search for: hpdintervals

Hi all R mailing listers: I am using the coda package. I tried to view the source of HPDinterval code by typing fix(HPDinterval), it dispalys as follows: function (obj, prob = 0.95, ...) UseMethod("HPDinterval") Then I search the answers about this case (see below), it still failed. Thank you in advance! David > getAnywhere('HPDinterval')2 differing objects matching

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

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

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:

(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 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

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

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 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%

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

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

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

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 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 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

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

Dear R users, I have now tried out several options of obtaining p-values for (quasi)poisson lmer models, including Markov-chain Monte Carlo sampling and single-term deletions with subsequent chi-square tests (although I am aware that the latter may be problematic). However, I encountered several problems that can be classified as (1) the quasipoisson lmer model does not give p-values when

Dear R helpers, I need to find out maximized log likelihoods, parameters estimates and standard errors (in parentheses) of r largest-order statistics model, with different values of r by using the function rlarg.fit. I want to specify required number of order statistics to the model. I attached my data file with this mail.please help me. Ruposh --- r-help-request at stat.math.ethz.ch wrote:

Dear R users, I have developed a model I have compared several options of obtaining p-values for poisson lmer model including Marlov chain monty carlo methods, single term deletions and summary. > > However, I encountered several problems that can be classified as > (1) the p values from the summary command are total different from those derived from Marlov chain monty carlo methods >

When I do lmer models I only get Estimate, Standard Error and t value in the output for the fixed effects. Is there a way I get degrees of freedom and p values as well? I'm a very new to R, so sorry if this a stupid question. Thank you - Mike Mike Ford Centre for Speech and Language Department of Experimental Psychology Downing Street Cambridge CB2 3EB Tel: +44 (0) 1223 766559 Fax: +44