similar to: Parameter estimates for each observation (ordered choice)

Hi John (and other authors of ChoiceModelR package), I am experiencing a weird thing when using the function choicemodelr under R3.0.1. Before I updated to R3.0, I had used choicemodelr unde R2.15. It was always as fast or faster than exactly the same task in Sawtooth software. Now, I've tried to run it under R3.0.1. It seems to be doing the job. But something is slowing it down dramatically.

Hello, I searched everywhere but I didn't find what I want, that is why I as the question here. Threads discussing this issue on this mailing list are already quite old. Does anybody know of a function in R which allows to estimate ordered probit/logit model with random coefficients. The only mixed effect model I found was clmm of the ordinal package but it only provides random intercepts. I

> I make a study in health econometrics and have a categorical > dependent variable (take value 1-5). I would like to fit an ordered > probit or ordered logit but i didn't find a command or package who > make that. Does anyone know if it's exists ? R is very fancy. You won't get mundane things like ordered probit off the shelf. (I will be very happy if someone will show

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

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

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

Hello R Programmers, We have a large existing Perl codebase which is currently being upgraded to use the new optimizing RPerl compiler (not related to the R language). http://rperl.org/ Also in use by the same Perl codebase is some custom R code which loads the "bayesm" library in order to execute Markov Chain Monte Carlo (MCMC) calculations.

Hi: Is there a R-function that can generate samples from the posterior distribution of an ordered logistic regression model (just like MCMCoprobit from MCMCpack in R). I will greatly appreciate some guidance in this regard. Thanks Reez

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

Is anyone familiar with a way to account for sampling weights (e.g., in order to cope with selection bias) for individual respondents using the bayesm package (e.g., rhierMnlRwMixture)? In the regular MNL this can easily be done in STATA using the mlogit function with pweights option. However, I am unfamiliar with a way to do it in HB estimation. Any help or hints are appreciated. Best, Klaus

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-Help Group: R will not start on my imac running 10.6.2. I installed both R2.10.1 and 2.10.0 from pkg. It crashed on both. I tried both the R and R64 apps and both versions 2.10.1 and 2.10.0. I googled and found nothing on this. Below is part of report: Process: R [767] Path: /Applications/R64.app/Contents/MacOS/R Identifier: org.R-project.R Version: R

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

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