similar to: sample exponential r.v. by MCMC

Hi, I'm having issues with what I believe is a bug in the MCMCpack's MCMCmetrop1R function. I have code that basically looks like this: posterior.sampler <- function(data, prior.mu){ log.posterior <- function(theta) log.likelihood(data, theta) + log.prior(prior.mu, theta) post.samples <- MCMCmetrop1R(log.posterior, theta.init=prior.mu, burnin=100, mcmc=1000, thin=40,

Hi, I have a dataset with the binary outcome Y(0,1) and 4 covariates (X1,X@,X#,X$). I am trying to use MCMClogit to model logistic regression using MCMC. I am getting an error where it doesnt identify the covariates ,although its reading in correctly. The dataset is a sample of actual dataset. Below is my code: > ####################### > > > #retreive data > # considering four

Dear R-users (and developers), I am looking for an efficient framework to carry out parameter estimations based on MCMC (optionally with specified priors). My goal is as follow: * take ANY R-function returning a likelihood-value (this function may itself call external programmes or other code!) * run a sampler that covers the multidimensional parameter space (thus creating a posterior

Hi everyone: I am just wondering how can I use weights with MCMClogit function (in MCMCpack package). For example, in case of glm function as given below, there is weights option in the arguments. Aparently there is no option of using weights in MCMClogit. glm(formula, family = gaussian, data, weights, subset, na.action, start = NULL, etastart, mustart, offset, control =

Hello, I tryed to use the MCMC pack, particularly the function MCMCirtKd to simulate the posterior distribution in a multidimensional IRT model. The code I used is: posterior1 <- MCMCirtKd(Y, dimensions=2, item.constraints=list("V2"=list(3,0)), burnin = 1000, mcmc = 10000, thin=1, verbose = 1, seed = NA, alphabeta.start = NA, b0 = 0, B0=0, store.item = FALSE,

I'm having some problems in installing some source packages under linux. As an example, MCMCpack. An error is raised when linking: > install.packages("MCMCpack") [...] * Installing *source* package 'MCMCpack' ... checking for C++ compiler default output file name... a.out checking whether the C++ compiler works... yes checking whether we are cross compiling... no checking

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 am receiving the above error ( full r session output below) the script runs OK in windows. and "genotypes" and "ploidy" are both correct arguments any suggestions would be most welcome Nevil Amos MERG/ACB Monash University School of Biological Sciences > library(Geneland) Loading required package: RandomFields Loading required package: fields Loading required

Hi all, I am attempting to fit, and test the goodness of fit of, a mixed exponential distribution to my dataset which consists of 15minute rainfall intensity data. FYI, the dataset spanning approx.2 years and 7 rainfall stations consists of some three hundred thousand 15min data records, of which some 30 thousand are non-zero rainfall amounts. Could anyone please tell me how i could do

Dear R-users, I have to plot a exponential distribution like the plot in the pdf attached. I've write this code but I don't know how to draw the two lines.. Can anyone help me please? Thank you very much Pippo http://r.789695.n4.nabble.com/file/n3394476/exponential_smoothing.pdf exponential_smoothing.pdf -- View this message in context:

Dear R Users, I apology for not being able to provide an adequately informative subject. Let me describe my problem with an example. For a matrix * (mat <- matrix(c(1,1,2,2,2,3,3, 5,9,1,3,7,4,8), ncol = 2))* my desired output is * (desired <- matrix(c(1,2,3, 5,1,4), ncol = 2))* That is, the first column is numerically grouped and only the first item in each group is wanted. The

Dear R users: I am drawing a graph with the following code: Tau<-seq(0.05,0.95,0.05); Pi <- seq(0.19,0.01,-0.01); par(cex.axis=0.8,ps=9,mar=c(1.5,1,0.5,1), oma=c(1,1,0.2,1) ,tck=-0.01); plot(Tau,Pi, type='l', xlab="Tau",ylab="Pi",col=4); I want to make the graph take as little space as possible. Here I run into two problems. One is that the labels are gone, the

Can you write an R function to generate from N samples from the given Gibbs algorithm. Also we must repeat the study for different N values and different L values .And plot iterations vs F1 and iteration vs F2. F1, F2 ~N2 (0, ( 1 L ) ) 1 L --------------------------------- Send a seasonal email greeting and help others. Do good. [[alternative

Dear useRs, Can someone help me to adjust the color of math annotation in a legend? The following code gives me a black "alpha = 2". x=y=1:100 z=seq(0.5,50,by=0.5) plot(x,y,type='l',col='black') lines(x,z,col='red') legend('topleft',c(expression(paste(alpha," = ", 1)), expression(paste(alpha," = ",

Is there any way that I can see the step by step code for functions in the base package? For instance the dexp function. I am a student working on writing my own function for something that is similar to this dexp function and I would like to see the step by step code. Brittany Laine GTA WVU Statistics Department 331 Hodges

Does anyone know if R have any problems with the exponential random number generation (function rexp)? I comment it because I executed data<-sort(rexp(100)) plot(data,dexp(data)/(1-pexp(data)),type="l") and the graphic isn't constant. (Note: exponential distribution have a constant hazard failure rate). Thank you, Juan

Hello to R people Does anybody know to calculate exact logistic regression in R? Does such option exist anywhere? Surprisingly, could not find it using search engine. It is hard to believe, however, that such useful function is not implemented in R yet? Could you help, please Thank you Denis

A naive question from a non-statistician: I'm looking into running a Bayesian analysis of a model with high dimensionality. It's not a standard model (the likelihood requires a lot of code to implement), and I'm using a Linux machine. Was wondering if someone has any thoughts on what the advantages of OpenBugs are as opposed to just R (or should I be thinking WinBUGS under Wine?)?

Hi, I am fairly new to R, and am stuck. I want to write an R function with argument n that returns a vector of length n with n simulated observations from the double exponential distribution with density: ??g(y) = 1/2e^-y ? For the double exponential, I want to generate y~Exp(1) and then take ?y with probability 0.5 ? Does anyone know how I can do this in R? Thanks! Fran [[alternative

Dear all, I am trying to use the logistic regression with MCMClogit (package: MCMCpack/Coda) and I want to put a beta prior on the parameters, but it's giving me error message (please see output below) no matter what shape 1 or 2 I use. It works perfect with the cauchy or normal priors. Do you know if there is a catch there somewhere? Thanks logpriorfun <- function(beta,shape1,shape2){