similar to: Function rmultiregfp from package bayesm gone?

Hi everyone, I have a list betaMoments with 2 levels, e.g. > beta1 = list( + m = 4, + v = 5 + ) > beta2 = list( + m = 6, + v = 7 + ) > > betaMoments = list() > betaMoments[[1]] = beta1 > betaMoments[[2]] = beta2 and I have a matrix Names which has the same number of lines as the list has first level elements (here 2; beta1 and beta2). Now I need to make

Hi all, I need to do massive simulations in the next two years. I estimated that I will need about 64GB memory, if I do not want to split up the calculations. Additionally I would like to have it as fast as possible. Can R handle multi-core processors and can all standard operating systems handle the same amount of memory and speed? Perhaps someone could point me to a webshop that sales

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)

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

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 I can install RWinEdt if I start R with administrator rigths, but it does not paste my code to the console. I found advice in the link below how to manage the problem, but it did not work, any other idea? http://yusung.blogspot.com/2009/01/rwinedt-and-windows-vistawindow-7.html Thanks a lot,Johannes >From: Uwe Ligges <ligges_at_statistik.tu-dortmund.de> >Date: Sun, 08 Nov 2009

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 all, For my research I have to use a Multinomial Probit model. I saw that there are two packages, that include a method to estimate my parameters. The first one is the MNP-package of Imai and van Dyk. The second one is part of the bayesm-package of Rossi. The results for both packages are not the same using the same data. Does anybody know what the difference is between these two approaches?

Hello, I have built a multi-variate probit model using the package "bayesm", which requires that the X data is constructed using the function "CreateX". I've gone through the documentation and run my model, but wanted to be sure about my interpretation of the results for the coefficients - beta. Steps: 1) I have 5 choices for the dependent variable Y, so p=5 2) I have 8

Hello, I have built a multi-variate probit model using the package "bayesm", which requires that the X data is constructed using the function "CreateX". I've gone through the documentation and run my model, but wanted to be sure about my interpretation of the results for the coefficients - beta. Steps: 1) I have 5 choices for the dependent variable Y, so p=5 2) I have 8

Hi all, Please let me know the how to include Bayesm with R-2.4.1 Thanks Jomy [[alternative HTML version deleted]]

Hello Everyone Both the MCMCpack and the bayesm libraries allow us to make draws from the Inverse Wishart distribution. But I wanted to find out how exactly is the Inverse Wishart distribution parameterized in these libraries. The reason I ask is the following: Now its generally standard to express Inverse Wishart as IW(0.5 * DOF,0.5* Scale). (DOF-> Degree of freedom, Scale -> Scale

Version 2.0-2 of bayesm is available on CRAN. This version includes bug fixes for rhierMnlRwMixture and rhierLinearModel. peter rossi _______________________________________________ R-packages mailing list R-packages at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-packages

Version 2.0-2 of bayesm is available on CRAN. This version includes bug fixes for rhierMnlRwMixture and rhierLinearModel. peter rossi _______________________________________________ R-packages mailing list R-packages at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-packages

Hello! I am using rhierMnlRwMixture from bayesm package. I would like to use it with a categorical covariate (Z). I have 2 clariciation questions: 1. If the covariate is categorical, do I have to represent it as dummy variable(s)? (e.g., 2 dummy variables for a 3-level categorical variable)? 2. Do those dummy variables have to be centered? Help file for rhierMnlRwMixture says: "Z should not

Folks- a patch release of bayesm, v0.0-1, is now available on CRAN. This release corrects some errors in the help pages as well as one error in the function rhierLinearModel involving an incorrect default prior setting. peter ................................ Peter E. Rossi Joseph T. and Bernice S. Lewis Professor of Marketing and Statistics Editor, Quantitative Marketing and Economics Rm

Version 1.0-1 of bayesm is now available on CRAN. This is our first "production" version which include s much improved documentation as well as five data sets used in our book, Bayesian Statistics and Marketing. peter r ................................ Peter E. Rossi Joseph T. and Bernice S. Lewis Professor of Marketing and Statistics Editor, Quantitative Marketing and Economics

Version 1.1-0 of bayesm is now available on CRAN. This version includes Bayesian inference for the NBD(Poisson) regression model and a hierarchical version of the same. It also includes an additional dataset with count data and various covariates. Comments and suggestions for improvement are most welcome. ................................ Peter E. Rossi Joseph T. and Bernice S. Lewis

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