similar to: Multi-variate Probit model using Bayesm in R

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

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 any, Can some please detail me the createX command in bayesm package? To make things easy for you to help me, let me put forward my problem Suppose I have 3 covariates (say X matrix) and my Y has 3 categories say (1,2,3). Now from the CreateX I understand that the data matrix say 'Xa' must be of dimension n* (naxp), where 'na' is the number of variables and 'p' is

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

Hello, I have a small problem with developing design matrix X, which I use in estimation the log-likelihood of a multinomial logit model. I have the data: number of observation - 289 number of choice alternative- 3 number of choice specific variables in matrix X -4 matrix X =289x4 I tried to use the function createX, I know that I have to get design matrix 289x12 (am I right?) but

Background: I administer a cluster of RedHat EWS 3U4 Linux workstations at a university. I built R 2.1.0 from source: ./configure \ --prefix=/sscc/opt/R-2.1.0 \ --with-blas=no \ 2>&1 \ | tee NUInstall.configure R is now configured for i686-pc-linux-gnu Source directory: . Installation directory: /sscc/opt/R-2.1.0 C compiler:

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

Hello, I have a small problem with developing design matrix X, which I use in estimation the log-likelihood of a multinomial logit model. I have the data: number of observation - 289 number of choice alternative- 3 number of choice specific variables in matrix X -4 matrix X =289x4 I tried to use the function createX, I know that I have to get design matrix 289x12 (am I right?) but

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

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

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

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

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 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