similar to: Package for maximizing likelihood function with EM algorithm

Hi, R Users; I am trying to maximize a likelihood function which contains an integral. The integral contains the unknown parameter as well. I am trying to use the following code to do the maximization: ll<-function(b.vec){ b0<-b.vec[1] b1<-b.vec[2] b2<-b.vec[3] p<-1/(1+exp(-b0-b1*z1-b2*x2))

for learning purposes and also to help someone, i used roger peng's document to get the mle's of the gamma where the gamma is defined as f(y_i) = (1/gammafunction(shape)) * (scale^shape) * (y_i^(shape-1)) * exp(-scale*y_i) ( i'm defining the scale as lambda rather than 1/lambda. various books define it differently ). i found the likelihood to be n*shape*log(scale) +

Hello, I have a log likelihood function that I was able to optimize using mle2. I have two years of the data used to fit the function and I would like to fit both years simultaneously to test if the model parameter estimates differ between years, using likelihood ratio tests and AIC. Can anyone give advice on how to do this? My likelihood functions are long so I'll use the tadpole

Salve, this is just to probe whether other fans of an unimplemented feature exist or what priority/difficulty compiz coders would assign to it. I constantly used ?maximize vertically? (and also ?maximize horizontally?) in Metacity when there wasn't enough space to see a portion of text in a terminal or another window. Assigned to <Super_R>Up/<Super_R>Right it was a powerful pair

I want to maximize a conditional likelihood function that is basically logistic conditional on the number of successes within strata. What would be a good starting place for this? A complication is that the denominator includes a term that is the sum over all permutations. Although there is no time dimension to the problem, it's possible a degenerate use of the Cox proportional hazards

Hi all, I'm curious if anyone has coded an Expectation-Maximization algorithm that could help me model some circular data I have. I'd like to model it as a mixture of uniform and Von Mises centered on 0, so the only free parameters is the mixing proportion and the kappa of the Von Mises. I couldn't find anything in the contributed packages that seemed to suit this purpose. Any

Hello, I am looking for documentation (PDF ?) about doing clustering with the EM Algorithm on R... I'm sorry for this question which may be trivial... Thanks... Dominique

Dear list, The task view on optimization does not reference a package for non linear constrained optimization problems. Stefan Theussl told me to look at the Rsolnp package, but unfortunately it is not very clear what method is R ported. (The authors ported the matlab code of Yinyu Ye http://www.stanford.edu/~yyye/ <http://www.stanford.edu/%7Eyyye/>) Currently I'm looking for an

Hello All,   I¢m  kinda new to R language and any help that I can get is greatly appreciated.   Basically, I want to find the values of the two parameters that will maximize the function and I¢m currently using the optim function to find these values.  My R code works fine but not all the time.  Sometimes the solution converges, sometimes not.   Since I¢m planning to do this 5000 times, is there a

Dear All, have a general question about coefficients estimation of the mixed model. I simulated a very basic model: Y|b=X*\beta+Z*b +\sigma^2* diag(ni); b follows N(0,\psi) #i.e. bivariate normal where b is the latent variable, Z and X are ni*2 design matrices, sigma is the error variance, Y are longitudinal data, i.e. there are ni

I have the following item response theory (IRT) likelihood that I want to maximize w.r.t. to theta (student ability). L(\theta) = \prod(p(x)) Where p(x) is the 3-parameter logistic model when items are scored dichotomously (x_{ij} = 0 or 1) and p(x) is Muraki's generalized partial credit model when items are scored polytomously (x_{ij} = 0 \ldots J). Now, I wrote the following two functions

I recently got a new macbook pro 13", 2.4 Ghz, running Mac OS X 10.6.3. I am having trouble maximizing the Wine Desktop window that opens when I try running applications. It could be possible that the Wine window can't be stretched or made any bigger, but I'm not sure. If I try to make the window bigger by dragging the bottom right corner, the window gets larger, but it just adds a

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 random sample of size 100 from this model. I will do this 300 times. That means, I will have

Hi, I am looking for a package to do the clustering analysis using the expectation maximization algorithm. Thanks in advance. Ming

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 am trying to understand how the functions em() and me() from the mclust package work. I cannot make sense of what the algorithm returns. Here is a basic, simple example: ######################################### # two bivariate normals, centered at (-5,0) and (5,0), with Id covariance x1 = cbind(rep(-5, 100), rep(0, 100)) + matrix(rnorm(100*2), 100, 2) x2 = cbind(rep(5, 100),

hi, I want to use EM for MLE estimation.WHAT would be the relevant package or code?? Regards, Subhajit. [[alternative HTML version deleted]]

Hi, I woul like to know if it is possible to have a "R code" to generate EM Algorithm for a normal bivariate mixture. Best regard, S.F.

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

Hello, i need some help concerning the library norm. i habe to impute some missing values using the em algorithm. The help offered for the library doesn't really help me, maybe somebody already worked on em algorithm or multiple imputation. some fictive Data x1 x2 50 60 24 . 26 20 87 . 21 . Problem: Em Algorithm in R calculating the missings in x2. Thanks in advance. -- View this