similar to: MCMC for sampling from ordinal logistic regression

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

Hello, thank you very much for your previous answers about the C++ code. I am interested in the application of the Gibbs Sampler in the IRT models, so in the function MCMCirt1d and MCMCirtkd. I've found the C++ source codes, as you suggested, but I cannot find anything about the Gibbs Sampler. All the files are for the Metropolis algorithm. Maybe I am not able to read them very well, by the

Hi everyone, I am running an ordinal probit using the Bayesian MCMCpack and I am getting an error saying "attempt for find suitable starting values failed" Here is my code: > posterior <- MCMCoprobit(y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 +x9 + x10 + x11 + x12 +x13 , beta.start=c(-10, 0.05, 0.02, 0.04, 0.98, 0.61, -0.29, 0.91, -0.82, 1.34, 0.68, 0.57, 0.09, 0.5), mcmc=10000)

> 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 everyone: I am just wondering how can I use weights with MCMClogit function (in MCMCpack package). For example, in case of glm function as given below, there is weights option in the arguments. Aparently there is no option of using weights in MCMClogit. glm(formula, family = gaussian, data, weights, subset, na.action, start = NULL, etastart, mustart, offset, control =

Hi, I have a dataset with the binary outcome Y(0,1) and 4 covariates (X1,X@,X#,X$). I am trying to use MCMClogit to model logistic regression using MCMC. I am getting an error where it doesnt identify the covariates ,although its reading in correctly. The dataset is a sample of actual dataset. Below is my code: > ####################### > > > #retreive data > # considering four

Hello, just to follow up a question from last week. Here what I've done so far (here an example): library(MCMCpack) Y=c(15,14,23,18,19,9,19,13) X1=c(0.2,0.6,0.45,0.27,0.6,0.14,0.1,0.52) X2a=c(17,22,21,18,19,25,8,19) X2b=c(22,22,29,34,19,26,17,22) X2 <- function()runif(length(X2a), X2a, X2b) model1 <- MCMCregress(Y~X1+X2()) summary(model1) but I am not sure if my X2-function is

Dear R-users (and developers), I am looking for an efficient framework to carry out parameter estimations based on MCMC (optionally with specified priors). My goal is as follow: * take ANY R-function returning a likelihood-value (this function may itself call external programmes or other code!) * run a sampler that covers the multidimensional parameter space (thus creating a posterior

Dear R-helpers, Hi! All. I'm doing a project which needs MCMC simulation. I wonder whether there exists related packages in R. The only one I know is a MCMCpack package. What I want to do is implementing gibbs sampling and Metropolis-Hastings Algorithm to get the posterior of hierarchical bayesian models. Thanks in advance. Jun

I installed R-2.3.1 and ran make check without problem, but I am having trouble installing several packages using gcc (GCC) 3.2.3 20030502 (Red Hat Linux 3.2.3-53) on Red Hat Enterprise Linux AS release 3 (Taroon Update 7) Kernel 2.4.21-40.ELsmp on an x86_64 Below are the messages from MCMCpack. Perhaps my systems are messed up? Paul Gilbert > install.packages("MCMCpack")

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 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

Dear All I try to use your R package MCMCpack and I have encountered the following problem: The following code works fine: library(MCMCpack) line <- list(X = c(-2,-1,0,1,2), Y = c(1,3,3,3,5)) posterior1 <- MCMCregress(X~Y, data=line) summary(posterior1) But as long as I try the following lines library(MCMCpack) line <- list(X = c(-2,-1,0,1,2), Y = c(1,3,3,3,5))

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

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.

Dear R users, I am leaning MCMC sampling, and have a problem while trying to sample exponential r.v.'s via the following code: samp <- MCMCmetrop1R(dexp, theta.init=1, rate=2, mcmc=5000, burnin=500, thin=10, verbose=500, logfun=FALSE) I tried other distribtions such as Normal, Gamma with shape>1, it works perfectly fine. Can someon

Compilation of MCMCpack under freebsd 6.2 i386 fails because of the following cryptic error: * 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 for suffix of executables... checking for suffix of object files... o checking whether we

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

Dear All, What is the preferred way to get Bayesian analysis results (such as those from MCMCpacki, MCMCglmm, and DPpackage) into LaTeX table automatically? I have been using the "apsrtable" package and similar functions in "memisc" package, but neither seems to handle MCMC output directly. Many thanks. Shige

R users- In moving from R 2.0.0 to R 2.1.0 in Windows, I have encountered a problem with the "riwish" command in the package "MCMCpack". I've searched the documentation and can't seem to figure it out. For example: Define a matrix: > lam <- matrix(c(.00233,-.00057,-.00057,.00190),2,2) and then use the riwish command to generate a random inverse-Wishart