similar to: Number of components in a mixture model

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 Weibull mixture -- where the components have a large overlap, and I am trying to adapt the "mclust" package which concern to normal

Dear R-help, I am doing clustering via finite mixture model. Please suggest some packages in R to find clusters via finite mixture model with continuous variables. And also I wish to verify the distributional properties of the mixture distributions by fitting the model with lognormal, gamma, exponentials etc,. Thanks in advance,  warm regards,Ms.Karunambigai M PhD Scholar Dept. of Biostatistics

Dear list, Can anyone provide a package or code for estimating the parameters of a mixture of c (c >=2) univariate normal distributions? I've tried the algorithm provided by Venables & Ripley (1999) p 263, for the mixture of two normal, but I don't find the "ms" function in R. I've used nls instead, but I'm not sure if it works the same. The data I have is very

Dear R-experts, I have some questions about boxplots with lattice. My data is similar as in the example below, I have two factors (Goodness of Fit and Algorithms) and data values but in each panels the scales are quite different, therefore the normal boxplots produced by set.seed(1) GOF <- factor(rep(c("GOF1","GOF2","GOF3"),each=40)) Alg <-

Hi! Does anyone know how to do the test for goodness of fit of a logistic model (in rms package) after running fit.mult.impute? I am using the rms and Hmisc packages to do a multiple imputation followed by a logistic regression model using lrm. Everything works fine until I try to run the test for goodness of fit: residuals(type=c("gof")) One needs to specify y=T and x=T in the fit. But

I would like to fit a gaussian mixture model to a vector with about 50,000 points. I have tried using Mclust to do so, but 50,000 points requires more memory than I have (and I am running with 4Gb). Any other suggestions for how to do so? Oh, I don't know the number of components, but the number will likely be less than 5 or 6. Thanks, Sean

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<FONT face="Default Sans Serif, Verdana, Arial, Helvetica, sans-serif" size=2><div>I was wondering if anyone knew there are functions to do mixture modeling in R.</div><DIV>&nbsp;</DIV><DIV>Many thanks in advance,</DIV><DIV>Peter

Hey, Dose R include some package for Gaussian Mixture Model data generation and parameters estimation? Now I want to assign lots of multivariate data into a GMM model. So just wondering if given sample data, can we use some functions to estimate the compoents' density function. Thanks for your support. Fred -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-

Are there any S-Plus or R libraries/packages that do Finite Mixture Analysis following the algorithms similar to those implemented in Geoffrey MacLachlan's EMMIX program? Thanks. Dr. Marc R. Feldesman email: feldesmanm at pdx.edu email: feldesman at attglobal.net fax: 503-725-3905 "Don't know where I'm going. Don't like where I've been. There may be no exit. But

Dear list members, Within a loop, I need to create an xyplot with only a legend, not even with the default external box drawn by lattice. I already managed to remove the axis labels and tick marks, but I couldn't find in the documentation of xyplot how to remove the external box. I would really appreciate any help with this ------------- START ----------- library(lattice) x<-1:100

Hello, I was looking for a way to evaluate the goodness-of-fit of a logistic regression model. After googling, I found that I could use "resid(fit, 'gof')" method implemented in the rms package. However, since I am not used to the "le Cessie-van Houwelingen normal test statistic," I do not know which statistic from the returned from the "resid(fit,

? Hallo, which function can i use to do (baseline) logistic regression + goodness of fit tests? so far i found: # logistic on binary data lrm combined with resid(model,'gof') # logistic on binary data glm with no gof-test # baseline logit on binary data

I am trying to test goodness of fit for my legalistic regression using several options as shown below.  Hosmer-Lemeshow test (whose function I borrowed from a previous post), Hosmer–le Cessie omnibus lack of fit test (also borrowed from a previous post), Pearson chi-square test, and deviance test.  All the tests, except the deviance tests, produced p-values well above 0.05.  Would anyone please

Hi, There is a point which is irritating me currently quite a bit and that is an aspect of different behaviour between the %in% function and the smaller/bigger than signs (<>). Here is are two examples to demonstrate what I mean: Example1: > c(1,1,2,2,3,4,4,6,7) %in% c(1,2,3) [1] TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE Right, that is what I expect. Example2: > ps

Dear R-List, to assess the model fit for 2PL-models, I tried to mimic the bootstrap-approach chosen in the GoF.rasch()-function. Not being a statistician, I was wondering whether the following simplification (omit the "chi-squared-expressed model fit-step") would be appropriate: GoF.ltm <- function(object, B = 50, ...){ liFits <- list() for(i in 1:B){ rndDat <-

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 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

Hello, I would like to calculate a cluster analysis and I use the function 'hclust'. I have seen the GRAPHICAL results of this function with 'plot'. I would like to analyse this cluster but I don't know how to see the NUMERICAL results of each step of this cluster like: - R Squared - Pseudo F - Pseudo t**2 Thank you for any help, Pierre-Olivier Chasset ===== Pierre-Olivier

I have used Sweave a lot the latest year, but never really used any long function calls. If I have code which look like this ------------------------------------------------------------- gof <- benthic.flux(ID="Gulf of Finland", meas.conc=conc, bw.conc=bw.conc, time=times,