similar to: Regarding Modeling - Please! QUICK HELP

Hi all, I am having issues comparing models with lmer. As an example, when I run the code below the model summaries (AIC, BIC, loglik) differ between the summary() and anova() commands. Can anyone clear up what's wrong? Thank you! Darren Ward library(lme4) data(sleepstudy) fm1<-lmer(Reaction ~ Days + (1|Subject), sleepstudy) summary(fm1) fm2<-lmer(Reaction ~ Days +

Dear lmer-ers, My thanks for all of you who are sharing your trials and tribulations publicly. I was hoping to elicit some feedback on my thoughts on denominator degrees of freedom for F ratios in mixed models. These thoughts and practices result from my reading of previous postings by Doug Bates and others. - I start by assuming that the appropriate denominator degrees lies between n

Full_Name: Dale Barr Version: 2.5.1 (patched) OS: Ubuntu linux x86_64 Submission from: (NULL) (138.23.70.108) When RMySQL is loaded in before lme4, the summary() function for lmer objects in the lme4 packages produces the following error: Error in printMer(object) : no slot of name "status" for this object of class "table" When RMySQL is loaded AFTER lme4, however, no such

>>>>> "MM" == Martin Maechler <maechler at stat.math.ethz.ch> >>>>> on Tue, 18 May 2010 12:37:21 +0200 writes: >>>>> "GaGr" == Gabor Grothendieck <ggrothendieck at gmail.com> >>>>> on Mon, 17 May 2010 09:45:00 -0400 writes: GaGr> BIC seems like something that would logically go into stats

If you are indeed using lme and not lmer then the needed function is VarCorr(). However, 2 recommendations. First, this is a busy list and better emails subject headers get better attention. Second, I would recommend using lmer as it is much faster. However, VarCorr seems to be incompatible with lmer and I do not know of another function to work with lmer. Hence, a better email subject header

Hi, Thanks for your response, it nearly worked! But it only wrote one coloumn of data and not the three columns I need. Using fixef(m1) doesnt give the same results as coef(m1) when you are using more than one random effect. I need the coefficients for each individual so I use coef(m1) to get this which results in an object of class lmer.coef, 3 columns by 700 rows. as.data.frame() wont work on

Hello there, I am using the 'nlme' package to analyse among group and in between group variances. I do struggle a little using the respective plot-functions. I created a grouped object: group.lme <- groupedData(obsday ~ oro | id, data=read.table("data-lme.txt", header=T), labels=list(x = "Day of Year", y = "ID number")) When I plot, however

Dear List, I am looking for what B.S.Everitt refers to as Cochrane Method for testing independence in combined 2x2 contingency tables. Is it the same method as the Cochran-Mantel-Haenszel Chi-Squared Test for Count Data in R? Thanks, Serguei [[alternative HTML version deleted]]

Hi, I have been having a tedious issue with lmer models with lots of factors and lots of levels. In order to get the basic information at the beginning of the print out I also have to generate these enormous tables as well. Is there a method command to leave off all of the effects and correlations? Or, do I have to go to string commands?

LS, I'm estimating multilevel regression models, using the lme-function from the nlme-package. Let's say that I estimated a model and stored it inside the object named 'model'. The summary of that model is shown below: Using summary(model)$tTable , I receive the following output: > summary(model)$tTable Value Std.Error DF t-value

I'm trying to understand how to plot individual growth curve trajectories, with the overall mean trajectory superimposed (preferably in a slightly thicker line, maybe in black) over the individual trajectories. Using the sleepstudy data in lme4, here is the code I have so far: library(lme4) library(lattice) xyplot(Reaction ~ Days, data = sleepstudy, group = Subject, type = 'l')

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 all, I found a problem with 'lme4'. Basically, once you load the package 'aod' (Analysis of Overdispersed Data), the functions 'lmer' and 'glmer' don't work anymore: library(lme4) (fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)) (gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), family = binomial, data

Hi, For some reason do.call on anova fails if the models are named lmer objects. Consider the following example: library(lme4) models <- list( lmer(Reaction ~ Days + (1| Subject), sleepstudy), lmer(Reaction ~ Days + (Days | Subject), sleepstudy)) # # models is an unnamed list, do.call works (although with warning): do.call(anova, models) # # after labeling the models, do.call gives an

I encounter such problem with lmer under FreeBSD, but not under Windows. Anyone knows why? Thanks. > example(lmer) lmer> (fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)) Error in UseMethod("as.logical") : no applicable method for "as.logical" > traceback() 9: as.logical(EMverbose) 8: as.logical(EMverbose) 7: lmerControl() 6:

Dear All, I am working with linear mixed-effects models using the lme4 package in R. I created a model with the lmer function including some main effects, a two-way interaction and a random effect. Now I am searching how I could incorporate an interaction between the random effect and one of the fixed effects. I tried to express the interaction in:

Buenos días; Estoy intentando aprender algo de sobre los modelos lineales mixtos. Estoy siguiendo el libro "Multilevlel analysis: techniques and applications" de Joop Hox. Estoy intentado reproducir las tablas y gráficos que trae el libro con el paquete lmer. En este libro se indica que en la comprobación de los supuestos del modelo hay que estudiar los gráficos de residuales tanto

consider p as random effect with 5 levels, what is difference between these two models? > p5.random.p <- lmer(Y ~p+(1|p),data=p5,family=binomial,control=list(usePQL=FALSE,msV=1)) > p5.random.p1 <- lmer(Y ~1+(1|p),data=p5,family=binomial,control=list(usePQL=FALSE,msV=1)) thanks, Aimin Yan

Dear R users: In the example of meta-analysis (cochrane, package rmeta), I can not found the p-value of Test for overall effect, and some other indices (Z, I, weight and et al). How can I get the these indices listed? > library(rmeta) > data(cochrane) > cochrane name ev.trt n.trt ev.ctrl n.ctrl 1 Auckland 36 532 60 538 2 Block 1 69 5

Hello, Is there a package that implements the Cochrane-Orcutt itterative procedure for dealing with autocorrelation in a regression model? Thanks, John. -- ========================================== John Janmaat Department of Economics Acadia University, Wolfville, NS, B0P 1X0 (902)585-1461 All opinions stated are personal, unless otherwise indicated.