similar to: lmer in package of lme4

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, 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

I'm still relatively new to R, so my apologies if this is covered somewhere. I've been running some mixed-effect models in R using lme{nlme}, but read in Faraway's recent book, Extending the Linear Model with R, that lmer in package lme4 is a much improved version. I tried using this approach, but the output for the fixed effects doesn't report a p-value or the degrees of freedom

I am helping my wife do some statistical analysis. She is a biologist, and she has performed some measurements on various genotypes of mice. My background is in applied mathematics and engineering, and I have a fairly good statistics background, but I am by no means a PhD level expert in statistical methods. We have used the lmer package to fit various models for the various experiments that she

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

Hello, I've just located the illuminating explanation by Douglas Bates on degrees of freedom in mixed models. The take-home message appears to be: don't trust the p-values from lme. Questions: Should I give up hypothesis testing for fixed effects terms in mixed models? Has my time spent reading Pinheiro & Bates been in vain? Is there a publication on this issue? Thanks, Dan Bebber

I've got a problem with "mcmcsamp" used with glmer objects produced with "lmer" from the lme4 package. When calling mcmcsamp, I get the error Error in if (var(y) == 0) { : missing value where TRUE/FALSE needed This does not occur with all models, but I can't find anything wrong with the dataset. If the error is in my data, can someone tell me what I am looking

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

I am trying to duplicate the example by Spencer Graves in the wiki, using lmer with the Nozzle data. http://wiki.r-project.org/rwiki/doku.php?id=guides:lmer-tests However the Chisq value and the fitAB values that are calculated are different compared to those in the example. I also get a warning message when I attempt the fitAB. Does anyone have any guidance as to why this might happen and how

Hi, I would appreciate help with the following model <<1>>= gunload <- read.table(hh('datasets/gunload.dat'), header = T) gunload$method <- factor(gunload$method, labels = c('new', 'old')) gunload$physique <- factor(gunload$group, labels = c('slight', 'average', 'heavy')) gunload$team9 <- factor(rep(1:9, each = 2)) @ This

Hello, R community, I have been using the lmer and mcmcsamp functions in R with some difficulty. I do not believe this is my code or data, however, because my attempts to use the sample code and 'sleepstudy' data provided with the lme4 packaged (and used on several R-Wiki pages) do not return the same results as those indicated in the help pages. For instance: > sessionInfo() R

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

I am analyzing data from an experiment with two factors: Carbon (+/-) and O3 (+/-), with 4 replicates of each treatment, and 4 harvests over a year. The treatments are assigned in a block design to individual Rings. I have approaches this as a repeated measures design. Fixed factors are Carbon, O3 and Harvest, with Ring assigned as a random variable. I have performed repeated measures analysis

Hello, I’m trying to fit the following model: Dependent variable: MAXDEPTH (the maximum depth reached by a penguin during a given dive) Fixed effects: SUCCESSMN (an index of the “individual quality” of a bird), STUDYDAY (the day of the study, from -5 to 20, with 0=Dec 20), and the interaction SUCCESSMN*STUDYDAY Random effect: BIRD (the bird id, as each bird is performing several dives)

Hi, I am having problems trying to assess the significance of random terms in a generalized linear mixed model using lme4 package. The model describes bird species richness R along roads (offset by log length of road log_length) as a function of fixed effects Shrub (%shrub cover) and Width (width of road), and random effect Site (nested within Site Cluster). >From reading answers to previous

Dear list, This is more of a stats question than an R question per se. First, I realize there has been a lot of discussion about the problems with estimating P-values from F-ratios for mixed-effects models in lme4. Using mcmcsamp() seems like a great alternative for evaluating the significance of individual coefficients, but not for groups of coefficients as might occur in an experimental design

I'm fitting a GLMM to some questionnaire data. The structure is J individuals, nested within I areas, all of whom answer the same K (ordinal) questions. The model I'm using is based on so-called continuation ratios, so that it can be fitted using the lme4 package. The lmer function fits the model just fine, but using mcmcsamp to judge the variability of the parameter estimates produces

(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

Hello, I’m trying to perform a REML analysis using the lmer() function (lme4 package). Well, it seems to work well, except that I’m not getting any p-value (see example below). Can someone tell me what I did wrong? Thanks for your help, Amélie > library(gdata) > dive <- read.xls("C:/Documents and Settings/Amelie/My Documents/Postdoc/CE 2005-2006/divebydive.xls",

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