similar to: MCMC logit

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){

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 =

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)

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 =

Dears, I have the below code for metropolis of the GLM logit (logistic regression) using a flat prior. Can someone help me modify the prior so that the model becomes hierarchical by using a flat prior for mu and sigma, the derived density for beta ~ N(mu, sigma^2)? Actually I took my code from a teacher that posted on the internet and modified it to the GLM logit but I can't adapt it to the

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

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.

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 +

Hi All: Not sure why my previous question never got posted. Here I am seeking some help on my code. I am using the following code to run MCMC simulation on the following data using the model below: # Data matrix<-NULL > csvs<-paste("MVN", 1:2,".csv",sep="") > for (i in 1:length(csvs)){ + matrix[[i]]<- read.csv(file=csvs[i],header=TRUE) +

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)

Sorry to bother your. I am trying to post my question for more than 10 times, but I still didn't see it. It drives my crazy!!! It is a test for posting some simple pure text. Chao

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

Hi, I want to know is there other functions in R to estimate qualitative response model besides multinom() in library nnet, if this is the only possibility, I have a question about the application: for example: there is three transportation choice : car, bus , subway. each alternative has own characteristic variables, I want to apply conditional logit model to analysis the choice of three

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

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Hi Good People, I've created a blog to help newbies in the world of Linux. Can you people see it and tell what departments that I've to improve more to help the grate community of Linux. Thank you -- Sadaruwan Samaraweera -------------- next part -------------- An HTML attachment was scrubbed... URL:

Full_Name: Ximing Zhao Version: R 1.7.1 OS: MacOs X Submission from: (NULL) (64.136.27.51) When I used function plot in R 1.7.1, I found two bugs. My whole function is this: ***************** drawexp<-function(X,x,c,b){ exp<-function28(X,x,c,b) if(max(exp)>=max(dexp(x,rate=1))){ plot(x,exp, type="l",main= paste("Exponential ,n=",length(X),"

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 all, I wrote the following lines of codes try to do some iterations to find the global optimal values, but the function does not execute properly. Every time codes stop after one iteration right after executing the optim() function. Does anyone could have me to take a look? Thanks. if (count>0){ k=k+0.05; mu0=c(83+k,0,0) Sigma0= diag(0.4,3) initpar=c(.1+10*k,10*k,10*k,10*k) # initial