similar to: EM Algorithm in R

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

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

Dear all, Currently I have the following output > mydensity <- density(x) > print(mydensity) x y Min. : -92.14 Min. :0.000e+00 1st Qu.: 356.66 1st Qu.:5.530e-09 Median : 805.45 Median :4.681e-05 Mean : 805.45 Mean :5.564e-04 3rd Qu.:1254.24 3rd Qu.:3.370e-04 Max. :1703.04 Max. :5.541e-03 How can I access the Max value of

Hi, I have this string, in which I want to extract some of it's element: > x <- "Best-K Gene 11340 211952_at RANBP5 Noc= 3 - 2 LL= -963.669 -965.35" yielding this array [1] "211952_at" "RANBP5" "2" In Perl we would do it this way: __BEGIN__ my @needed =(); my $str = "Best-K Gene 11340 211952_at RANBP5 Noc= 3 - 2 LL= -963.669

Hi, I have two histograms created separately using the following code. It creates two separate figures. dat <- read.table(file="GDS1096.modelout", head = FALSE ) __BEGIN__ dat <- read.table(file="GDS1096.modelout", head = FALSE ) hist(dat$V2, main="AIC Freq", xlab = "\# Component", breaks = 36, xlim = c(0,max(dat$V2)), col = "dark red",

Hi, How can I convert the matrices to list. For example I have this snippet: samples<-mymatrix[1,] print(samples) which prints: V1 V2 V3 V4 V5 V6 1 103.9 88.5 242.9 206.6 175.7 164.4 How can I convert the object "samples" such that it prints: [1] 103.9 88.5 242.9 206.6 175.7 164.4 The reason I ask this because I can't use the former "samples"

Hi, Below I have a function mlogl_k, later it's called with "nlm" . __BEGIN__ vsamples<- c(14.7, 18.8, 14, 15.9, 9.7, 12.8) mlogl_k <- function( k_func, x_func, theta_func, samp) { tot_mll <- 0 for (comp in 1:k_func) { curr_mll <- (- sum(dgamma(samp, shape = x_func, scale=theta_func, log = TRUE))) tot_mll <- tot_mll + curr_mll }

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Dear R expert I am a student and I am currently conducting a research project on the Modeling Loss Index Triggers to price Cat Bonds: Application of the risk of hurricanes in USA. I need to solve with R (especially with EM algorithm) this specific problem below. CRAN Package archive doesn't seem to have it also the statistical modeling journal didn't contain a paper that implements this:

Dear all, How can I compute the log likelihood of a gamma distributions of a vector. I tried the following. But it doesn't seem to work: samples<-c(6.1, 2.2, 14.9, 9.9, 24.6, 13.2) llgm <- dgamma(samples, scale=1, shape=2, log = TRUE) It gives [1] -4.291711 -1.411543 -12.198639 -7.607465 -21.397254 -10.619783 I expect it only returns "one" value instead of vector.

Hi, I tried to compute maximum likelihood under gamma distribution, using nlm function. The code is this: __BEGIN__ vsamples<- c(103.9, 88.5, 242.9, 206.6, 175.7, 164.4) mlogl <- function(alpha, x) { if (length(alpha) > 1) stop("alpha must be scalar") if (alpha <= 0) stop("alpha must be positive") return(- sum(dgamma(x, shape = alpha, log = TRUE)))

Dear R-Users, I have a model with a latent variable for a spatio-temporal process. I would like to use EM algorithm to estimate the parameters. Does anybody know how to implement the algorithm in R? Thank you very much in advance, Michela

I am creating a side-by-side histogram using "multhist" of PLOTRIX. However, I can't seem to control the X-axis. It yields the following X points in the axis: 1 3 5 7 9 13 17 21 25 29 33 with uneven spacing between x-ticks (see attached file). Especially I want to create a bar for every x-axis (1,2,3,4...36), now not every x-axis has a bar. Is there a way to alter my following

Hello, Is there any literature there that says that the EM is better/worse than a Baysian model when it comes to differentiating univariate mixture of normal distributions? -- Thanks, Jim. [[alternative HTML version deleted]]

Dear all members, I am Phd. candidate student at Chulalongkorn U., Thailand. I am interested in expectation maximization algorithm (EM) and multiple imputation (MI) for imputation missing values(missing at random(MAR) and missing not at random (MNAR)) in IRT (3-PL). So, I want to know about package in R or function of EM and MI for simulate this problem I am looking forward your answer.

Hello, I apologize that this is off-topic. I am seeking information on perception of graphical data, in an effort to improve the plots I produce. Would anyone point me to literature reviews in this area? (Or keywords to try on google?) Is this located somewhere near cognitive science, psychology, human factors research? For example, some specific questions I have are: I recall as a child

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.

Hello everyone. I have a few MCLUST questions and I was hoping someone could help me out. If you’re an MCLUST user, they will likely be pretty easy to answer. Thanks in advance for any help. Ken What are the pros/cons of starting a finite mixture model at the “m” step versus the “e” step (where “m” is the maximization step and “e” is the expectation step of the EM algorithm)? In

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, could someone send me examples of the EM Clustering algorithm and/or some pdf describing it? Thanks, Douglas Sousa.