search for: 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 , 4 genes in th...

...ap, and I am trying to use the "mclust" package to solve this problem. I need to decompose that mixture into its 2 components which will need to be plotted. ----------- QUESTIONS ----------- What I don't know how to do is: (1) restrict the number of components to 2 in the "EMclust" function (2) obtain and plot a component gaussian density Regarding (1), I think this should be the 'G' value but the documentation is somewhat cryptic as regards the format. Regarding (2), I think I need to use the "cdens" function, but again the documentation is somewh...

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

...ing 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 matrix (not very large, but the same happens always when specifying k): > dim(m2) [1] 242 2 > em321 <- emclust(m2,k=50) Error in mhtree.VVV(data[smpl, ]) : data must be a matrix with at least 2 columns > em321 <- emclust(m2) (No problems) > em321 <- emclust(m2,modelid="VEV",k=50) Error in mhtree.VVV(data[smpl, ]) : data must be a matrix with at least 2 columns > library(help=mclus...

...cannot allocate vector of size 1115070Kb In addition: Warning messages: 1: Reached total allocation of # Mb: see help(memory.size) 2: Reached total allocation of # Mb: see help(memory.size) Execution halted -------------------------------------------------------- after attempting line: summary(EMclust(y),y) which is computationally intensive (performs a "deconvolution" of the data into a series of Gaussian peaks) and where # is either 766Mb or 2048Mb (depending on the max memory size I set). The call I make is to Rterm.exe (to try to avoid Windows overhead): "C:\Program Files\R\...

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 _ platf...

...e the components have a large overlap, and I am trying to use the "mclust" package to solve this problem. I need to decompose that mixture into its 2 components which will need to be plotted. What I don't know how to do is: (1) restrict the number of components to 2 in the "EMclust" function (2) obtain and plot a component gaussian density Regarding (1), I think this should be the 'G' value but the documentation is somewhat cryptic as regards the format. Regarding (2), I think I need to use the "cdens" function, but again the documentation is somewha...

...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 shape, in particular that the points of a given cluster should lie on a line? The "EMclust" algorithm does allow to chose a general shape, such as "elliptical" for example. However, this method proved not to be successful. Thank you very much, Yours sincererly Philip

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 value? Thanks in advance....

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

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]]

...t: R memory problem Date: Fri, 13 Jul 2001 13:35:18 -0700 Dear Kurt, My name is Inge Monika. I'm currently using R version 1.3.0 to do clustering analysis for my data set. My data set consists of 7 variables and 21120 observations. Now, I have trouble increasing my vector size to do the "emclust" function for my data. I only could increase my vector size up to 2047M which is still too small. I keep getting an error message "cannot allocate vector of size 82Kb" after I applied the emclust function. Could you tell me what's that mean? And how can I solve that problem? My s...

...es 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) ... # output means we have rather 2 or more claster It would be nice not only for me. > EMclust of library mclust decides about an optimal number of mixture > components using the BIC. It is not clear for me whether one could use BIC without a statement about the familiy of distribution. Indeed BIC is based on likelihood, and what the likelihood should be if the only adequate statement...

...0.1, wascores = TRUE, expand = TRUE, trace = FALSE, plot = FALSE, old.wa = FALSE) ######################### BEGIN EM ANALYSIS ######################### #Use the points determined by MDS to perform EM clustering. #Allow only the unconstrained models. Sometimes, constrained models mess things up! EMclusters <- mclustBIC(mds$points, G=Clusterrange, modelNames= c("VII", "VVI", "VVV"), prior=NULL, control=emControl(), initialization=list(hcPairs=NULL, subset=NULL, noise=NULL), Vinv=NULL, warn=FALSE, x=NULL) The input data are in the form of an N...

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?

...oods maples. I am using R 1.8.1 with the code below. The same code works fine in S-Plus 6.2 Am I missing something or is this a bug? Thanks Mike White library(mclust) data(lansing) # R only maples<-lansing[as.character(lansing[,"species"]) == "maple", -3] maplesBIC <- EMclust(maples) maplesModel <- summary(maplesBIC, maples) plotMaples2 <- function(type, what, transformation) { out <- do.call("surfacePlot", c(maplesModel, list(data=maples, type=type, what=what,transformation=transformation))) invisible() } par(pty="s", mfrow=c(1,2)) plotMa...

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

I have not followed the history of this thread, but I am quite flummoxed as to why the OP is rewriting code to estimate parameters from an univariate Gaussian mixture model when alternatives such as EMCluster (which generally appears to handle initialization better than MClust) exist. Or perhaps there is more to it in which case I apologize. But I thought that I would make the OP aware of the package (of which, in full disclosure, I am co-author). Best wishes, Ranjan On Sun, 27 Aug 2017 16:01:59 +0...

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