similar to: Multivariate mixture distributions

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

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

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

Hello, I would like to fit a mixture of two Weibull distributions to my data, estimate the model parameters, and compare the fit of the model to that of a single Weibull distribution. I have used the mix() function in the 'mixdist' package to fit the mixed distribution, and have got the parameter estimates, however, I have not been able to get the log-likelihood for the fit of this model

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

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

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

Hi all, How can we use ks.test() to evaluate goodness of fit of mixtures of distributions? For example I have the following dataset: > x [1] 176.1 176.8 259.6 171.6 90.0 234.3 145.7 113.7 105.9 176.2 168.9 136.1 [13] 109.2 110.3 164.3 117.7 131.3 163.7 200.4 196.4 196.2 168.6 190.4 127.5 [25] 136.0 114.2 112.0 91.9 333.4 295.5 172.0 293.3 91.7 289.7 118.8 55.1 [37] 161.9 233.9 197.7

Dear List, I have read that a lognormal mixture model having a pdf of the form f(x)=w1*f1(x)+(1-w1)*f2(x) fits most data sets quite well, where f1 and f2 are lognormal distributions. Any pointers on how to create a function that would produce the 5 parameters of f(x) would be greatly appreciated. > version _ platform i386-pc-mingw32 arch i386 os

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, Does anyone know how to compute the quantile of a mixture of four bivariate normal distriutions? Many thanks! Hannah [[alternative HTML version deleted]]

Hi, i would to know, if someone have ever write the code to estimate the parameter (mixing proportion, mean, a var/cov matrix) of a mixture of two multivariate normal distribution. I wrote it and it works (it could find mean and mixing proportion, if I fix the var/cov matrix), while if I fix anything, it doesn't work. My suspect is that when the algorithm iterates the var/cov matrix, something

Hi, i would to know, if someone have ever write the code to estimate the parameter (mixing proportion, mean, a var/cov matrix) of a mixture of two multivariate normal distribution. I wrote it and it works (it could find mean and mixing proportion, if I fix the var/cov matrix), while if I fix anything, it doesn't work. My suspect is that when the algorithm iterates the var/cov matrix, something

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

hi Suppose I have a mixture of 2 distributions generated by rtwonormals <- function(npnt,m1,s1,m2,s2,p2){ rv<-vector(npnt,mode="numeric") for( i in seq(1:npnt)){ if(runif(1,0,1)<=p2){ rv[i]<-rnorm(1,m2,s2) } else{ rv[i]<-rnorm(1,m1,s1) } } return(rv) } x <- rtwonormals(50000,0,100,500,500,0.05) #and I try to fit these with (based on thread: [R]

Hi all, I suspect the answer to this query will be the tongue-in-cheek "use a quantum computer", but I thought my understanding might be sufficiently limited that I'm missing a simpler option. I'm looking for a way to cycle through all possible combinations of 2 groups of data. For 10 data points, that's 2^10 combinations, for 20 data points it's 2^20, etc. combn() from

Dear List, I am looking for a possibility to fit a mixture model under R using maximum likelihood estimation. Venables and Ripley describe a solution working under S+ (in MASS, 3. ed., p. 263) which requires the D system function and deriv3. This solution does not seem to be portable to R or at least I do not realise how. Is there anyone who a) knows how one could make the MASS-method run under

I have been authoring a very small R package on CRAN, named "normix" which implements an S3 class "norMix" has plot and print methods; further, E[X] and Var[X] methods, random number generation ("r") and density evaluation. It also provides the 16 "Marron-Wand densities" (known in the (1d) density estimation business). Erik J?rgensen has provided

I have been authoring a very small R package on CRAN, named "normix" which implements an S3 class "norMix" has plot and print methods; further, E[X] and Var[X] methods, random number generation ("r") and density evaluation. It also provides the 16 "Marron-Wand densities" (known in the (1d) density estimation business). Erik J?rgensen has provided

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