similar to: MCMCpack Ordinal Probit Help

> 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: 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

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

A student brought this question to me and I can't find any articles or examples that are directly on point. Suppose there are 2 ordinal logistic regression models, and one wants to set them into a simultaneous equation framework. Y1 might be a 4 category scale about how much the respondent likes the American Flag and Y2 might be how much the respondent likes the Republican Party in America.

I'm having some problems in installing some source packages under linux. As an example, MCMCpack. An error is raised when linking: > install.packages("MCMCpack") [...] * Installing *source* package 'MCMCpack' ... checking for C++ compiler default output file name... a.out checking whether the C++ compiler works... yes checking whether we are cross compiling... no checking

Dear all, I?ll need your help with obtaining robust clustered errors. I use polr command in MASS package m<?porl(y~x1+x2,data=mydata, method=probit). In the rms package, this is as simple as: clusterSE<?robcov(m, mydata$id). Is it possible to do something similar for polr object as well? Thank you very much Best, Faradj [[alternative HTML version deleted]]

Hello, all. I'm investigating the rate at which skeletal joint surfaces pass through a series of ordered stages (changes in morphology). Current statistical methods in this type of research use various logit or probit regression techniques (e.g., proportional odds logit/probit, forward/backward continuation ratio, or restricted/unrestricted cumulative probit). Data typically include the

What is the current state of the R-art for ordered probit models, and more esoterically is there any available R strategy for ordered cauchit models, i.e. ordered multinomial alternatives with a cauchy link function. MCMC is an option, obviously, but for a univariate latent variable model this seems to be overkill... standard mle methods should be preferable. (??) Googling reveals that spss

I'm completely new to R and am trying to install an add-on package for one of our faculty members. I've had no problems with most of them, but am completely stuck trying to figure out why MCMCpack won't install.. any help is VERY much appreciated!!! here's what I get when I try to install the package: * Installing *source* package 'MCMCpack' ... checking for C++ compiler

Hello everyone I am trying to fit an ordered probit/logit model for bank rating prediction. Besides polr() in MASS package which is not written especially for this as far as I know, do you know how else I can do this? I already found the modified polr () version on the Valentin STANESCU Enrst and Young Tel. 402 4000 ---------------------------------------------------------- The information

Dear R users, Thanks in advance. I am Deb, Statistician at NSW Department of Commerce, Sydney. I am using R 2.9.1 on Windows XP. This has reference to the package “MCMCpack”. My objective is to select a better model using various alternatives. I have provided here an example code from MCMCpack.pdf. The matrix of Bayes Factors is: model1 model2 model3 model1 1.000 14.08

Hi list, I tried to install MCMCpack to R-2.12.0, got the following error, CC -m64 -library=stlport4 -I/apps/sparcv9/R-2.12.0/lib/R/include -DSCYTHE_COMPILE_DIRECT -DSCYTHE_DEBUG=0 -DHAVE_TRUNC -DHAVE_IEEEFP_H -I/opt/csw/include -KPIC -g -c MCMCSVDreg.cc -o MCMCSVDreg.o "error.h", line 598: Error: The function "abort" must have a prototype.

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

Hi, I've been having problems getting MCMCpack installed on my Solaris 10 x86 system. I've installed gcc from opencsw.org, and, for the most part, other R packages install nicely. >From what I can gather, the build fails due to a mismatch of include files. I *think* it wants to use the include files that are contained within the MCMCpack package (but I could be wrong on this - if this

Hi, I'm having issues with what I believe is a bug in the MCMCpack's MCMCmetrop1R function. I have code that basically looks like this: posterior.sampler <- function(data, prior.mu){ log.posterior <- function(theta) log.likelihood(data, theta) + log.prior(prior.mu, theta) post.samples <- MCMCmetrop1R(log.posterior, theta.init=prior.mu, burnin=100, mcmc=1000, thin=40,

Hi R users, I am using the package MCMCpack and I would compute the Deviance Information Criterion for selecting models, instead of using bayes factors. I know that a similar question have been sent previously to the list by others, but I have found no response. Can somebody help me? best Giacomo

Hi there, I am running MCMCpack (MCMCirt1d model) on some files (26 items x about 800 – 1200 individuals). I have a problem. When I am working on “big” files, the R program crashes. More precisely I got the following Microsoft Warning: ******************************************* Microsoft Visual C++ Runtime Library Runtime Error! Program: C:\Program Files\R\R-2.4.1\bin\Rgui.exe

Dear all, I have two questions that are almost completely related to how to do things in R. I am running an ordinal probit regression analysis in R. The dependent variable has three levels (0=no action; 1=warning; 2=sanction). I use the lrm command in the rms package: print( res1<- lrm(Y ~ x1+x2+x3+x4+x5+x6, y=TRUE, x=TRUE, data=mydata)) I simply couldn't make any sense of the

Hi, I've been testing some models with the MCMCpack library. I can run the process and get a nice model "object". I can easily see the summary and even plot it. I can't seem to figure out how to: 1) Access the final coefficients in the model 2) Turn the coefficients into a model so I can then run predictions using them. A summary command will SHOW Me the coefficients, but

Hello, I'm working on the multiple categorical data (5-points scale) using linear mixed effect model and wondering if anyone knows about or works on the linear mixed effect model with ordered logit or probit link. I found that the "lmer" function in R is very flexible and supports various models, but not ordered logit/probit models. I may conduct my analysis by turning my DVs