search for: mcmclogit

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 prese...

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 <-...

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, of...

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=...

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 glm) or at least to be able to set the weights also...

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 covariates &gt...

...SHOW Me the coefficients, but I can't run a "predict" with them. Can someone suggest how to pull them out and format them so that I can run some predictions to test the quality. I thought something like "as.model" would be there, but isn't. For example. foo <- MCMClogit(label ~ x1 + x2) summary(foo) plot(foo) foo has all the info. How do I pull out the coefficients and format them into a "standard" logit model?? Thanks! -N

...s 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. But it seems odd that this datum is not being put into output objects, so I wonder if I'm missing something. Plus sometimes I can save the output object, but then the power goes out and what's on sc...

...y 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 + fzd + fur3, data=samp_i, b0=prior, B0=precision, tune=0.5) # calculation ergebnisse[i] <- summary(posterior_i) # saving the results (works) } --snip-- I have a data set called "daten". I produce samples of the size 200. The samples ar...

...q(1:100)) { n <- dim(daten)[1] ix <- sample(n,200) samp_i <- daten[ix,] # draw samples y <- sum(samp_i$y) # number of defaults stichproben200[[i]] <- samp_i # saving the samples default200[i] <- y # saving the number of defaults # Modell berechnen: posterior_i <- MCMClogit(y ~ fbl.ind + fekq3 + febitda4 + fuvs + fkru + fzd + fur3, data=samp_i, b0=prior, B0=precision, tune=0.5) # calculation ergebnisse200[i] <- summary(posterior_i) # saving the results } # write out the solutions into an excel-file write.csv2(ergebnisse200, "ergebnisse.csv") --snip-- M...

...** libs g++ -I/usr/lib/R/include -DSCYTHE_COMPILE_DIRECT -DSCYTHE_NO_RANGE -c distributions.cc -o distributions.o [...etc. etc. All compilations are ok] g++ -o MCMCpack.so distributions.o ide.o la.o lecuyer.o MCMCdistn.o MCMCdynamicEI.o MCMCfactanal.o MCMCfcds.o MCMChierEI.o MCMCirt1d.o MCMClogit.o MCMCmetrop1R.o MCMCmixfactanal.o MCMCmnlMH.o MCMCmnlslice.o MCMCoprobit.o MCMCordfactanal.o MCMCpanel.o MCMCpoisson.o MCMCprobit.o MCMCprobitres.o MCMCregress.o MCMCrng.o MCMCtobit.o mersenne.o optimize.o rng.o smath.o stat.o -L/usr/lib/R/lib -lR /usr/lib/gcc-lib/i486-linux/3.3.5/../../../crt1....

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

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