similar to: MCMC results into LaTeX

Dear R users, Thanks in advance. I am Deb, Statistician at NSW Department of Commerce, Sydney. I am using R 2.8.1 on Windows XP. This has reference to the package “DPpackage”. The binary version is available on Mac OS, but I am using Windows XP. May I request you to assist me in the followings: How can I prepare Windows binary of DPpackage from source? 2. Is there any possibility

Dear All, I've had trouble compiling DPpackage as a user in one system. It works fine as root in other machines. I can see any clues in error messages My guess is that it is a permissions matter. Any help is appreciated. OS: Linux Kernel: 2.6.27 SMP Arch: Intel 64 bits gfortran not available Thank you. ----------------------><8------------------------------------- g77 ? -fpic ?-g

Hi - I am doing a monte carlo experiment that requires to do a linear regression of a matrix of vectors of dependent variables on a fixed set of covariates (one regression per vector). I am wondering if anyone has any idea of how to speed up the computations in R. The code follows: #regression function #Linear regression code qreg <- function(y,x) { X=cbind(1,x) m<-lm.fit(y=y,x=X)

Hi all, I would like to start to use R's MCMC abilities to compute answers in Bayesian statistics. I don't have any specific problems in mind yet, but I would like to be able to compute/sample posterior probabilities for low-dimensional custom models, as well as handle "standard" Bayesian cases like linear regression and hierarchical models. R clearly has a lot of abilities in

Dear List: I have uploaded version 1.0-4 of DPpackage on CRAN. Since the first version (1.0-0), I have not communicated the improvements of the package. I'll use this email to summarize its current status. The name of the package is motivated by the Dirichlet process. However, DPpackage tries to be a general package for Bayesian nonparametric and semi-parametric data analysis. So

Dear List: I have uploaded version 1.0-4 of DPpackage on CRAN. Since the first version (1.0-0), I have not communicated the improvements of the package. I'll use this email to summarize its current status. The name of the package is motivated by the Dirichlet process. However, DPpackage tries to be a general package for Bayesian nonparametric and semi-parametric data analysis. So

Dear List, I am pleased to announce the release of version 1.0.0 of DPpackage on CRAN. DPpackage covers some important models using Dirichlet process priors. The package includes: Semiparametric Bernoulli regression Semiparametric Density estimation Semiparametric Linear mixed models Semiparametric Generalized linear mixed models Semiparametric AFT model for interval-censored data I

Hi, I am trying to estimate a quantile regression using panel data. I am trying to use the model that is described in Dr. Koenker's article. So I use the code the that is posted in the following link: http://www.econ.uiuc.edu/~roger/research/panel/rq.fit.panel.R How to estimate the panel data quantile regression if the regression contains no constant term? I tried to change the code of

Dear List, I am trying to estimate a 3 dimensional density through the DPpackage. For example # model sigma <- matrix(c(0.1,0.05,0.05,0.05,0.1,0.05,0.05,0.05,0.1), ncol=3) rnormm<- rmvnorm(n=100, mean=c(5,100,150), sigma=sigma) sigma2 <- matrix(c(10,0.05,0.05,0.05,10,0.05,0.05,0.05,10), ncol=3) rnormm2<- rmvnorm(n=100, mean=c(20,1,110), sigma=sigma) rnormm<-rbind(rnormm,rnormm2)

Hi to everyone, I'm a PhD student and I'm involved in non parametric analyses of hierarchical models. I tried to use package DPpackage on my data, but I encountered some problems in interpreting ouputs. Can anybody help me? The problem can be remued as follows: I have a logit hierarchical model for survival (i.e. binary response) in patients affected by heart failure (the court

Hi, I'm new to R and am hoping someone might be able to help with the following lme problem. I am trying to fit an ellipse equation to some spatial human factors data, varying the major and minor axes randomly and specifying an exp~ variogram for errors. Using normally-distributed random effects produces some -ve minor/major axes. I am hoping to be able to specify a gamma distribution to

Hi, [note: 4th posting trial - apologize if the other ones would ever show up...] I have a (3-level) ordinal response data set which needs the integration of an spatial autocorrelation structure. What packages / functions are available to fit such a thing ? The heterogeneous, cluster-alike structuring of the autocorrelation seems to make a mixed effects model with random factors capturing

Hi, My computer has 2GB of ram and I also request 2GB of virtual ram from c drive, therefore totally I have 4GB of ram. Before I open R workshop, I also add "C:\Program Files\R\R-2.5.0\bin\Rgui.exe" --max-mem-size=3000Mb--max-vsize=3000Mb" into the target of R by right clicking the R icon-properties. I am running a program (DPglmm), which is built in DPpackage. The program is

Dear all, I'm attempting to use a piecewise regression to model the trajectory of reproductive traits with age in a longitudinal data set using a mixed model framework. The aim is to find three slopes and two points- the slope from low performance in early age to a point of high performance in middle age, the slope (may be 0) of the plateau from the start of high performance to the

Hi all, in the function DPdensity( ) example -- ?DPdensity --, we can extract the density estimate by: # Extracting the density estimate cbind(fit1.1$x1,fit1.1$dens) cbind(fit1.2$x1,fit1.2$dens) How is density calculated? I can imagine it is a mixture of normal densities, but where is the information about the density stored? Can we extract that so that we can write out the density function?

Hi all, I have used the apsrtable package to generate tables (using LaTeX and Sweave) for quite some. However, suddenly the notes option in the package appears to have stopped working (or I am doing something wrong). So, when I try to run the following commands (in the R console): > attach(NatExp.df) > model1.lm <- lm((1-schoolspend) ~ jadif) > model2.lm <- lm((1-nohousdisc) ~

I am just starting to use R, converting over from SAS. I want to estimate the upper quartile in a scatterplot of demographic rates against rainfall in a small mammal population. The data looks like this: | |* * <- point in question |* |* * |* * |* * * |* * * * * * |_______________________________________ Based on a paper by Scharf et al. in Ecology (vol 79,

Hi All Please see the Rprofile file which i have modified as follows and after that when I start R then I see that R says to me "TRUE" for all the packages implying that all loaded at once. But when i try to use commands as simple as help("lm"), it doesnt work nor any of the menu "Packages" is not working. Although the regression using lm ( Y ~ X ) is working

We would like to announce the following statistics course; Course: Introduction to Linear mixed effects models, GLMM and MCMC with R When: 10-14 February, 2014 Where: Pousada de juventude parque das nacoes. Rua de Moscavide, Lt 47 ? 101, 1998- 011. Lisbon, Portugal Info: http://www.highstat.com/statscourse.htm Flyer: http://www.highstat.com/Courses/Flyer2014_02SIM_LisbonV2.pdf Kind regards,

library(lme4) library(coda) library(languageR) fit = lmer(Reaction~Days + (1|Subject) + (0+Days|Subject), data=sleepstudy) pvals.fnc(fit)$random # compare with... samp = mcmcsamp(fit, n=10000, trans=FALSE) HPDinterval(samp) densityplot(samp, plot=F) # 'pvals.fnc' reports sigma instead of sigma^2, but it looks like the # Sbjc.(In) and Sbjc.Days are also sqrt compared with the