similar to: MCMClogit: using weights

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 everybody, I'm new to MCMClogit. I'm trying to use MCMClogit to fit a logistic regression model but I got some warnings I can't understand. My input data X is 32(tissue sample)*20(genes) matrix, each element in this matrix corresponds to the expression value of one particular gene in one of 32 samples. And the Y presents the corresponding classes (0-non cancer, 1-cancer)

Dear all, I am trying to use the logistic regression with MCMClogit (package: MCMCpack/Coda) and I want to put a beta prior on the parameters, but it's giving me error message (please see output below) no matter what shape 1 or 2 I use. It works perfect with the cauchy or normal priors. Do you know if there is a catch there somewhere? Thanks logpriorfun <- function(beta,shape1,shape2){

Deal all, as MCMClogit does not allow for the specification of several chains, I have run my model 3 times with different random number seeds and differently dispersed multivariate normal priors. For example: res1 = MCMClogit(y~x,b0=0,B0=0.001,data=mydat, burnin=500, mcmc=5500, seed=1234, thin=5) res2 = MCMClogit(y~x,b0=1,B0=0.01,data=mydat, burnin=500, mcmc=5500, seed=5678, thin=5) res3 =

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

I hope some of the authors of the package MCMCpack read this. I don't know if there is a way to set the response in the model formula of MCMClogit other than a (numeric) response vector. I think the MCMClogit in the MCMCpack needs some development so that the response in the formula could be set as a two-column matrix with the columns giving the numbers of successes and failures (just as in

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, 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

Is there a way to recover Metropolis-step acceptance rates AFTER completing posterior draws? The immediate application is in the probit.bayes and logit.bayes models used by Zelig... which I believe is merely calling MCMCpack. So one strategy, to which I am fixing to resort, is to call, say, MCMClogit with verbose set to mcmc (or mcmc divided by an integer) and then look at my screen.

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

Hello! I have the following problem. My code: --snip-- ergebnisse <- rep(0, each=2) stichproben <- rep(0, each=2) for (i in seq(1:2)) { n <- dim(daten)[1] ix <- sample(n,200) # producing samples samp_i <- daten[ix,] stichproben[i] <- samp_i # doesn???t works # Calculation of the model: posterior_i <- MCMClogit(y ~ fbl.ind + fekq3 + febitda4 + fuvs + fkru +

Hello! So, the simulation works (drawing 100 samples and then calculate the model for each sample). Here is the code: --snip-- # sample size n=200 ergebnisse200 <- rep(0, each=100) stichproben200 <- vector(?list?, 100) default200 <- rep(0, each=100) for (i in seq(1:100)) { n <- dim(daten)[1] ix <- sample(n,200) samp_i <- daten[ix,] # draw samples y <- sum(samp_i$y)

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 to R people Does anybody know to calculate exact logistic regression in R? Does such option exist anywhere? Surprisingly, could not find it using search engine. It is hard to believe, however, that such useful function is not implemented in R yet? Could you help, please Thank you Denis

Hello, Suppose one is interested in fitting a GLM with a log link to binomial data. How does R choose starting values for the estimation procedure? Assuming I don't supply them. Thanks, Tyler

Hi, Currently the `gaussian' family's initialization code signals an error if any response data are zero or negative and a log link is used. Given that zero or negative response data are perfectly legitimate under the GLM fitted using `gaussian("log")', this seems a bit unsatisfactory. Might it be worth changing it? The current offending code from `gaussian' is:

Hello R Users, I am having difficulty deleting the last warning message in a loop so that the only warning that is produced is that from the most recent line of code. I have tried options(warn=1), rm(last.warning), and resetting the last.warning using something like: > warning("Resetting warning message") This problem has been addressed in a previous listserve string,

I encountered a segfault running glm() and wonder if it could have something to do with the way memory is handled in a dual core system (which I just set up). I'm running R-base-2.4.0-1, installed from the SuSE 10.1 x86_64 rpm (obtained from CRAN). (My processor is an AMD Athlon 64 x2 4800+). The error and traceback are *** caught segfault *** address 0x8001326f2b, cause 'memory not

G'day all, I had a look at the GLM code of R (1.4.1) and I believe that there are problems with the function "glm.fit" that may bite in rare circumstances. Note, I have no data set with which I ran into trouble. This report is solely based on having a look at the code. Below I append a listing of the glm.fit function as produced by my system. I have added line numbers so that I

Bug summary: glm() causes a segfault if the argument 'data' is a data frame with more than 16384 rows. Bug demonstration: -------input --------------- N <- 16400 df <- data.frame(x=runif(N, min=1,max=2),y=rpois(N, 2)) glm(y ~ x, family=poisson, data=df) ------ output --------------- *** caught segfault *** address (nil),