similar to: clustering

Displaying 20 results from an estimated 400 matches similar to: "clustering"

2003 May 07
1
-means, hybrid clustering or similar implementations on R
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
2003 Aug 05
2
Error on mclust
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
2002 Nov 05
2
Canonical variates
Hi, Does anybody know some package or function that computes canonical variates? Luis & Janete -------------------------------------------- sapo.pt/kitadsl -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the
2002 Feb 14
1
Subsets in mclust
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
2003 Feb 27
2
qda plots
Hi, I have been using some of the functions in r for classification purposes, chiefly lda, qda, knn and nnet. My problem is that the only one I can figure out how to represenent graphically is lda (using plot.lda). I have tried 'fooling' this function into accepting qda input for plotting but to no avail. I wonder if you have any suggestions? Thanks alot, Anne Marie Power Marine lab.
2004 Oct 04
3
Help with normal distributions
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
2003 Apr 24
1
AW: estimating number of clusters ("Null or more")
Dear Christian, first of all thank you for your answer. I am going to parse through the pages you told me. Meanwhile I'd like to note that probably it is a good idea to put 2-3 lines of R-code demonstrating such a simple needs somnewhere in docs of `cluster' package. E.g. x<-rnorm(500) ... # output means we have rather 1 claster x<-c(rnorm(500), rnorm(500)+5)
2003 Aug 11
2
cluster analysis
I'like to do cluster analysis by using mahalanobis distance. Could you tell me how to do?
2003 Apr 24
1
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
2003 Nov 16
1
help with EMclust
we have implemented teh following code for determinging the clustering model of a dataset. bicvals <- EMclust( hdata, 7) sumry1 <- summary(bicvals, hdata,7) # summary object for emclust() print(sumry1) This set of code gives the following output classification table: 1 2 3 4 5 6 7 1 1 1 4 1 1 1 which I think means there is 1 gene in the 1st cluster...1 gene in the 2nd cluster ,
2006 Jan 05
4
Q: R 2.2.1: Memory Management Issues?
Dear Developers: I have a question about memory management in R 2.2.1 and am wondering if you would be kind enough to help me understand what is going on. (It has been a few years since I have done software development on Windows, so I apologize in advance if these are easy questions.) ------------- MY SYSTEM ------------- I am currently using R (version 2.2.1) on a PC running Windows 2000
2003 Sep 11
3
Rgui access violation
Dear All; While using EMclust() in the mclust package, I frequently encountered a program error. A message window popped up with the message " Rgui.exe has generated errors and will be closed by Windows. You will need to restart the program. An error log is be created." > version _ platform i386-pc-mingw32 arch i386 os mingw32
2005 Oct 21
1
finite mixture model (2-component gaussian): plotting component gaussian components?
Dear Knowledgeable R Community Members, Please excuse my ignorance, I apologize in advance if this is an easy question, but I am a bit stumped and could use a little guidance. I have a finite mixture modeling problem -- for example, a 2-component gaussian mixture -- where the components have a large overlap, and I am trying to use the "mclust" package to solve this problem. I need
2004 Dec 15
3
Massive clustering job?
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
2006 Mar 06
2
[Q] BIC as a goodness-of-fit stat
Dear R-List I have a question about how to interpret BIC as a goodness-of-fit statistic. I was trying to use "EMclust" and other "mclust" library and found that BIC was used as a goodness-of-fit statistic. Although I know that smaller BIC indicates a better fit, it is not clear to me how good a fit is by reading a BIC number. Is there a standard way of interpreting a BIC
2004 Jun 07
2
MCLUST Covariance Parameterization.
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
2002 Dec 13
1
clustering dissimilarities
Hello. I know my dissimilarity matrix but not my original data. Is there any way i could use the clustering function Mclust or EMclust with this dissimilarity matrix? or at least some equivalent of these functions? As this is model based clustering i dont know if it is actually possible to do it without the original data thanks in advance for your help [[alternate HTML version deleted]]
2003 May 24
1
predicting fuzzy cluster membership
Dear all, I'm trying to obtain a fuzzy clustering with fanny from the cluster package, using a given set of data. That worked just fine. I have another separate sample of data from the same problem. For each case in this new sample I would like to know their membership coefficients with respect to the clustering obtained with the first dataset. In effect I want to have a kind of prediction
2011 Mar 30
4
a for loop to lapply
Dear all, I am trying to learn lapply. I would like, as a test case, to try the lapply alternative for the Shadowlist<-array(data=NA,dim=c(dimx,dimy,dimmaps)) for (i in c(1:dimx)){ Shadowlist[,,i]<-i } ---so I wrote the following--- returni <-function(i,ShadowMatrix) {ShadowMatrix<-i} lapply(seq(1:dimx),Shadowlist[,,seq(1:dimx)],returni) So far I do not get same results
2003 Jul 17
3
univariate normal mixtures
Hello, I have a concrete statistical question: I have a sample of an univariate mixture of an unknown number (k) of normal distributions, each time with an unknown mean `m_i' and a standard deviation `k * m_i', where k is known factor constant for all the normal distributions. (The `i' is a subscript.) Is there a function in R that can estimate the number of normal distributions k