similar to: Bayesian Stats Job, MCMC Code Porting

We are pleased to announce the release of version 0.0 of bayesm on CRAN. bayesm covers many important models used in marketing and micro-econometrics applications. The package includes: Bayes Regression (univariate or multivariate dep var) Multinomial Logit Multinomial and Multivariate Probit Multivariate Mixtures of Normals Hierarchical Linear Models with a normal prior and covariates

We are pleased to announce the release of version 0.0 of bayesm on CRAN. bayesm covers many important models used in marketing and micro-econometrics applications. The package includes: Bayes Regression (univariate or multivariate dep var) Multinomial Logit Multinomial and Multivariate Probit Multivariate Mixtures of Normals Hierarchical Linear Models with a normal prior and covariates

Hi all, I have managed to get the r-scale usage algorithm to work, but I need to obtain the final results from this. As I understand it, this code is designed to generate a matrix after processing and store it somewhere????? Here is the code..... I get this part of the code, it all makes sense. ## if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=1000} else {R=5} { data(customerSat)

Hi R-users, I have the following problem: I am trying to learn something about bayes methodology and started paying around bayesm package, but could not replicate the Conjunctive model's estimates as they appear in Rossi et al "Bayesian Statistics and Marketing", 2005, JWS, pages 264-265, Table CS4.4. I have downloaded in my working directory the documents from

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 all, I would like to start to use R's MCMC abilities to compute answers in Bayesian statistics. I don't have any specific problems in mind yet, but I would like to be able to compute/sample posterior probabilities for low-dimensional custom models, as well as handle "standard" Bayesian cases like linear regression and hierarchical models. R clearly has a lot of abilities in

Dear All, I am using rhierMnlRwMixture in the bayesm package for the analysis of data from a choice experiment. I am trying to follow the margarine example set out in the bayesm manual (p.28). However, after several attempts I keep getting an error message with regards to my Z matrix as below. > Error in Z %*% t(matrix(olddelta, ncol = nz)) : >requires numeric/complex matrix/vector

Hi, I tried to use rmnlIndepMetrop (bayesm package) for my MNL model with 4 choice alternatives, 5 independent variables, 69 observations, dim(X) [1] 276 5, nu=6. So I run such code: if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=2000} else {R=10} set.seed(66) df=read.table("X_metrop.dat",header=TRUE) inp=as.matrix(df) y=as.numeric(inp[,1]) n=length(y) p=4

Folks- We are pleased to announce the release of version 2.0-1 of bayesm. Highlights of the new version: 1. Bayesian treatment of SUR (seemingly unrelated regression) 2. Added clustering to mixture of normals models 3. Added routines to compute implied univ and bivariate densities from mixture of normals MCMC draws 4. improved input error checking in many routines please get rid of your

Release 2.1-1 is now available on CRAN. This release includes-- bayesm classes (some compatible with the mcmc class of coda) for output. plot and summary methods for these classes. additional datasets including store-level panel data. peter r ................................ Peter E. Rossi Joseph T. and Bernice S. Lewis Professor of Marketing and Statistics Editor, Quantitative Marketing

Folks- We are pleased to announce the release of version 2.0-1 of bayesm. Highlights of the new version: 1. Bayesian treatment of SUR (seemingly unrelated regression) 2. Added clustering to mixture of normals models 3. Added routines to compute implied univ and bivariate densities from mixture of normals MCMC draws 4. improved input error checking in many routines please get rid of your

I am a pretty new R user, I am running the latest linux version on xandros, updated with some extra debian packages, and I also run the latest windows version, but prefer linux. I am having trouble downloading "bayesm", it won't do it all from any of the sites on the web, I resorted to this one, http://packages.debian.org/unstable/math/r-cran-bayesm. and got slightly further, but I

Hi Friends, I'm trying to model the consumer decisions (Click-Through Rate and Conversion) in Search Engine Advertising using a hierarchical Bayesian binary logit. The input data is the weekly CTRs and Avg. Position for each search keyword. CTR is modeled as (for each keyword i and week j): Pij = exp(C + Bi x Positionij + A1 x Lengthi + A2 x Brandi + A3 x ProductSpecifici) / [1 + exp(C +

Hi, I have been using the function rmultiregfp from the bayesm package for a long time. In the current version of the package this function (and the corresponding init.rmultiregfp) is no longer available, does anyone know why? Can someone who has the "old" bayesm still on his computer please post the code of these two functions? Would be a great help as I have them both in a number of

I have an mcmc object and I''m trying to plot the quantiles of the variables - and not as a function of the iterations as in cumuplot. I cannot seem to find the right combination of indexing to access the variables; after which I''m sure I can plot all the statistics I could hope for. Any hints for accessing the mcmc object would be appreciated. =Dave [[alternative HTML

Dear List members, I am using R to generate MCMC time series plots. This is the code I use; however, everytime an error message will come out saying could not find function "plot.mcmc." I have coda package and Lattice package installed. Does anyone know what may get wrong here? Thanks so much for your suggestions. Hongli Li *library (coda) dat1=read.csv("Itemtime.csv")

I have been running a Gibbs Sampler to estimate levels of efficiency in the Louisiana Shrimp Industry. I created a matrix (samp) where I stored the results of each iteration for 86 variables. I run 10,000 iterations. So, the matrix samp is 10,000 x 86. I want to use the gelman-rubin test to check for convergence. To do that, I need at least two chains. If I run second chain with different starting

I am trying to use the function 'filter.NA=TRUE' in Geneland. The function appears to be set on TRUE by default, as it appears as TRUE in the 'parameter.txt' file output and hence I do not need to enter the function per se (as it is an 'Unused argument otherwise') . Hence all my missing data (individuals that I have not yet scored at that specific loci) are scored as

Dear R users, I am leaning MCMC sampling, and have a problem while trying to sample exponential r.v.'s via the following code: samp <- MCMCmetrop1R(dexp, theta.init=1, rate=2, mcmc=5000, burnin=500, thin=10, verbose=500, logfun=FALSE) I tried other distribtions such as Normal, Gamma with shape>1, it works perfectly fine. Can someon

Dear officer I have a question concerning running R when I am doing my research. Can you help me to figure that out? I am now running a MCMC iteration in the R program. But it is always stucked in some loop. This cause big problems for my research. So I want to know whether we can skip the current dataset and move to next simulated data when the iteration is stucked? Alternatively, can the MCMC