similar to: Bayesian Quantile regression installation

I typed the following command *install.packages('bayesQR')'* to install bayesQR(my R version is 2.14) i am encountered the following error. Installing package(s) into ?C:/Users/knreddy.IDRBTVM/Documents/R/win-library/2.14? (as ?lib? is unspecified) Warning: unable to access index for repository http://essrc.hyogo-u.ac.jp/cran/bin/windows/contrib/2.14 Warning: unable to access index

i have R 2.14 version.and i have downloaded bayesQR package from following link http:// http://cran.r-project.org/web/packages/bayesQR/index.ht ml my OS is Windows7.i have downloaded Windows binary: bayesQR_1.3.zip file from above link.I am new to R. So please tell me what is the next step i have to do inorder to install the bayesQR package.pls reply me as quickly as possible. thanks in

sir, i am trying to install r package Bayesian quantile regression but i am facing with following problem which says forPlease select a CRAN mirror for use in this session --- Warning: unable to access index for repository http://cran.cnr.Berkeley.edu/bin/windows/contrib/2.6 Warning: unable to access index for repository http://www.stats.ox.ac.uk/pub/RWin/bin/windows/contrib/2.6 Error in

i know this is not about R. After applying quantile regression with t=0.5,0.6 on the data set WBC( Wisconsin Breast Cancer)with 678 observations and 9 independent variables(inp1,inp2,...inp9) and 1 dependent variable(op) i have got the following results for beta values. when t=0.5(median regression) beta values b1=0.002641,b2=0.045746,b3=0.

Hi All: Recently I am researching my dissertation about the quantile model selection by bayesian approach. I have the dependent variable(return) and 16 independent variables and I need to select the best variable for each quantile of return. And the method I used is the bayesian approach, which is based on calculating the posterior distibution of model identifier. In other words, I need to obtain

Error in `[.data.frame`(x, order(x, na.last = na.last, decreasing = decreasing)) : undefined columns selected during the execution of following r sequence of commands X<-subset(data,select=c(V1,V2,V3,V4,V5,V6,V7,V8,V9)) y<-subset(data,selcet=10) Data = list(y=y, X=X, p=.75) Prior = list(betabar=c(rep(0,ncol(X))),A=.01*diag(ncol(X))) Mcmc = list(R=100000, keep=10, step=.2) out <-

quantreg package is used. *fit1 results are* Call: rq(formula = op ~ inp1 + inp2 + inp3 + inp4 + inp5 + inp6 + inp7 + inp8 + inp9, tau = 0.15, data = wbc) Coefficients: (Intercept) inp1 inp2 inp3 inp4 inp5 -0.191528450 0.005276347 0.021414032 0.016034803 0.007510343 0.005276347 inp6 inp7 inp8 inp9 0.058708544

Looking for help with a project for the US Navy, requires knowledge of Bayesian Statistics, Bayesian Networks and Survival Analysis. Please respond with CV. Thanks. -- David Katz www.davidkatzconsulting.com [[alternative HTML version deleted]]

Hi, I have data for one dependent variable and multiple independent variables y = b0 + b1*x1 + b2*x2 + ... I want to a list of all models that have some subset of the independents (just x1 x2, and not x3, etc.) and their corresponding BIC values. Is there a pre-existing function that does this? I saw that you can calculate individual BIC values using 'lm' and something like AIC(lm1, k

I want to try Bayesian cluster analysis. Someone suggested using package mcclust. Is there a website that says how to install mcclust or another appropriate Bayesian package? Including the appropriate R functions that I can follow? I am trying to get probability of membership for each individual I am trying to cluster Thank you! -- View this message in context:

Hi, I am interested in using a bayesian network as a predictor (machine learning); however, I can't get any of the implementations (deal, nblearn) to learn & predict stuff. Shouldn't there also be probabilites for each node after the learning phase, how can I access these? Cheers, Stephan -- View this message in context:

Version 2.2.0 of package bcp is now available.? It replaces the suggests of NetWorkSpaces (previously used for optional parallel MCMC) with the dependency on package foreach, giving greater flexibility and supporting a wider range of parallel backends (see doSNOW, doMC, etc...). For those unfamiliar with foreach (thanks to Steve Weston for this contribution), it's a beautiful and highly

Version 2.2.0 of package bcp is now available.? It replaces the suggests of NetWorkSpaces (previously used for optional parallel MCMC) with the dependency on package foreach, giving greater flexibility and supporting a wider range of parallel backends (see doSNOW, doMC, etc...). For those unfamiliar with foreach (thanks to Steve Weston for this contribution), it's a beautiful and highly

Hi, I have explored many of the R packages that construct Bayesian trees including the tgp, bart, BMA and maptree packages. I have also searched through some other packages but they do not seem to be suitable for the type of analysis I need to do. I need to construct Bayesian CART that have terminal nodes which have bivariate regressions (not multiple regressions like most of the packages do).

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.

Hello, Is there anywhere I can find some Bayesian Code for 2 by 2 tables or even the non-central hypergeometric distribution in R? Packages would be helpful but the actual coding in R is much better. Thanks, Thanks Jim [[alternative HTML version deleted]]

Hi, In Winbugs, one can assign a distribution called "dflat()", which corresponds to an improper uniform prior on the whole real line. What would be the equivalent in R? I ask because I'm trying to run Winbugs through R, and the model I've constructed in Winbugs includes a "dflat()" distribution. For all of the normal priors in Winbugs, I've been using

I'm trying to teach myself about Bayesian Networks and am working with the following data and the bnlearn package. I understand the conceptual aspects of BNs, but I'm not sure how to specify the response variables in R when constructing a dag plot. I've cecked ?hc and done numerous google searches without luck. Can anyone help? library("bnlearn")

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.