similar to: EM and Mixtools

Dear Ingmar & Dave, Thanks a lot for your help and sorry for the late reply. Finally, I've found a way to separate the mixture of distributions (empirically). But the gamlss package looks great, I'm sure it will help me during my further studies. Kind regards, Susanne On 15 Jun 2009, at 20:09, Ingmar Visser wrote: > Dear Susanne & Dave, > > The gamlss package family

Hello, Can the x and y axis limits be specified in a density plot with the mixtools package for a finite mixture model? Uncommenting the xlim2/ ylim2 lines in the plot command below generates 'not a graphical parameter' warnings (and does not change the axis settings), and uncommenting the xlim/ylim lines generates a 'formal argument "ylim" matched by multiple actual

Hello, Is there any literature there that says that the EM is better/worse than a Baysian model when it comes to differentiating univariate mixture of normal distributions? -- Thanks, Jim. [[alternative HTML version deleted]]

Moved to R-help because there's no obvious financial content. Michael On Sat, Apr 6, 2013 at 10:56 AM, Stat Tistician <statisticiangermany at gmail.com> wrote: > Hi, > I want to implement the EM algorithm manually, with my own loops and so. > Afterwards, I want to compare it to the normalmixEM output of mixtools > package. > > Since the notation is very advanced, I

Hi, I would like to know if it is possible to have a "R code" to estimate the parameters of a mixture of bivariate (or multivariate) normals via EM Algorithm. I tried to write it, but in the estimation of the matrix of variance and covariance, i have some problems. I generate two bidimensional vectors both from different distribution with their own vector means and variance and

I couldn't find a direct answer in CRAN to this question, so I'm asking with some trepidation. I have a multivariate dataset (data.frame) with columns that can be expressed as a set of mixed normals (at least I think) and need to impute values that have constraints (truncated mixture of normals where the values cannot be below zero). If there isn't a package that can do this, is there

Dear all, I am attempting to model some one-dimensional data using a mixture model of non-central Student's t distributions. However, I haven't been able to find any R package that provides this functionality. Could there be a way to "manipulate" the EM algorithms from the mixdist or mixtools package to fit the model, or do you have any other suggestions? If anyone could help

Hello I am trying to find an automated way of fitting a mixture of normal and log-normal distributions to data which is clearly bimodal. Here's a simulated example: x.1<-rnorm(6000, 2.4, 0.6)x.2<-rlnorm(10000, 1.3,0.1)X<-c(x.1, x.2) hist(X,100,freq=FALSE, ylim=c(0,1.5))lines(density(x.1), lty=2, lwd=2)lines(density(x.2), lty=2, lwd=2)lines(density(X), lty=4) Currently i am using

Dear List: I have uploaded version 1.0-4 of DPpackage on CRAN. Since the first version (1.0-0), I have not communicated the improvements of the package. I'll use this email to summarize its current status. The name of the package is motivated by the Dirichlet process. However, DPpackage tries to be a general package for Bayesian nonparametric and semi-parametric data analysis. So

Dear List: I have uploaded version 1.0-4 of DPpackage on CRAN. Since the first version (1.0-0), I have not communicated the improvements of the package. I'll use this email to summarize its current status. The name of the package is motivated by the Dirichlet process. However, DPpackage tries to be a general package for Bayesian nonparametric and semi-parametric data analysis. So

Hi, I am currently working on fitting a mixture density to financial data. I have the following data: http://s000.tinyupload.com/?file_id=00083355432555420222 I want to fit a mixture density of two normal distributions. I have the formula: f(l)=πϕ(l;μ1,σ21)+(1−π)ϕ(l;μ2,σ22) my R code is: normalmix<-normalmixEM(dat,k=2,fast=TRUE) pi<-normalmix$lambda[1] mu1<-normalmix$mu[1]

I am trying to install mixtools on Debian Etch and get the following error dell-2 /usr/lib # R CMD INSTALL mixtools_0.1.0.tar.gz * Installing *source* package 'mixtools' ... ** libs gcc -I/usr/share/R/include -I/usr/share/R/include -fpic -g -O2 -std=gnu99 -c new_svalues.c -o new_svalues.o gfortran -fpic -g -O2 -c sphericaldepth.f -o sphericaldepth.o make: gfortran: Command not

Hello all, I would like to fit a mixture model whose components are normal distributions confined in a closed interval. Since there are already several packages for EM, I would like to extend one of these instead of writing a new script from scratch. What would be the best way to customize such an existing package for doing that? Which one is the best in terms of extensibility? (e.g mixtools,

We are pleased to announce the release of version 0.0 of bayesm on CRAN. bayesm covers many important models used in marketing and micro-econometrics applications. The package includes: Bayes Regression (univariate or multivariate dep var) Multinomial Logit Multinomial and Multivariate Probit Multivariate Mixtures of Normals Hierarchical Linear Models with a normal prior and covariates

We are pleased to announce the release of version 0.0 of bayesm on CRAN. bayesm covers many important models used in marketing and micro-econometrics applications. The package includes: Bayes Regression (univariate or multivariate dep var) Multinomial Logit Multinomial and Multivariate Probit Multivariate Mixtures of Normals Hierarchical Linear Models with a normal prior and covariates

Hi, as you can see in the topic, I am trying to fit a normal mixture distribution with the approach suggested by Hamilton (1991). Since I couldn't find any existing packages including the quasi-bayesian mle, I have to write my own function. Unfortunately, I have absolutely no experience in doing this. If you're not familiar with the QB-MLE, I attached the formula as pdf. The idea

Hi folks, I have circular data that I'd like to model as a mixture of a uniform and a Von Mises centered on zero. The data are from 2 groups of human participants, where each participant is measured repeatedly within each of several conditions. So, the data frame would look something like: ######## design = expand.grid( person = 1:20 , variable1 = 1:2 , variable2

Hello all, May be silly question, but what exactly is beta parameter in functions like regmixEM from mixtools package? I mean, how to determine this beta, if i have a set of metrics for each case? Is there a function for that? I have try to put NULL at this parameter, but function just do not work in this case. Cheers, Dima [[alternative HTML version deleted]]

Please help me in writing the R code for this problem. I've been solving this for 4 days. It was hard for me to solve it. It's a simulation problem in R. The problem is My true model is a normal mixture which is given as 0.5 N(-0.8,1) + 0.5 N(0.8,1). This model has two components. I will get a sample size equal to 100 from this model. I will do this 200 times. That means, I will have

Can anyone send me the R code for fitting a mixture of normal distributions. Or in fact any'combination' of mixtures of distributions. I looke dta mix but it is still not clear to me. Jim [[alternative HTML version deleted]]