similar to: Bayesian logistic regression for 85.000 variables

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, I need assitance with performing a Bayesian Ordered Logistic Regression in R. Would you be able to assist? Aruna Sent from my BlackBerry? wireless device available from bmobile.

Hi all. Are there any R functions around that do quick logistic regression with a Gaussian prior distribution on the coefficients? I just want posterior mode, not MCMC. (I'm using it as a step within an iterative imputation algorithm.) This isn't hard to do: each step of a glm iteration simply linearizes the derivative of the log-likelihood, and, at this point, essentially no

Hi, I would like to apply the L-BFGS optimization algorithm to compute the MLE of a multilevel multinomial Logistic Regression. The likelihood formula for this model has as one of the summands the formula for computing the likelihood of an ordinary (single-level) multinomial logit regression. So I would basically need the R implementation for this formula. The L-BFGS algorithm also requires

Workshop on Bayesian methods and WinBUGS *************************************** A two-day workshop on Bayesian methods is being held on Friday 3 - Saturday 4 February 2012 at the University of Sydney. This course is suitable for graduate students, academics, researchers and professionals who wish to introduce themselves in the application of Bayesian methods and the use of WinBUGS software.

Dear list members, Apologies for cross-posting. Please, find below the information of two statistical courses with R: 1) Statistical Analysis with R 2) Bayesian Data Analysis with R and WinBUGS If you have any question don't hesitate to contact me. Best regards, Pablo ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ *Two days course in: Statistical Analysis with R *Where:

Dear R users, I am pleased to announce the release of a new packaged called `bcrm? (version 0.1), now available on CRAN. The package implements a wide range of Bayesian continuous reassessment method (CRM) designs to be used in Phase I dose-escalation trials. The package is fully documented and highlights include ? A choice of 1-parameter working models or the 2-parameter logistic model.

Dear R users, I am pleased to announce the release of a new packaged called `bcrm? (version 0.1), now available on CRAN. The package implements a wide range of Bayesian continuous reassessment method (CRM) designs to be used in Phase I dose-escalation trials. The package is fully documented and highlights include ? A choice of 1-parameter working models or the 2-parameter logistic model.

Hi Everybody, A new package called ``Bayesian Prediction with High-order Interactions'' is available from CRAN. The description of this package is as follows" "This R package is used in two situations. The first is to predict the next outcome based on the previous states of a discrete sequence. The second is to classify a discrete response based on a number of discreate

Hi Everybody, A new package called ``Bayesian Prediction with High-order Interactions'' is available from CRAN. The description of this package is as follows" "This R package is used in two situations. The first is to predict the next outcome based on the previous states of a discrete sequence. The second is to classify a discrete response based on a number of discreate

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

Hello, I'm interested in doing Bayesian kriging using R. I see that the package geoR has a function that will allow one to do this (krige.bayes). However, my data are not in the form of points, but rather they are blocks that represent spatial averages (i.e., the number of fishing hooks per month in a given lat x long square). I am therefore interested in treating the data as

Is there a way to do hierarchical (bayesian) logistic regression in R, the way we do it in BUGS? For example in BUGS we can have this model: model {for(i in 1:N) { y[i] ~ dbin(p[i],n[i]) logit(p[i]) <- beta0+beta1*x1[i]+beta2*x2[i]+beta3*x3[i] } sd ~ dunif(0,10) tau <- pow(sd, -2) beta0 ~ dnorm(0,0.1) beta1 ~ dnorm(0,tau) beta2 ~ dnorm(0,tau) beta3 ~

I am working with a logistic-normal model (i.e, GLMM with random intercept model) by Bayesian method. BUt I met some difficulities for programming by R. Is there anyone have experience of this model or the R code I can refer as example? Thanks for your help. Syl

First of all I should say this email is more of a general statistics questions rather than being specific to using R but I'm hoping that this may be of general interest. I have a dataset that I would really like to use PCA on and have been using the package pcaMethods to examine my data. The results using traditional PCA come out really nicely. The dataset is comprised of a set of questions

Hello R users! I've been experimenting with lmer to estimate a mixed model with a dichotomous dependent variable. The goal is to fit a hierarchical model in which we compare the effect of individual and city-level variables. I've run up against a conceptual problem that I expect one of you can clear up for me. The question is about random effects in the context of a model fit with a

Hi, as you can see in the topic, I am trying to fit a normal mixture distribution with the approach suggested by Hamilton (1991). Since I couldn't find any existing packages including the quasi-bayesian mle, I have to write my own function. Unfortunately, I have absolutely no experience in doing this. If you're not familiar with the QB-MLE, I attached the formula as pdf. The idea

I familarized myelf with kalmanlike and structts which are approaches for building and estimating ( and forecasting ) state space models ( or the equivalent arima models ). back in 2003, gavin simpson wrote an email describing the west and harrison apprach to estimate state space models and asked if anything was out there for using that approach. the goals of this approach are the same as kalman

Hi The Food and Agriculture Organization of the United Nations (FAO), based in Rome, is in urgent need of an econometrican/statistican. The candidate will be required to provide in the absence of primary information model-based estimates of elements of utilization that enter our Food Balance Sheets (see http://faostat3.fao.org/home/index.html#HOME). We are therefore looking for two candidates

Dear R users, I would like to fit cross classified or multiple membership logistic models or a 3 level hierarchical logistic model using the Umacs package. Can anyone advise me on how to proceed or better point me to examples of how its done. Regards, -- Luwis Diya, Leuven Biostatistics and Statistical Bioinformatics Centre (L-BioStat), Kapucijnenvoer 35 blok d - bus 7001, 3000 Leuven,