similar to: Gibbs sampler

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 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),       

I am writing a Gibbs sampler. I think it is outputting some of what I want, in that I am getting vector of several thousand values (but not 10,000) in a txt file at the end. My question is, is the error message (see below) telling me that it can't output 10,000 values (draws) because of a limitation in my memory, file size, shape etc, or that there is an error in the sampler itself? >

Hey everyone, I have read the paper by Ritter and Tanner(1992) on Griddy-Gibbs sampler and I am trying to implement it in R without much luck. I was wondering if anyone had used this or could point me to any example code. Thanks, Liz --------------------------------- [[alternative HTML version deleted]]

I have got following error in drawing wavelet fitting. can some one help? > library(faraway) > data(lidar) > newlidar<-lidar[c(1:128),] > library(wavethresh) > wds <- wd(newlidar$logratio) > draw(wds) Error in plot.default(x = x, y = zwr, main = main, sub = sub, xlab = xlab, : formal argument "type" matched by multiple actual arguments [[alternative HTML

Dear all: I am using R to do multiple imputation for longitudinal data set. The Gibbs chain basically requires draw posterior distribution of model parameters, including the random effects. The multiple imputation requires several independent Gibbs chains. So my program structure is like: for (chain in 1:5) { # perform Gibbs sampling... for (row in 1:row.no) { b.row=some function # draw

Hi all I have a function of log defined by y = log(1- exp(-a)), when exp(-a) is greater, 1, it produce NaN. How can I remove this in R? [[alternative HTML version deleted]]

Hello everyone, I have some binary, spatially autocorrelated data I would like to run autologistic regression on. I hope to incorporate both ordinary covariates (environmental predictors) and a spatial autocovariate in the model, ideally with a second-order neighbourhood structure. Since my computing skills are limited, I am wondering if anyone has composed an algorithm for this purpose, and

Hi group, I imported 16 data frames using the function "list.files" temp <- list.files(path="...........") myfiles = lapply(temp, read.table,sep = "") Now I have 16 data set imported in R window. I want to combine them by row and tried some thing like (Here I am considering only 20 columns) for(i in 1:16){ data<- cbind(myfiles[[i]][,1:20]) } but it

Hi all, I have the data from the posterior distribution for some parameter. I want to find the 95% credible interval. I think "t.test(data)" is only for the confidence interval. I did not fine function for the Bayesian credible interval. Could some one suggest me? Thanks [[alternative HTML version deleted]]

Hi folks, I have a question about how to efficiently produce random numbers from Beta and Binomial distributions. For Beta distribution, suppose we have two shape vectors shape1 and shape2. I hope to generate a 10000 x 2 matrix X whose i th rwo is a sample from reta(2,shape1[i]mshape2[i]). Of course this can be done via loops: for(i in 1:10000) { X[i,]=rbeta(2,shape1[i],shape2[i]) } However,

Hi all, why factorial(150) shows the error out of range in 'gammafn'? I have to calculate the number of subset formed by 150 samples taking 10 at a time. How is this possible? best [[alternative HTML version deleted]]

Hi R community, I have some data set and construct the likelihood as follows likelihood <- function(alpha,beta){ lh<-1 d<-0 p<-0 k<-NULL data<-read.table("epidemic.txt",header = TRUE) attach(data, warn.conflicts = F) k <-which(inftime==1) d <- (sqrt((x-x[k])^2+(y-y[k])^2))^(-beta) p<-1 - exp(-alpha*d) for(i in 1:100){

I'd appreciate it if you'd keep on list for the archives. That said, I think this function does what you were hoping for. Michael powerset <- function(n, items = NULL){ if(!is.null(items)) { if(n != length(items)) warning("Resetting n in preference to length(items)") n = length(items) } smat <- do.call(expand.grid, rep(list(c(0,1)), n))

Hi R community! I am analyzing the data set "motorins" in the package "faraway" by using the generalized additive model. it shows the following error. Can some one suggest me the right way? library(faraway) data(motorins) motori <- motorins[motorins$Zone==1,] library(mgcv) >amgam <- gam(log(Payment) ~ offset(log(Insured))+ s(as.numeric(Kilometres)) + s(Bonus) + Make +

I have a problem in where i generate m independent draws from a binomial distribution, say draw1 = rbinom( m , size.a, prob.a ) then I need to use each draw to generate a beta distribution. So, like using a beta prior, binomial likelihood, and obtain beta posterior, m many times. I have not found out a way to vectorize draws from a beta distribution, so I have an explicit for loop

Hi all, Can some one tell me the difference between the following two formulas? 1. epiG.rf <-randomForest(gamma~.,data=data, na.action = na.fail,ntree = 300,xtest = NULL, ytest = NULL,replace = T, proximity =F) 2.epiG.rf <-randomForest(gamma~.,data=data, na.action = na.fail,ntree = 300,xtest = NULL, ytest = NULL,replace = T, proximity =F) [[alternative HTML version deleted]]

Hi all, I have a set of elements (1, 1, 0,1,1,0,1,0,1,1) with ten elements. I have to construct the permutation matrix of this set which is of the size 10 by 2^10. Can some one help how is this possible? Is there is a particular function in R or I need to make function? Best [[alternative HTML version deleted]]

Hi all, I have the following code, When I run the code, it never terminate this is because of the while loop i am using. In general, if you need a loop for which you don't know in advance how many iterations there will be, you can use the `while' statement so here too i don't know the number how many iterations are there. So Can some one suggest me whats going on? I am using the

Hi all, I got following problem in fitting the data. Any kind of suggestions are welcome > beta <- 3.5 > d <- seq(0.1,62.5,0.1) > y <- exp(-beta*d) > y1 <- y > x <- read.table("epidist.txt", header = TRUE) > data.nls <- as.data.frame(cbind(y1,x)) > #attach(data.nls) > nls.fit <- nls(y1~dist,data.nls) Error in cll[[1L]] : object of type