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Dears, I have the below code for metropolis of the GLM logit (logistic regression) using a flat prior. Can someone help me modify the prior so that the model becomes hierarchical by using a flat prior for mu and sigma, the derived density for beta ~ N(mu, sigma^2)? Actually I took my code from a teacher that posted on the internet and modif...

I'm testing some different formulations of pairwise interaction point processes in Spatstat (version 1.11-6) using R 2.5.0 on a Windows platform and I wish to simulate them using the Metropolis-Hastings algorithm implemented with Spatstat. Spatstat utilizes Fortran77 code with the preprocessor RatFor to do the Metropolis-Hastings MCMC, but the Makefile is more complicated than any I have worked with. Any suggestions on how I could get started working with the Fortran code in conjunction...

...4C:4E ipHostNumber: 192.168.30.254 ipHostNumber: 192.168.90.252 ipHostNumber: 192.168.31.2 ipHostNumber: 192.168.33.253 owner: uid=psaborano,ou=accounts,dc=telbit,dc=pt manager: uid=psaborano,ou=accounts,dc=telbit,dc=pt description: Compaq deskpro Pentium III 500MHz cedido pela PTIN o: PTIN dn: cn=metropolis,ou=sistemas,dc=telbit,dc=pt objectClass: top objectClass: device objectClass: ieee802Device objectClass: ipHost cn: metropolis owner: uid=psaborano,ou=accounts,dc=telbit,dc=pt description: Pentium 4 2.40GHz, RAM=1G, 2xSATA 80G, 1xATA 80G, DVD+-RW macAddress: 00:0C:6E:13:65:3E ipHostNumber: 192.168...

Hello all, could you tell where is an example of metropolis hasting? Thank you! Tania Sent from my iPod

Is there a way to recover Metropolis-step acceptance rates AFTER completing posterior draws? The immediate application is in the probit.bayes and logit.bayes models used by Zelig... which I believe is merely calling MCMCpack. So one strategy, to which I am fixing to resort, is to call, say, MCMClogit with verbose set to mcmc...

...;s encrypted message and decrypts it with his Private key. Then Bob sends half rest of encrypted message to Alice. *(7)* Alice combines the two parts of Bob's encrypted message and decrypts it with her Private key. Here is the Code (running well in my Slackware Linux). I named this program as Metropolis, consist of two parties, the Miracle and the Sleeper. It's assumed that you have exchange your public key to your partner in secure way: SOURCE CODE METROPOLIS (Also attached) #################################################################### # Implementation of Interlock Protocol i...

Hi R Users: This package provides two generic functions for performing Markov chain sampling in a naive way for a user-defined target distribution, which involves only continuous variables. The function "gibbs_met" performs Gibbs sampling with each 1-dimensional distribution sampled with Metropolis update using Gaussian proposal distribution centered at the previous state. The function "met_gaussian" updates the whole state with Metropolis method using independent Gaussian proposal distribution centered at the previous state. The sampling is carried out without considering any spec...

...),1) theta1<-matrix(0,n,2) theta1[1,]<-mu for(i in 2:n){ theta<-theta1[i-1,] phi<-theta+runif(-0.5,0.5) k<-rbinom(1,1,alpha(theta,phi)) k1<-k1+k theta1[i,]<-theta+k*(phi-theta) } plot(theta1) Thanks alot :) -Mike -- View this message in context: http://r.789695.n4.nabble.com/Metropolis-Hastings-in-R-tp4472547p4472547.html Sent from the R help mailing list archive at Nabble.com.

...described persists if I just run the MCMCmetorp1R call (with the variables renamed of course) on its own and not as part of my posterior.sampler function. Any idea what's going on? Thanks, Dan Cervone -- View this message in context: http://r.789695.n4.nabble.com/Possible-bug-with-MCMCpack-metropolis-sampler-tp4639199.html Sent from the R help mailing list archive at Nabble.com.

Following is my code, can some one help on the error at the bottom? > mh<-function(iterations,alpha,beta){ + data<-read.table("epidemic.txt",header = TRUE) + attach(data, warn.conflicts = F) + k<-97 + d <- (sqrt((x-x[k])^2 + (y-y[k])^2)) + p <- 1-exp(-alpha*d^(-beta)) + p.alpha<-1 - exp(-3*d^(-beta)) + p.beta <- 1 - exp(alpha*d^(-2)) +

Hi, Do any body know which packages of R I need to go for the below topics? 1. Monte Carlo Markov chain (MCMC) 2. Gibbs Sampling 3. Metropolis Hastings Thanks in advance... Shubha [[alternative HTML version deleted]]

Hello, Consider MCMC sampling with metropolis / metropolis hastings proposals and a density function with a given valid parameter space. How are MCMC proposals performed if the parameter could be located at the very extreme of the parameter space, or even 'beyond that' ? Example to express it and my very nontechnical 'beyond t...

...gular file Device: 29h/41d Inode: 35820037 Links: 1 Access: (0774/-rwxrwxr--) Uid: ( 3535/ jh3) Gid: ( 513/Domain Users) Access: 2016-11-03 19:51:46.000000000 +0000 Modify: 2016-11-01 13:06:04.000000000 +0000 Change: 2016-11-01 13:06:04.000000000 +0000 Birth: - [root at zalma ~]# ssh metropolis "stat testfile" root at metropolis's password: ... Size: 1286700 Blocks: 2514 IO Block: 65536 regular file Device: 26h/38d Inode: 35820037 Links: 1 Access: (0774/-rwxrwxr--) Uid: ( 3535/ jh3) Gid: ( 513/Domain Users) Access: 2016-11-03 19:51:46.000000000...

...er.matrix[i,]<- c(rnorm(1, 0, (1/parameter.matrix[i-1,3])), rnorm(1, 0, (1/parameter.matrix[i-1,3])), rgamma(1, shape=(a0+1), rate=(1/b0+(parameter.matrix[i-1,1]^2+parameter.matrix[i-1,2]^2)/2))) # as the Gamma with specified parameters is the conditional for tau given beta, data # implementing Metropolis-Hastings within Gibbs to get estimates of beta0 and beta1 proposal.b0[i]<-sum(log( ((exp(parameter.matrix[i,1])^y)/((1+exp(parameter.matrix[i,1])^n))*exp(-parameter.matrix[i-1,3]*(parameter.matrix[i,1]^2)/2)))) proposal.b1[i]<-sum(log( ((exp(parameter.matrix[i,2]*x)^y) / ((1+exp(param...

...ponds to the expression value of one particular gene in one of 32 samples. And the Y presents the corresponding classes (0-non cancer, 1-cancer) for those 32 samples. The formula is Y~. All other parameters are default. This is the output message: @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ The Metropolis acceptance rate for beta was 0.00000 @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ Warning messages: 1: In glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, : fitted probabilities numerically 0 or 1 occurred 2: In glm.fit(x = X, y = Y, weights = weights, start = sta...

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

Hello, I have written a simple Metropolis-Hastings MCMC algorithm for a binomial parameter: MHastings = function(n,p0,d){ theta = c() theta[1] = p0 t =1 while(t<=n){ phi = log(theta[t]/(1-theta[t])) phisim = phi + rnorm(1,0,d) thetasim = exp(phisim)/(1+exp(phisim)) r = (thetasim)^4*(1-thetasim)^8/(theta[t]^4*(1-theta[t])^8...

...writing several functions and running out variable names. I am using words such as "t", "c", "matrix" to keep the notation same as formulas I am using. For example I have, unnormalized <- function(t, x, y){ val <- rnorm(t, mean=x, var=y) return(val) } metropolis <- function(t, c, x, y){ den1 <- unnormalized(t, mean=x, sd=y) den2 <- unnormalized(c, mean=x, sd=y) if(den1 < den2) return(a) else return(b) } for(i in 1: 100){ matrix <- c() matrix[i] <- metroplis(1, 2, 3, 4) } Here, I reused letter "...

...fixed. But eventually, I have to run optim on the unmodified likelihood function above and it is very slow, taking several days for large problems. I have also tried metrop() in mcmc, but I find this needs to be very close to the mode of the likelihood to be efficient (in fact, MCMCpack's metropolis function calls optim first and even requires it to invert the hessian before even starting the metropolis algorithm, unless we can provide our own covariance matrix). I will probably use metrop() to generate standard errors once I find a mode.... In the mean time, I can't help thinking th...

...rom CRAN. SUMMARY The package provides functions to perform the fitting of an adaptive mixture of Student-t distributions to a target density through its kernel function. The mixture approximation can then be used as the importance density in importance sampling or as the candidate density in the Metropolis-Hastings algorithm to obtain quantities of interest for the target density itself. We believe that this approach may be applicable in many fields of research and hope that the R package AdMit will be fruitful for many researchers like econometricians or applied statisticians. MODIFICATIONS o ch...