similar to: mclust: modelName="E" vs modelName="V"

Hi, I'm trying to use the library mclust for gaussian mixture on a numeric vector. The function Mclust(data,G=3) is working fine but the fitting is not optimal and is using modelNames="E". When I'm trying Mclust(data,G=3,modelName="V") I have the following message: Error in if (Sumry$G > 1) ans[c(orderedNames, "z")] else ans[orderedNames] : argument is

Hi, I got the same problem like http://tolstoy.newcastle.edu.au/R/devel/04/11/1204.html R crashes when I use the em function from the mclust package on univariate data and on a special case on bivariate data (when the matrix is not provided as written in the manual). It seems as if the problem is the format of the data to be analyzed. Operating System: Windows XP (SP2) R version: R-2.0.1 The

I keep on receiving the message below after submitting the following line using the mclust package. m2 is a 99 X 1 column vector. * em(modelName = "E", m2, mu = c(25, 50), sigmasq=10, pro = c(0.4, 0.6)) Error in as.double.default(data) : (list) object cannot be coerced to double. Why do I receive this error? Thank, Brian C. Newquist Research Statistician

Dear list members, I am using Mclust in order to deconvolute a distribution that I believe is a sum of two gaussians. First I can make a model: > my.data.model = Mclust(my.data, modelNames=c("E"), warn=T, G=1:3) But then, when I try to plot the result, I get the following error: > mclust1Dplot(my.data.model, parameters = my.data.model$parameters, what = "density")

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

Hello All... I gave a task to my students that involved using mclust to look for clusters in some bivariate data of isotopes vs various mining locations. They discovered something I didn?t expect; the data (called tur) is appended below. p <- qplot(x = dD, y = dCu65, data = tur, color = mine) print(p) # simple bivariate plot of the data; looks fine mod1 <- Mclust(tur[,2:3]) mod1$G mod2

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

Whenever I submit the following command >result <- em("E", m1, mu=c(25, 50), sigmasq=10, pro=c(0.49,0.51)) in MCLUST , I experience a problem with my Windows XP. Has anyone had this type of problem? I receive the general "send report" dialogue box and my program is no longer able to run. Should I change any of my system settings? Let me know if you think of anything

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,

I'm running Mclust 3.0 in R-2.7.0 and have run into a situation where the optimal number of clusters, 12, seems to be greater than the number of colors available to Mclust. The code below, which demonstrates errors, does not if the max no of clusters is set at 10. sessionInfo, after a restart of R and loading of packages, follows the output. Is this a known problem with Mclust? Can I

Hi All, I need to fit a mutlivariate normal model to a dataset in order to obtain the mean and covariance parameters. I see that the MVN function in the MCLUST package can do this, however when I try to run even the simplest example provided in the documentation, as below, I get the following error: n <- 1000 set.seed(0) x <- rnorm(n, mean = -1, sd = 2) mvn(modelName = "X", x)

> No HTML this time. Sorry Dear all, I am attempting to model some one-dimensional data using Gaussian mixture model with mclust.? Generally, the data that I have have 3 overlapping populations (with one of them being the majority, and the other two combining to less than 15%) and for some reason, mclust consistently ignores the smaller peaks, giving me strange values for the means

Hi All, I have a n x m matrix. The n rows are individuals, the m columns are variables. The matrix is in itself a collection of 1s (if a variable is observed for an individual), and 0s (is there is no observation). Something like: [,1] [,2] [,3] [,4] [,5] [,6] [1,] 1 0 1 1 0 0 [2,] 1 0 1 1 0 0 [3,] 1 0 1 1 0 0 [4,] 0 1 0

Hi, I am trying to run th mclust package on a variable "Tuberculin indurations" recorded as mm. The file has only one variable. When I run the package I get NULL value for mu and sigma. Can anybody say why? This is the program: library("mclust") mc<-Mclust(x.trab,G=1:9,warn=TRUE) mc mc$mu sqrt(mc$sigmasq) and the output I get is > library("mclust")

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 all, I am attempting to model some one-dimensional data using Gaussian mixture model with mclust. Generally, the data that I have have 3 overlapping populations (with one of them being the majority, and the other two combining to less than 15%) and for some reason, mclust consistently ignores the smaller peaks, giving me strange values for the means (completely anti-intuitive in terms of

Dear R users, I use mclust to fit a mixture of normal distributions to many datasets. Usually the Mclust function finds 1 or two normal distributions, rarely, 3. But I hit a strange case today. my.data <- c(57.96920, 51.79415, 51.20538, 55.53637, 51.64291, 56.61476, 51.28855, 55.56169, 51.85113, 54.03330, 51.37370, 49.48561, 52.41580, 53.51176, 60.49293, 55.77012, 51.59270, 56.29660,

Dear all I have a problem in fitting lines() of the normal distributions identified with Mclust on a histogram or a mclust1Dplot. Here is some sample code to explain : set.seed(22) foo <- c(rnorm(400, 10, 2), rnorm(500, 17, 4)) mcl <- Mclust(foo, G=2) mcl.sd <- sqrt(mcl$parameters$variance$sigmasq) mcl.size <- c(length(mcl$classification[mcl$classification==2]),

Hello, I'm trying to use Mclust to fit a Gaussian Mixture Model to a mulitdimensional data set. Because of the specific source of my data, I know that all components have the same variance and that the covariance between dimensions is zero (modelname=VII). Furthermore, I have a reliable estimate of the variance of the components. I want to to use this estimate as a prior in mclust, hoping

Hello I want to batch job the calculation of many GLM-models, extract some values and store them in a file. Almost everything in the script below works (read file, extract values and write them to file) except I fail in indexing the GLM with the modelstructure it should run. Running GLM's conventionally is no problem. Conventionally a GLM is calculated as: