similar to: How to view the source of code?

The object is a mcmc sample from lmer. I am using R v2.7.1. Please let me know what additional information I can provide, hopefully I am just making a simple mistake. Thanks in advance! > data(ratdrink, package = 'faraway') > rd.er <- lmer(wt ~ weeks*treat + (1 | subject), data = ratdrink) > rd.mc <- mcmcsamp(rd.er, 10000) > library(coda) Loading required package:

Hi, I've got a linear mixed model created using lmer: A6mlm <- lmer(Score ~ division + (1|school), data=Age6m) (To those of you to whom this model looks familiar, thanks for your patience with this & my other questions.) Anyway, I was trying this to look at the significance of my fixed effects: A6post <- mcmcsamp(A6mlm, 50000) library(coda) HPDinterval(A6post) ..but I got this

Dear all, I'm not 100% sure if this question is best directed at the r-list, or a mailing list concerned with Bayesian analysis, so please accept my apologies if another audience may be more appropriate. I have been using the rjags package to run Jags models with multiple chains and store the results in a Coda based mcmc list. For instance, having created a jags model and done initial

I've been running a glmer logit on a very large data set (600k obs). Running on a 10% subset works correctly, but for the complete data set, R completes apparently without error, but does not display the results. Given these jobs take about 200 hours, it's very hard to make progress by trial and error. I append the code and the sample and complete output. As is apparent, I upgraded R

Dear R users I am trying to obtain p-values for (quasi)poisson lmer models, including Markov-chain Monte Carlo sampling and the command summary. > > My problems is that p values derived from both these methods are totally different. My question is (1) there a bug in my code and > (2) How can I proceed, left with these uncertainties in the estimations of > the p-values? > > Below

Hi, Can anyone tell me why I am not getting the correct intervals for fixed effect terms for the following generalized linear mixed model from HPDinterval: > sessionInfo() R version 2.4.1 (2006-12-18) i386-pc-mingw32 locale: LC_COLLATE=English_United States.1252;LC_CTYPE=English_United States.1252;LC_MONETARY=English_United States.1252;LC_NUMERIC=C;LC_TIME=English_United States.1252

Hi all mailing listers, I want to export data with specified precision into .txt file. How can I make it? See below sprintf("%.10f",pi) [1] "3.1415926536" when carry out write.matrix(pi,"pi.txt"), 3.141592653589793115998 in pi.txt file not with 10 decimal places like using sprintf("%.10f",pi) Thanks Marino [[alternative HTML version deleted]]

Dear R users I am trying to obtain p-values for (quasi)poisson lmer models, using Markov-chain Monte Carlo sampling and the command summary. > > My problems is that p values derived from both these methods are totally different. My question is (1) there a bug in my code and > (2) How can I proceed, left with these uncertainties in the estimations of > the p-values? > > Below is

(To the list moderator: I just subscribed to the list. Apologies for not having done so longer before trying to post.) Hi all, I am currently using lmer to analyze data from an experiment with a single fixed factor (treatment, 6 levels) and a single random factor (block). I've been trying to follow the online guidance for estimating p-values for parameter estimates on these and other

Dear I am trying to find a 90% credible interval. I am using the following code. fit<-bugs( model.file=BUGScode, data=data, inits = list(geninits1,geninits2), parameters.to.save=keepers, n.chains=nchains, n.iter=runs, n.burnin=burn, n.thin=nthin, DIC= TRUE, bugs.directory="C:/Program Files/WINBUGS.14", \ ) But this is only giving 95%

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

Dear r-helpers, Following up on http://finzi.psych.upenn.edu/R/Rhelp02a/archive/ 81159.html where Douglas Bates gives a helpful application of lmer() to data(sleepstudy, package = 'lme4'), I need a bit more help in order to plot the correct confidence intervals of a designed experiment such as: > data(ratdrink, package = 'faraway') I follow the steps Douglas took in

Hello, I got some of the R source code and not being able to Run it in RStudio. I get the error: Error in setwd("dir") : cannot change working directory I have gone through forums but nothing seemed relevant to my issue. What bugs me the most is the ("dir") that the error shows, is of those who wrote the source code and not mine(it still sees the directory of their

Hello, I recently updated to R 2.10 on my 64 bit Dell Precision T7400 running Enterprise Linux and now I can't get the arm library to load when I run R using Wine. Prior to the update I was able to use arm without any trouble. My main goal is to use arm with WinBUGS on this machine, calling it from R for Windows, which is executed with wine. After the update, when I try to load the arm

Hi folks, I have assumed that ratios of variance components (Fst and Qst in population genetics) could be estimated using the output of mcmcsamp (the series on mcmc sample estimates of variance components). What I have started to do is to use the matrix output that included the log(variances), exponentiate, calculate the relevant ratio, and apply either quantile or or HPDinterval to get

Dear all, I am running a mixed model using lmer. In order to obtain CI of individual coefficients I use mcmcsamp. However, I need advice which values that are most appropriate to present in result section of a paper. I have not used mixed models and lmer so much before so my question is probably very naive. However, to avoid to much problems with journal editors and referees addicted to

Hi! I am trying to calculate HPD for the coeficients of regression models fitted with lm or lmrob in R, pretty much in the same way that can be accomplished by the association of mcmcsamp and HPDinterval functions for multilevel models fitted with lmer. Can anyone point me in the right direction on which packages/how to implement this? Thanks for your time! R. [[alternative HTML version

Hello, I got some of the R source code and not being able to Run it in RStudio. I get the error: Error in setwd("dir") : cannot change working directory I have gone through forums but nothing seemed relevant to my issue. What bugs me the most is the ("dir") that the error shows, is of those who wrote the source code and not mine(it still sees the directory of their

Dear all, I am currently analyzing count data from a hierarchical design, and I?ve tried to follow the suggestions for a correct estimation of p-values as discusssed at R-Wiki (http://wiki.r-project.org/rwiki/doku.php?id=guides:lmer-tests&s=lme%20and%20aov). However, I have the problem that my model only consists of parameters with just 1 d.f. (intercepts, slopes), so that the

Dear R users, I?d like to pose aquestion about pMCMC and HDP. I have performed a mixed logistic regression by MCMCglmm (a very good package) obtaining the following results: Iterations = 250001:799901 Thinning interval = 100 Sample size = 5500 DIC: 10.17416 G-structure: ~ID_an post.mean l-95% CI u-95% CIeff.samp ID_an 0.7023 0.0001367 3.678 2126 R-structure: ~units post.mean l-95%