similar to: rtmvt

I want to use the rtmvt from the {tmvtnorm} package using the "gibbs" algorithm but how to i specify the nested function rtmvnorm to use gibbs as well? Right now I am using the code: for (i in 1:g){ for (j in 1:n){ sgamma[,,i,j] = rtmvt(n=50, mean=mu[i,j], sigma[i,j], df=nu[i], lower=rep(0,2),algorithm="gibbs") } } heres an example of one iteration: >

Dear Members, I am working with 6-dimensional Student-t distribution with 4 degrees of freedom truncated to [20; 60]. I have generated 100 000 samples from truncated multivariate Student-t distribution using rtmvt function from package ?tmvtnorm?. I have also calculated mean vector using equation (3) from attached pdf. The problem is, that after summing all elements in one column of rtmvt result

I am sampling from the truncated multivariate student t distribution "rtmvt" in the package {tmvtnorm}. My question is about the mean vector. Is it possible to define a mean vector outside of the truncated region? Thank you in advance for any help. -- View this message in context: http://r.789695.n4.nabble.com/truncated-distributions-tp3422245p3422245.html Sent from the R help mailing

Dear R users, the tmvtnorm package, the package for the truncated multivariate normal and Student-t distribution, has been updated on CRAN. The major changes in version 1.0-2 (2010-03-04) are: * The package now provides methods for the truncated multivariate Student-t distribution, i.e. random number generation, density function, distribution functions like rtmvt(), dtmvt() und ptmvt() and

Dear R users, the tmvtnorm package, the package for the truncated multivariate normal and Student-t distribution, has been updated on CRAN. The major changes in version 1.0-2 (2010-03-04) are: * The package now provides methods for the truncated multivariate Student-t distribution, i.e. random number generation, density function, distribution functions like rtmvt(), dtmvt() und ptmvt() and

>>>>> David Winsemius <dwinsemius at comcast.net> >>>>> on Tue, 9 May 2017 14:33:04 -0700 writes: >> On May 9, 2017, at 2:05 PM, Czarek Kowalski <czarek230800 at gmail.com> wrote: >> >> I have already posted that in attachement - pdf file. > I see that now. I failed to scroll to the 3rd page. from a late reader:

Hello! I was wondering if it's possible to see the actual code of checkTmvArgs function that is part of the code for rtmvnorm (which is below - I just typed "rtmvnorm" on the prompt). I get an error: Error in checkTmvArgs(mean, sigma, lower, upper) : sigma must be a symmetric matrix At the same time I am pretty sure that the matrix I am passing as sigma is a var-covar matrix

Hello! I am trying to generate a sample from a truncated multivariate normal distribution using rtmvnorm. I am using Gibbs because my alpha (line below) is teeny-tiny ( 4.083475e-64 ) alpha = pmvnorm(lower=lower, upper=upper, mean=btilde, sigma=MyVarCovar). When I try my Gibbs run, it takes quite a long time (with 10,000 iterations and 5,000 iterations burn-in) ~ 22 sec. And then what I get is

Dear R users, the tmvtnorm package, the package for the truncated multivariate normal distribution, has been updated on CRAN. The major changes in version 0.8 are: * Reimplemented the Gibbs' sampler for random number generation in Fortran for performance reasons. This compiled code is now even faster than conventional rejection sampling and allows for the generation of large amounts of

Dear R users, the tmvtnorm package, the package for the truncated multivariate normal distribution, has been updated on CRAN. The major changes in version 0.8 are: * Reimplemented the Gibbs' sampler for random number generation in Fortran for performance reasons. This compiled code is now even faster than conventional rejection sampling and allows for the generation of large amounts of

Hello, thank you very much for your previous answers about the C++ code. I am interested in the application of the Gibbs Sampler in the IRT models, so in the function MCMCirt1d and MCMCirtkd. I've found the C++ source codes, as you suggested, but I cannot find anything about the Gibbs Sampler. All the files are for the Metropolis algorithm. Maybe I am not able to read them very well, by the

I have downloaded the Umacs (Universal Markov chain sampler) and submitted the following sample code from Kerman and Gelman.   s <-Sampler( J=8, sigma.y  =c(15,10,16,11,9,11,10,18),           y  =c(28, 8,-3,7,-1,1,18,12),      theta =Gibbs(theta.update,theta.init),           V =Gibbs(V.update,mu.init),         mu =Gibbs(mu.update,mu.init),         tau =Gibbs(tau.update,tau.init),       

Hi, I want to use R_qsort_int_I() in my C function, but getting the following error. It looks like there is a conflict between Rmath.h, which I use to generate random numbers, and R_ext/Boolean.h I would appreciate any help to fix this problem. gcc -ansi -g -o Gibbs gibbs.c subroutines.o rand.o vector.o -lm -lRmath -llapack -lblas -lfrtbegin -lg2c -lm -shared-libgcc In file included from

Hello R community, following is my code and it shows error, can some one fix this error and explain why this occurs? gibbs <-function(m,n, theta = 0, lambda = 1){ alpha <- 1.5 beta <- 1.5 gamma <- 1.5 x<- array(0,c(m+1, 3)) x[1,1] <- theta x[1,2] <- lambda x[1,3]<- n for(t in 2:m+1){ x[t,1] <- rbinom(x[t-1,3], 1, x[t-1,1])

I try to fit a LDA model to a TermDocumentMatrix with the topicmodels package... but R says: > Error in LDA(TDM, k = k, method = "Gibbs", control = list(seed = SEED, : > x is of class ?TermDocumentMatrix??simple_triplet_matrix? > class(TDM) > [1] "TermDocumentMatrix" "simple_triplet_matrix" I try to use a matrix... but don't work: > MAT

Hi, I have to draw samples from an asymmetric-Laplace-Normal distribution: f(u|y, x, beta, phi, sigma, tau) \propto exp( - sum( ( abs(lo) + (2*tau-1)*lo )/(2*sigma) ) - 0.5/phi*u^2), where lo = (y - x*beta) and y=(y_1, ..., y_n), x=(x_1, ..., x_n) -- sorry for this huge formula -- A WinBUGS Gibbs sampler and the HI package arms sampler were used with the same initial data for all parameters. I

Dear R-users I have a problem choosing initial points for the function arms() in the package HI I intend to implement a Gibbs sampler and one of my conditional distributions is nonstandard and not logconcave. Therefore I'd like to use arms. But there seem to be a strong influence of the initial point y.start. To show the effect I constructed a demonstration example. It is reproducible

I have the following code, gibbs <-function(m,theta = 0.25, lambda =0.55, n =1){ alpha <- 1.5 beta <- 1.5 gamma <- 1.5 x<- array(0,c(m+1, 3)) x[1,1] <- theta x[1,2] <- lambda x[1,3]<- n for(t in 2:(m+1)){ x[t,1] <- rbinom(1, x[t-1,3], x[t-1,1]) x[t,2]<-rbeta(1, x[t-1,1] + alpha, x[t-1,3] - x[t-1,1] + beta) x[t,3]

Dear all, I'm currently experiencing big troubles with my C code called by .Call in a R function. I know this may not be the right place for such things but these errors are driving me crazy. And I hope other people may have already experienced these problems so that they could give me good suggestions. Sorry if I'm completely out of topic. Well let's go... I'm writing a

I read the Ed Gibbs blog posting on BDD, and there was a reference to implementing the game Deflexions as a way of learning about expectations... I''d like to do this exercise, can you provide me with the details (starting points, objectives, etc...) Thanks! Dominique ------------------------------------------------------------- dom.website = http://www.binaryshift.com