similar to: Sampling Weights in HB Choice Modelling (e.g., rhierMnlRwMixture)

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! 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

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, 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

Dear Group I was referring to a conjoint analysis scenario using R from the paper referred below: Agricultural Information Research 17(2),2008,86-94 available online at www.jstage.jst.go.jp/ This paper describes the data modelling of a conjoint study design based on conditional logit procedure. I understand that Heirarchical Bayes is asymptotically equivalent to Conditionallogit. However it

I have several demographic variables with which I want to explain the ordered choice of individuals within a survey in an ordered choice (probit or logit, this is not important) framework. Standard ordered choice estimations of course just give me aggregate/average parameter estimates. For my task it would however be useful to estimate or extract "hypothetical" individual-level parameter

Hello, I'm estimating an ordered logit model on a probability weighted survey sample. polr permits case weights with the "weights" option, but I cannot figure out from existing documentation what it actually does with these weights. I'm concerned about this because I get somewhat different results using Stata's ologit command with the pweights option and very

Hi all, Does anyone knows how to handle ordered preferences applying the R package mlogit (multinomial logit model)? My data set provides for each customer preferences (given as percentages) for 6 different brands. I would like to use for model calibration not just that brand with maximum stated preference. I know that ordered preferences can be used in the R package MNP (multinomial Probit

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

Dear R users, I would like to perform Latent Class Logit Models for the analysis of choice experiments in environmental valuation. This kind of analysis is usually performed with NLogit Software (http://www.limdep.com). I attach the results I usually obtain using NLogit and NLogit model specifications. For Random parameter models and Logit Models I usually perform my analysis with the package

Hi! The Netfilter project proudly presents: nftables 0.4 This release contains a lot of bug fixes and new features contained up to the recent 3.18 kernel release (and some features coming up in the yet unreleased 3.19-rc). New features ============ * Add support for global ruleset operations (available since 3.18). Get rid of all tables, chains, and rules in one go: # nft

Hi I am trying to estimate stated preferences for Tv sets by using mlogit. The choice set is designed as with 4 attributes: [ brand(has 4 levels), technology(has two levels), class[has 4 levels), price(has 4 levels)] There are 16 choice sets with 4 alternatives each one (i.e. each respondent has evaluated 64 cards in total). Therefore, there are 64 rows in the table below. The data in csv file

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

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: I'm trying to estimate a latent variable model in mnl discrete choice framework using R. I need to do first a uni dimensional integral within each observation (row) in the database and then sum over observations. I'm stacked in the point shown below. Apparently I have a dimensionality problem in the definition of the integral. Maybe it does not identify that what I need is only one

Hello, I can't figure out why using and not using weights in mlogit yields identical results. My motivation is for the case when an "observation" or "individual" represents a number of individuals. For example, library(mlogit) library(AER) data("TravelMode", package = "AER") TM <- mlogit.data(TravelMode, choice = "choice", shape =

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