similar to: question on DPpackage

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

Greetings, R users! It is with pleasure that I am announcing the release of msmtools package on CRAN: https://cran.r-project.org/web/packages/msmtools/index.html msmtools provides a fast and general method for restructuring classical longitudinal data into augmented ones. The reason for this is to facilitate the modeling of longitudinal data under a multi-state framework using the 'msm?

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 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

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 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 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?

Dear Sir/ Madam, I am writing about the panel data for my bachelor degree. I would really appreciate if You could help dealing with R functions. I am trying to estimate the panel data lm model with plm function. When i include 3dummy variables into the regression it dont appear in the sumarry of the model, but when i estimate a simple lm model it appears. Why is it so? What should i do to

Hi everyone, My goal is to sample from a two-dimensional grid. Consider the following example of code: n.grid <- 500 muA.grid <- seq(-4,4, length=n.grid) muB.grid <- seq(-4,4, length=n.grid) mu.p <- matrix(NA, nrow=n.grid, ncol=n.grid) for(i in 1:n.grid){ for(j in 1:n.grid){ mu.p[i,j] <- dnorm(muA.grid[i], 0, 1)*dnorm(muB.grid[j], 0, 0.5) } } mu.p <-

I'm using /parApply/() function in "snow" package for parallel computing (boostrapping repeating calculation), as the follows: MyBoostrapping <- function( i ){ ..... } cl <- makeCluster( ncluster, type = "SOCK") i.circle <- as.matrix( 1:128, , 1) parApply( cl, i.circle, 1, FUN = MyBoostrapping) It works. However, I want to set more than one arguments to

Hello, All, GWAF 1.2 R.Version() is below. system(lme.batch.imputed( phenfile = 'phenfile.csv', genfile = 'CARe_imputed_release.0.fhsR.gz', pedfile='pedfile.csv', phen='phen1', covar=c('covar1','covar2'), kinmat='imputed_fhs.kinship.RData', outfile='imputed.FHS.IBC.GWAF.LME.output.0.txt' )) Gives the error messages: Error in

I am making the question clear. Please help. > Dear R experts > > I was using kinship package to fit mixed model with kinship matrix. > The package looks like lme4, but I could find a way to extract p-value > out of it. I need to extract is as I need to analyse large number of > variables (> 10000). > > Please help me: > > require(kinship) > > #Generating

I didn't know that instant messaging was a feature with the Polycom's. Do you have any documentation, how-to's, etc. that you can point me to? That would be just way too cool. Thanks; John -----Original Message----- From: asterisk-users-bounces at lists.digium.com [mailto:asterisk-users-bounces at lists.digium.com] On Behalf Of Michael Englehorn Sent: Monday, January 12, 2015 12:19

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Is it possible to use the instant messaging feature of Polycom phones in Asterisk? At the moment I'm seeing this in the SIP messaging when I try to send one from a Polycom 450. <--- SIP read from UDP:<CENSORED POLYCOM IP>:5060 ---> INVITE sip:0100@<CENSORED>:5060;user=phone SIP/2.0 Via: SIP/2.0/UDP <CENSORED POLYCOM

Dear all, I can't get the labels slot in ICLUST to accept a character vector. library(psych) test.data <- Harman74.cor$cov ic.out <- ICLUST(test.data,nclusters =4,labels=letters[1:ncol(test.data)]) ##?Error in !labels : invalid argument type ic.out <- ICLUST(test.data,nclusters =4,labels=1:ncol(test.data)) ## OK Any ideas?

Hi, can I understand why this message was rejected ? Thanks, Eliano Sent from my iPhone On 6 Mar 2013, at 19:18, Eliano <eliano.m.marques at gmail.com> wrote: > Hi everyone, > > I am writing some code to generate a function. I am passing that code to a > dataset which i'm importing in R, e.g. > Test=read.table('C:/test.txt', header=F, sep='\t',

Dear All, What is the preferred way to get Bayesian analysis results (such as those from MCMCpacki, MCMCglmm, and DPpackage) into LaTeX table automatically? I have been using the "apsrtable" package and similar functions in "memisc" package, but neither seems to handle MCMC output directly. Many thanks. Shige

Dear all, I am encountering a cyclic dependency error when running R CMD check on an R package I wrote (R version 2.6.1, Mac OS X 10.4), see the error message below. Creating a new generic function for "print" in "clValid" Creating a new generic function for "summary" in "clValid" Creating a new generic function for "plot" in

Hallo R-users, I would like to know if there is a way to get the effective degrees of freedom in local polinomial regression. At the moment, to carry out the local polinomial regression, I am using the function locpoly() in library KernSmooth (I need to estimate both the regression function and its derivatives, up to the 3rd one). Thanks, Simone -- Simone Vantini MOX - Modeling and