional data set which needs to be clustered into groups: I'm searching for groups of points which show a positive correlation (in a twodimensional plot of the data set), but I do not have any knowledge about how many groups there might be. Do you know of a clustering algorithm in R (or in general) which can use a-priori information about the cluster's

Hi A few days ago I upgraded debian8 to debian9 dovecot is from source # 2.2.36.4 (baf9232c1): /etc/dovecot/dovecot.conf # Pigeonhole version 0.4.24.2 (aaba65b7) # OS: Linux 4.9.0-13-amd64 x86_64 Debian 9.13 Today I get some times in logs: "dovecot-uidlist line 112: Invalid data" and I dont know why This is claster dovecot: dovecot1 - debian8 dovecot2 - debian8 dovecot3 - debian8

> > It would be nice not only for me. > > I agree totally. If you belong to R-contributors group then thanks a lot in advance! > The problem is that you have to formalize what a cluster is, > and this is not a well defined notion. > It has different meanings in different applications. you are right if one follows the idea of full formalization of the notion it

Hi, I would like to know if someone knows an extended implementation of k-means in R to find appropriate number of clusters for a given k-dimensional data. Also, I am working on clustering for forecasting, if someone is interested or has knowledge on implementational details please mail me, I would appreciate it. Regards Skanda Kallur "Cogito, ergo sum" (I think, therefore I

I'like to do cluster analysis by using mahalanobis distance. Could you tell me how to do?

Hi everybody, I'd like to know if there's an easy way for extracting outliers record from a dataset, in order to perform further analysis on them. Thanks Alessandro

Hi, I have ~40,000 rows in a database, each of which contains an id column and 20 additional columns of count data. I want to cluster the rows based on these count vectors. Their are ~1.6 billion possible 'distances' between pairs of vectors (cells in my distance matrix), so I need to do something smart. Can R somehow handle this? My first thought was to index the database with

Hello all (especially MCLUS users). I'm trying to make use of the MCLUST package by C. Fraley and A. Raftery. My problem is trying to figure out how the (model) identifier (e.g, EII, VII, VVI, etc.) relates to the covariance matrix. The parameterization of the covariance matrix makes use of the method of decomposition in Banfield and Rraftery (1993) and Fraley and Raftery (2002) where

Hi list, not really a problem to solve but a strange unwanted behavior (at least I do not like it): I'm loading library(multiv) to use sammon. Later I load library(MASS) which tells me that sammon is masked now, because there is also a MASS version of sammon. Since I believe that the MASS version is at least as good as the multiv version, I would also use the MASS version, but help(sammon)

Hi, I have run rpart to construct a regression tree. Is there any simple method to draw a nice picture of it, as it is usually done in books and paper to visualize the tree? Thank you, Christian *********************************************************************** Christian Hennig Fachbereich Mathematik-SPST/ZMS, Universitaet Hamburg hennig at math.uni-hamburg.de,

Hi, exist a special cluster-analysis algorithms which can work with NA's. a further "problem" is that i want cluster variables not cases to identify special variable-set's. Is it a common way turn the data.frame and use kmeans,because this works with NA's, or have anybody another method for finding "variable-sets" , with exception of factor analysis. thanks for

Hello list, I try to optimize some R code and it turns out that the function as.matrix takes a lot of time in my code. There is only one reason why I need as.matrix: My code should work for matrices with ncol=p and p should be allowed to be 1 or larger. Now I have a matrix x with dim(x)=(n,p), and I need to work with the submatrix y <-x[gv,], (gv a vector of n logicals) and to calculate

Hello list, I want to apply svm from library e1071, and I want to supply class weights. I do not really understand the help entry (and there is no example) class.weights: a named vector of weights for the different classes, used for asymetric class sizes. Not all factor levels have to be supplied (default weight: 1). All components have to be named. I have two

Dear group, I would like to document more than one function on one help page using the Rd language. I tried something like \value{ \code{foo1} returns a list with components \item{arg1}{Argument 1} \item{arg2}{Argument 2} \code{foo2} returns a list with components \item{arg3}{Argument 3} \item{arg4}{Argument 4} } Unfortunately this makes the text "\code{foo2} returns a list

Hi, I want to do something like this: par(mfrow=c(7,7)) for (i in 1:49) hist(RATDACOM[SUBJNO==i],breaks=0.5+(0:6), main="",xlab="",ylab="",xaxt="n",yaxt="n") (Don't think about what RATDACOM and SUBJNO are.) I get an error Error in plot.new() : Figure margins too large. 36 histograms with mfrow=c(6,6) work. But the 36 histograms