similar to: Subsets in mclust

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 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 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 regarding parameters in 2 functions in the "mclust" package. -------------------- PROBLEM DESCRIPTION -------------------- I have a finite mixture modeling problem -- for example, a 2-component gaussian

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)

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

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

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 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

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

I am trying to follow the mclust examples in "MCLUST: Software for Model Based Clustering, Density Estimation and Disriminant Analysis" by Chris Fraley and Adrian Raftery, but I cannot reproduce the density and uncertainty surfaces for the Lansing Woods 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?

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

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 ,

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]]

Hi, Sorry to trouble the list - I would like to ask a question - I can't find the answer in the r-help archives. I am trying to plot 2 survival curves in different colors. What I have tried is this plot(survfit(sim.surv ~ sim.km.smpl),col="red") but both the survival curves turn red... Your advice would be most appreciated! Thank you. Min-Han

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

My question is with respect to mCLUST and the values of BIC and log likelihood. The relevant part of my R script is: ######################### BEGIN MDS ANALYSIS ######################### #load data data <- read.table("Ecoli33_Barry.dis", header = TRUE, row.names = 1) #perform MDS Scaling mds <- metaMDS(data, k = Dimensions, trymax = 20, autotransform =TRUE, noshare = 0.1,

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?

Hello everyone. I have a few MCLUST questions and I was hoping someone could help me out. If you’re an MCLUST user, they will likely be pretty easy to answer. Thanks in advance for any help. Ken What are the pros/cons of starting a finite mixture model at the “m” step versus the “e” step (where “m” is the maximization step and “e” is the expectation step of the EM algorithm)? In

We should prefer `const struct pci_device_id` over `DEFINE_PCI_DEVICE_TABLE` to meet kernel coding style guidelines. This issue was reported by checkpatch. A simplified version of the semantic patch that makes this change is as follows (http://coccinelle.lip6.fr/): // <smpl> @@ identifier i; declarer name DEFINE_PCI_DEVICE_TABLE; initializer z; @@ - DEFINE_PCI_DEVICE_TABLE(i) + const