similar to: Bayes weighted regression?

Dear R-list, I am wondering how to perform a bootstrap in R for the weighted time dependent Cox model? (Andersen?Gill format, with multiple observations from each patients) to obtain the bootstrap standard error of the treatment effect. Below is an example dataset. Would 'censboot' be appropriate to use in this context? Any suggestions/references/direction to R-package will be highly

Dear List, Say, we generate data like this- dat<-rnorm(1000,1,2) hist(dat) How do i make the histogram, say, red (col = 2) before X = dat = 0, and rest say, green (col = 3) beyond X = dat = 0 in R? The resulting histogram could be like this http://ehsan.karim.googlepages.com/histogram.JPG (edited) Thanks in advance. Ehsan http://ehsan.karim.googlepages.com/diaryofastatistician

Hi, I'm attending summer School at UCLA (IPAM) on "probabilistics models of cognition". I have been an R-user since v. 1.4.1, but was trained in the frequentist tradition (as most psychologists!). I found that all faculty here use matlab and Murphy's bayes net toolbox. I have not had the need to use matlab before, and would love to stick to R for graphics models and

Hello fellow R programmers, I'm trying to use package e1071's naiveBayes function to create a model with weighted data. See example below, variable "d" is a count variable that provides the # of records for the given observation combination. Is anyone aware of a "weight" argument to this method? I've been unsuccessful in my research. Thanks, Mike

Dear List Members, I need some help about programming in S language. My problem is as follows: I have meteorological data (about rainfall measurement each day from 1989-2002), say like http://www.angelfire.com/ab5/get5/data.rainfall.txt or http://www.angelfire.com/ab5/get5/R.rainfall.txt in a sequence of 0(denoting dry day)'s and 1(denoting wet day)'s. I want to construct a frequency

Hello R Community, Subject: Bayes Factor A Bayesian ANOVA of the form: competitionBayesOut <- anovaBF(biomass ~ clipping, data = competition) Returns the following Error message: Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ?compare? for signature ?"BFlinearModel", "missing", "tbl_df"? My guess the problem is in

Dear List, After including cluster() option the coxreg (from eha package) produces results slightly different than that of coxph (from survival) in the following time-dependent treatment effect calculation (example is used just to make the point). Will appreciate any explaination / comment. cheers, Ehsan ############################ require(survival) require(eha) data(heart) # create weights

Hi, Currently I testing the packets that contain built-in features for classification. Actually I looked packages such as: e1071, Klar, Caret, CORElearn. However, from what I noticed when building a naive Bayesian classifier, that they package use of the finite mixture model to estimate P (x | C) and using a normal distribution. In my research I use binary data and I want modeled P (x | C), eg the

Dear R users, Thanks in advance. I am Deb, Statistician at NSW Department of Commerce, Sydney. I am using R 2.9.1 on Windows XP. This has reference to the package “MCMCpack”. My objective is to select a better model using various alternatives. I have provided here an example code from MCMCpack.pdf. The matrix of Bayes Factors is: model1 model2 model3 model1 1.000 14.08

Dear List, Suppose, I have some observed and expected frequencies, such as following. I need to draw a graph where plots of observed and expected frequencies are merged into one. ------------------------------------------------ m <- c(1,2,3,4,5,6,7,8,9,10,12,13,17) k <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 19) ExpWW <- c(0.309330628803245, 0.213645190887434, 0.147558189649435,

Hi Ehsan, All of the projects you have listed are quite interesting. If I were to advocate for one, it would be #2. I think the scope of work is perfect for GSoc. I'd encourage send out a more concrete proposal when you're ready. Thanks, Evan On Mar 27, 2009, at 2:35 PM, Ehsan Amiri wrote: > Dear all > > I am a PhD student of Computer Scince at Simon Fraser University

Dear all, I am pleased to announce that CRAN now hosts my package gRaven. This provides wrappers for functions in the 'gRain' package to emulate some 'RHugin' functionality, allowing the building of Bayesian networks consisting of discrete chance nodes incrementally - through adding nodes, edges and conditional probability tables, the setting of evidence, whether

Dear all, I am pleased to announce that CRAN now hosts my package gRaven. This provides wrappers for functions in the 'gRain' package to emulate some 'RHugin' functionality, allowing the building of Bayesian networks consisting of discrete chance nodes incrementally - through adding nodes, edges and conditional probability tables, the setting of evidence, whether

Hi This is a question regarding classification performance using different methods. So far I've tried NaiveBayes (klaR package), svm (e1071) package and randomForest (randomForest). What has puzzled me is that randomForest seems to perform far better (32% classification error) than svm and NaiveBayes, which have similar classification errors (45%, 48% respectively). A similar difference in

dcemri 0.10 has been released on CRAN "dcemri" is (to the best of my knowledge) the first public-domain software package for the quantitative analysis of dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DW-MRI or DWI). Data import and export is availble for ANALYZE or NIfTI data formats (sorry, no DICOM). Images are stored in neurological format regardless of the

dcemri 0.10 has been released on CRAN "dcemri" is (to the best of my knowledge) the first public-domain software package for the quantitative analysis of dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DW-MRI or DWI). Data import and export is availble for ANALYZE or NIfTI data formats (sorry, no DICOM). Images are stored in neurological format regardless of the

Hi Evan Thanks for the email. I had a look at gcc implementation of TBAA and I think three main steps in implementation of TBAA for LLVM will be this: April 20 ~ May 23: I will read gcc implementation in depth and play around with LLVM code. May 23 ~ July 6: Implementation and test of a simple version of TBAA that does not work with all aggregate types. I think part of the coding required for

Dear List, Can anyone please explain the difference between cluster() and frailty() in a coxph? I am a bit puzzled about it. Would appreciate any useful reference or direction. cheers, Ehsan > marginal.model <- coxph(Surv(time, status) ~ rx + cluster(litter), rats) > frailty.model <- coxph(Surv(time, status) ~ rx + frailty(litter), rats) > marginal.model Call: coxph(formula =

Dear UseRs: Recently, I have read an article regarding the association between age and lymph node metastases. http://jco.ascopubs.org/content/27/18/2931.long In statistical analysis, the authors stated "Because a nonlinear relationship between age and lymph node involvement was expected based on existing literature, lymph node involvement was also regressed on age using nonparametric

Actually, I re-read the post and think it needs clarification. We may both be right. If the question is "I am building a model and want to know if I should retain this random effect?" (or something like that) then the LRT should be used to compare the fitted model against another model. This would be accomplished via anova(). In other multilevel programs, the variance components are