similar to: Weighting data with normal distribution

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 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 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,

Dear R-Users, I have a large number of data(54000) and the field of data is 50 to 2.0e9. I want to use normalmixEM (package:mixtools) to fit them in finite mixture narmal distributions,but get some mistakes.I don't know which steps make the error. I have used the following functions before >x<-read.table("data") >log.x<-log10(x$V1) >log.x<-sort(log.x)

Hi All, I have a set of features of size p and I would like to separate my feature space into two sets so that p = p1 + p2, p1 is a set of features and p2 is another set of features and I want to fit a glm model for each sets of features separately. Then I want to combine the results of two glm models with a parameter beta. For example, beta * F(p1) + (1-beta) * F(p2) where F(p1) is a learned

I am running R 2.7.0 on a Suse 9.1 linux cluster with a job scheduler dispatching jobs and openmpi-1.0.1. I have tried running one of the examples at http://ace.acadiau.ca/math/ACMMaC/Rmpi/examples.html in Rmpi and they seem to be working, except mpi.close.Rslaves() hangs. The slaves are closed, but the master doesn't finish its script. Below is the example script and the call to R. The job is

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

Hi, Is there a way I can view the functions source code of a package I installed in my PC. For example I downloaded the great "mixtools" package. I want to see the source code of one of its function "normalmixEM" Is there a way to do it? Presumably from R command prompt? I tried to take a look at the zip file, but somehow I can't seem to find the file on which I can

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

I have the following function, a MSE calc based on some Multivariate normals: MV.MSE<-function(n,EP,X,S){ (dmvnorm(X,mean=rep(0,2),I+S+EP)-dmvnorm(X,mean=rep(0,2),I+S))^2 + 1/n*(dmvnorm(X,mean=rep(0,2),1+S+EP/2)*det(4*pi*EP)^-.5- (dmvnorm(X,mean=rep(0,2),I+S+EP ))^2)} I can get the MV.MSE for given values of the function e.g

Hi, I want to fit 3 beta distributions to my data which ranges between 0 and 1. What are the functions that I can easily call and specify that 3 beta distributions should be fitted? I have already looked at normalmixEM and fitdistr but they dont seem to be applicable (normalmixEM is only for fitting normal dist and fitdistr will only fit 1 distribution, not 3). Is that right? Also, my data has 26

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

Hi, I have added support for Piv normalization in Tf-Idf weighting scheme as a intermediate step to implementing the support for Piv+ normalization. All tests pass. But I'm running into some issues with Piv+ normalization. In the Piv+ formula , there are two parameters (s and delta) that control the weight assigned. I think the way I'm serialising and unserialising these parameters has

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]

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

I have 2 questions concerning the EM algorithm. Is it true that the EM algorithm gives unique answers for the means and variances of a mixture of 2 normals? I am using mixtools and I am surprised that it works better than a Bayesian program I wrote. If so can someone say why the mixing probabilities are so good? -- Thanks, Jim. [[alternative HTML version deleted]]

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]]

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

This might not be the correct forum for this question for there might be some flaws in my logic so the R function I'm looking for might not be the correct, but I know there?s a lot of smart people in this forum so please correct me if I'm wrong. I have been googling and searching in this forum for something useful but so far I'm out of luck. This is the background to my problem. I

> `ptr` is, if I inferred correctly, a `const char *`. (I'm not sure, > because I don't know why you're incrementing it. Please push your code > to github if you need further help so people can see the entire > context of your changes.) I've pushed all the changes I made so far https://github.com/xapian/xapian/compare/master...ivmarkp:piv+?diff=split&name=piv%2B