similar to: package to fit mixtures of student-t distributions

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

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)

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,

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

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

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

Hi everybody, I am currently working on mixtures of two densities ( f(xi,teta)= (1-teta)*f1(xi) + teta*f2(xi) ), particularly on the behavior of the variance for teta=0 (so sample only comes from the first distribution). To determine the maximum likelihood estimator I use the Newton-Rapdon Iteration. But when computing the first derivative I get a none linear function (with several asymptotes)

Hello, I have a concrete statistical question: I have a sample of an univariate mixture of an unknown number (k) of normal distributions, each time with an unknown mean `m_i' and a standard deviation `k * m_i', where k is known factor constant for all the normal distributions. (The `i' is a subscript.) Is there a function in R that can estimate the number of normal distributions k

Hi people, Does anybody know some Density-Based Method for clustering implemented in R? Thanks, Fernando Prass _______________________________________________________

Dear R Users?? I woould like to generate mixture model data. I try two type method as followings two method, can anyone tell me which is right? or better? 1. generate two vectors data y1 and y2 from f1 and f2 seperately, and calculate y=alpha1*y1+alpha2*y2, 2. generate prob=unif(1), if prob <alpha1 y=y1, else y=y2. Can anyone give me some idea? Thanks so much! Goeland goeland at

I want to create the mixture formulation of a discrete uniform ie, say f(x) = 1/10, for i = 1,2,3,4,5,6,7,8,9 and 10 and another discrete distribution which has the same values of x, but he probabilities can vary. Can this be done on any package in R? an if so, can the package estimate the 'probabilities' of each of the x value as well as the mixing proportion if I have the data?

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

If you submit these lines, you end up with variable "vare.dis". I want to export vare.dis to csv. Stuck I am. library(vegan,logical.return = TRUE) #return=true verifies package is available library(MASS,logical.return=TRUE) #return=true verifies package is available data(varespec) #varespec is an example data file in the vegan package vare.dis <- vegdist(varespec)

Hello This is a simple question but I couldn't google an answer. In the procrustes function of the vegan package, one uses plot(procrustes_object, kind=2) to obtain a plot of the residual differences. For instance: data(varespec) vare.dist <- vegdist(wisconsin(varespec)) library(MASS) mds.null <- isoMDS(vare.dist, tol=1e-7) mds.alt <- isoMDS(vare.dist,

Recently, I have been working with some data that look like two overlapping gaussian distributions. I would like to either 1) determine the mean and SD for each of the two distributions OR 2) get some (bayesian ?) statistic that estimates how likely an observation is to belong to the left-hand or right-hand distribution In case I'm using the wrong language, my data looks something like

Dear all, I realised a correspondence analysis with function cca() of vegan library. Just like in Okansen (2010) in the example of R help: library(vegan) data(varespec) data(varechem) vare.cca<-cca(varespec~ Al + P + K, varechem) With plot.cca() function I represented the species matrix in the next way: plot(vare.cca,display="species") Being similar to: plot((c(-2,2)),(c(-2,2)),

Hi, I have a couple questions about fitting environmental (land use factors, plant species presence-absence, and soil variables) constraints to my CCA biplot. 1. After successfully plotting species and site scores in my CCA, I have been trying to insert the biplot arrows of the environmental constraints in my data set using the text() function. When I do that, the plot changes completely. Is there

Let's say I have a program that returns variables whose names may be any string within the vector NAMES=c("varA","varB","varC","varD","varE","varF"..."varZ"), but I do not ever know which ones have actually been created. So in one example output, "varA", "varC", and "varD" could exist, but

I performed canonical correspondence analysis (cca) with the example data of vegan, but I'm not able to obtain a table like scores() for the constraining variables. I can see them in the summary() mode, but it would be great to have in a separate table. Any suggestion?, thanx Gianandrea require(vegan) data(varespec) data(varechem) vare.cca<-cca(varespec,varechem) scores(vare.cca)

Dear List, I am writing a custom panel function and xyplot method to plot the results of a procrustes analysis from the vegan package. I am having trouble getting the call to panel.arrows to work as I wish when conditioning. The attached file contains the function definitions for the xyplot method and the custom panel and prepanel functions I am using. This example, using data and functions from