similar to: Fitting mixtures with non-linear parameters constraints

Hi R-helpers, I'm trying to model a univariate as a bi-modal normal mixtures. I need to estimate the parameters of each gaussian (mean and sd) and their weights. What's the best way to do this in R? Thanks, Xiao-Jun

This question teeters on the brink of general statistics rather than an R-specific issue, but I'm hoping it boils down to how I coded V&R's EM algorithm for finite mixture modeling and not something worse (i.e. that my plan is impossible). Briefly, I'm trying to simulate an experiment where the hypothesis concerns bimodal vs. unimodal distributions, in order to see what

Hello, I know that Mclust does the fitting on its own but I am trying to implement an optimization with the aim to generate a the mixture gaussian with the combine moments as closed as possible to the moment of my return distribution. The objective is to Min Abs((Mean Ret - MeanFit)/Mean Fit) + Abs((Std Ret -Stdev Fit)/Stdev) + Abs((Sk Ret-Sk fit)/Sk Fit) + Abs((Kurt Ret- Kurt Fit)) Taking

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

Offlist, because this is (a) an opinion and (b) about statistics and therefore offtopic. I don't know whether any such package exists, but I would predict that this is likely to be overdetermined (too many parameters) and therefore unlikely to be a successful strategy. Fitting a mixture of Gaussians is already difficult enough. Feel free to ignore, of course, and no need to reply. Cheers,

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

I don?t see how neither a) or b) applies to this question nor the technical merit of the remark about mixture models. Do you have a suggestion for a more appropriate forum for this issue/question? (stackoverflow basically sent me here). Kind regards > On 29. Jun 2017, at 16:58, Bert Gunter <bgunter.4567 at gmail.com> wrote: > > Offlist, because this is (a) an opinion and (b)

Thanks Ranjan, that provides exactly what I need. I also got a more elaborate answer on stackoverflow (after the same question got rejected form cross validated?) with a running example for this package: https://stackoverflow.com/questions/44825529/package-to-fit-mixtures-of-student-t-distributions/44827220#44827220

Would package "teigen" help? Ranjan On Thu, 29 Jun 2017 14:41:34 +0200 vare vare via R-help <r-help at r-project.org> wrote: > Hello! > > I am new to R (before used python exclusively and would actually call the R solution for this issue inside a python notebook, hope that doesn?t disqualify me right of the batch). > > Right now I am looking for a piece of

gamlss.mx can fit these I believe (although no experience with these myself) flexmix may be (relatively easily) adaptable to accomplish this as well hth, Ingmar Ingmar Visser Universitair Hoofddocent ontwikkelingspsychologie | Directeur College Psychologie Afdeling Psychologie | Faculteit Maatschappij- en Gedragswetenschappen | Universiteit van Amsterdam Bezoek | Nieuwe Achtergracht 129B | Kamer

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! I am new to R (before used python exclusively and would actually call the R solution for this issue inside a python notebook, hope that doesn?t disqualify me right of the batch). Right now I am looking for a piece of software to fit a 1D data sample to a mixture of t-distributions. I searched quite a while already and it seems to be that this is a somehwat obscure endeavor as most

I fitted a gaussian mixture to my financial data. The data can be found here: http://uploadeasy.net/upload/32xzq.rar I look at the density with plot(density(dat),col="red",lwd=2) this has a skew of library(e1071) skewness(dat) -0.1284311 Now, I fit a gaussian mixture according to: f(l)=πϕ(l;μ1,σ21)+(1−π)ϕ(l;μ2,σ22) with:

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

Hi, Before I write code to do it does anyone know of code for fitting mixtures of multivariate-t distributions. I can't use McLachan's EMMIX code because the license is "For non commercial use only". I checked, mclust and flexmix but both only do Gaussian. Thanks Nicholas

I would really appreciate it if someone can give suggestions on how to do spectra fitting in R using ordinary least square fitting and non-negativity constraints. The lm() function works well for ordinary least square fitting, but how to specify non-negativity constraints? It wouldn't make sense if the fitting coefficients coming out as negative in absorption spectra deconvolution. Thanks.

Dear R users: I am wondering how to ask for *fixed* number of distributions under parameterized Gaussian mixture model. I know that em() and some related functions can predict the parameterized Gaussian mixture model. However, there seems no parameter to decide number of distributions to be mixed (if we known the value in advance). That is, assume I know the (mixed)data is from 3 different

Hi All, I apologise if this question has been answered before, but my background is a little different from most people using R, and the language we use seems to be different! I am trying to analyse some nuclear physics data, which consists of an ensemble of "energy" readings in a detector that, when binned, form a number of Gaussian shaped peaks superimposed on a varying background

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

Hey, I am confused about how to generate the sample data from a mixture of Multivariate Gaussian ditribution. For example, there are 2 component Gaussian with prior probability of 0.4 and 0.6, the means and variances are u1=[1 1]', Cov1=[1 0;0 1] and u2=[-1 -1]', Cov2=[1 0;0 1] repectively. So how can I generate a sample of 500 data from the above mixture distribution? Thanks. Fred