similar to: Question about acception rejection sampling - NOT R question

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 that': The von Mises distribution is a circular

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 (or mcmc divided by an integer) and then look at my screen.

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]]

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

I'm trying to shade part of a density plot. The code I'm trying (using the Old Faithful data as an example) is something like this: # The Old Faithful geyser data data(faithful) d <- density(faithful$eruptions, bw = "sj") plot(d) polygon(d[d$x>4], col = "wheat") I expected that the part of the curve to the right of 4 on the x axis should be shaded, but nothing

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

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) if(runif(1,0,1)<r){ theta[t+1] = thetasim }

Eric B's response provided just the kind of quick & simple solution I was hoping for (appears as the function com below). However, I once again failed to take advantage of the power of R and have reverted back to using a for loop for the next step of the processing. The example below (which requires the library EGRET for pulling an example dataset) works, but probably can be replaced

Hi, All. I have a barplot I am making that has a 2 line label: names(GC) <- c("Read-Off", "Spatial\n Transformations", "Vis Changes", "Plans", "Abandon") and here, Spatial Transformations should be on 2 separate lines. However, when I break it as above (with the \n), the "Spatial" part is pushed up so that it overlaps the Tick mark

Hello, I would like to plot : barplot(c(10,12,18)) and plot(c(0,2,3), t="l") and many other plots... in a same scale. With par(new=T) it's not align, points are not in the middle of the bar of barplot. Is there an easely solution to align that ? Thank for you help, G [[alternative HTML version deleted]]

Hallo, I'm dynamically generating buttons depending on the number of rows of my dataframe. Every button is supposed to call a function which generates a plot with the values of one of my dataframe rows. My code looks like this: base <- tktoplevel() plotten <- function(mat, namen, titel) { midpts <- barplot(height=mat, names.arg = namen, main = titel, las=2)

Hi Eric, the following works for me. HTH, Eric library(EGRET) StartDate <- "1990-10-01" EndDate <- "2017-09-30" siteNumber <- "10310000" QParameterCd <- "00060" Daily <- readNWISDaily(siteNumber, QParameterCd, StartDate, EndDate) # Define 'center of mass' function com <- function(x) { match(TRUE, cumsum(x/sum(x)) > 0.5) -

Dear list, After running for a while, it crashes and gives the following error message: can anybody suggest how to deal with this? Error in if (ratio0[i] < log(runif(1))) { : missing value where TRUE/FALSE needed ################### original program ######## p2 <- function (Nsim=1000){ x<- c(0.301,0,-0.301,-0.602,-0.903,-1.208, -1.309,-1.807,-2.108,-2.71) # logdose

Can someone address the boot once option. Is it to hard for syslinux to write a flag file to file system or the blank space after the mbr? .conf bootonce Lable /boot/flagfile or .conf bootonce Lable1 /boot/flagfileLable1 bootonce Lable2 /boot/flagFileLable2 bootonce Lable2 /boot/flagFileLable2 skip the default option if the flag file is 0 size. Write something to the

Hello! I am using package "VAR". I've fitted my model: mymodel<-VAR(mydata,myp,type="const") I can extract the Log Liklihood for THE WHOLE MODEL: logLik(mymodel) How could I calculate (other than manually) the corresponding Akaike Information Criterion (AIC)? I tried AIC - but it does not take mymodel: AIC(mymodel) # numeric(0) Thank you! -- Dimitri Liakhovitski

Hello, I would like to calculate the 95% confidence intervals for the estimates of a linear model and I just wanted to check that I am doing it correct. Is it just: Estimate + 1.95996*Std.Error to Estimate - 1.95996*Std.Error or is there another approach that doesn't assume a normal distrbution? Thanks. Apologies for my naiivity Dan --

Dear all, I have been trying to fit my data (only right censored) with gumbel distribution using fitdistrplus. I am getting very high standard error. I have been wondering why. The followings are the outputs: fit1=fitdistcens(dr0, "gumbel", start=list(a=99, b=0.6), optim.method= "L-BFGS-B", lower = 0.0, upper = Inf) > summary(fit1) FITTING OF THE DISTRIBUTION ' gumbel

Dear R users, I am doing MCMC using Metropolis-Hastings. My model is bivariate-log-normal and the prior for variance-covariance is wishart distribution. I am wondering if there are some simple codes about how to get the density of Wishart distribution in my case ? Thanks in advance. Meihua [[alternative HTML version deleted]]

Dear R-helpers, Hi! All. I'm doing a project which needs MCMC simulation. I wonder whether there exists related packages in R. The only one I know is a MCMCpack package. What I want to do is implementing gibbs sampling and Metropolis-Hastings Algorithm to get the posterior of hierarchical bayesian models. Thanks in advance. Jun

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