similar to: formula in lmer including both nested and crossed effects

Dear all, I want to estimate a crossed-random-effects model (i.e., measurements, students, schools) where students migrate between schools over time. I'm interested in the fixed effects of "SES", "age" and their interaction on "read" (reading achievement) while accounting for the sample design. Based on a previous post, I'm specifying my model as: fm1 <-

Hi all, I am not a very proficient R-user yet, so I hope I am not wasting people?s time. I want to run a linear mixed model with 3 random factors (A, B, C) where A and B are partially crossed and C is nested within B. I understand that this is not easily possible using lme but it might be using lmer. I encountered two problems when trying: Firstly, I can enter two random factors in lmer but

Hi all, I am just starting my first models in R and am having trouble with some of the basics. The main things at the moment are about setting up my data correctly. I have a repeated measures design-all participants complete 4 experimental conditions. I want to fit a linear mixed effects model with crossed random effects for subject and item. I have 4 conditions, 6 items per condition. Because

Hello R users, I've read the posts on this topic, and had a look at the R documentation for nlme, but I can't seem to make this work. I'd like to be able to fit a mixed effects model with crossed random effects, but also be able to specify the covariance matrix structure for the residuals. Here's the syntax using the lmer function in lme4 (which doesn't currently allow

Hi, I would like to know whether it is possible to obtain a value of significance for random effects when aplying the lme or related functions. The default output in R is just a variance and standard deviation measurement. I feel it would be possible to obtain the significance of these random effects by comparing models with and without these effects. However, I'm not used to perform

Dear all, I'd appreciate some advice on the following problem. I'm attempting to analyse a nested cross-sectional design in which an intervention was offered to a series of randomly selected (small) communities, so the unit of randomisation is the community. All available individuals in each community were interviewed before the intervention and again at follow-up

Dear all, I want to estimate a crossed-random-effects model (i.e., measurements, students, schools) where students migrate between schools over time. I'm interested in the fixed effects of "SES", "age" and their interaction on "read" (reading achievement) while accounting for the sample design. Based on a previous post, I'm specifying my model as: fm1 <-

Sounds distinctly like an assignment you've been set for which we wouldn't help. All I'll say is crossed random effects can be dealt with effectively in lmer. See that for more. -- View this message in context: http://r.789695.n4.nabble.com/A-question-about-conducting-crossed-random-effects-in-R-tp2278443p2279508.html Sent from the R help mailing list archive at Nabble.com.

I haven't been able to fully make sense of the conflicting online information about whether and how to specify nesting structure for a nested, mixed model. I'll describe my experiment and hopefully somebody who knows lme4 well can help. We're measuring the fluorescence intensity of brain slices from frogs that have undergone various treatments. We want to use multcomp to look for

I have a large dataset where each Subject answered seven similar Items, which are binary yes/no questions. So I've always used Subject and Item random effects in my models, fit with lmer(), e.g.: model<-lmer(Response~Race+Gender+...+(1|Subject_ID)+(1| Item_ID),data,binomial) But I recently realized something. Most of the variables that I've tested as fixed effects are properties

Hi everybody, I am trying to fit a model with the lmer function for mixed effects. I have an experimental design consisting of 5 field plots. Each plot is divided in 12 subplots where the influence of three factors on the growing of tree seedlings is tested: (1) seed (1 = presence; 0 = absence); (2) seedling species (oak holm vs. pine); (3) treatment (three different treatments). In each of

Hi All, I am frustrated by mixed-effects model! I have searched the web for hours, and found lots on the nested anova, but nothing useful on my specific case, which is: a random factor (C) is nested within one of the fixed-factors (A), and a second fixed factor (B) is crossed with the first fixed factor: C/A A B A x B My question: I have a functioning model using the aov command (see

I am trying to replicate the repeated measures example from Dr.Faraway's book (Extending the linear model with R) as follows: data(vision) vision$npower <- rep(1:4,14) mmod <-lmer(acuity~power+(1|subject)+(1|subject:eye),vision) When I look at the fixed effects p-value, it is missing. Am I missing something here? Thanks. Fixed effects: Estimate Std. Error t value

Hi, I am new to this list. I have a question regarding including both spatial and temporal random factors in lmer. These two are not nested, and an example of model I try to fit is model1<-lmer(Richness~Y+Canopy+Veg_cm+Treatment+(1|Site/Block/Plot)+(1|Year), family=poisson, REML=FALSE), where richness = integer Y & Treatment = factor Canopy & Veg_cm = numerical, continous

This question comes up periodically, probably enough to give it a proper thread and maybe point to this thread for reference (similar to the 'conservative anova' thread not too long ago). Moving from lme syntax, which is the function found in the nlme package, to lmer syntax (found in lme4) is not too difficult. It is probably useful to first explain what the differences are between the

Dear R Users, I have a question based on my research. I am analyzing reader-based diagnostic data set. My study involves diabetic patients who were evaluated for treatable diabetic retinopathy based on the presence or absence of two pathologies in their eyes. Pathologies were identified using the clinical examination (Gold standard method). In addition it can be identified by taking digital

Hi - I am doing some monte carlo simulations comparing bayesian (using Plummer's jags) and maximum likelihood (using lmer from package lme4 by Bates et al). I would like to know if there is a way I can flag nonconvergence and exceptions. Currently the simulations just stop and the output reads things like: Error in optim(.Call("lmer_coef", x, 2, PACKAGE = "Matrix"), fn,

Dear R experts, Could you please tell me how to nest random effects in an lmer model? Right now, my function looks like this: Full<-lmer(Consortship~((1|Male)+(1|Female))+MaleRank+MaleAge+MMGroom+MMAggrRankControl+FemaleRank+FemaleAge+MFAggrMating+GroomBirth07+MatNoConsortGroomF, family=poisson) However, a) I'm not sure if placing the two random effects (Male and Female) in parentheses

Hi all, I try to build a model using lmer, with 2 crossed fixed factors (poptype and matingtype) and 2 random factors (pop and family) which I want to nest within poptype. Which of these formulae should I use ? 1. lmer(y~poptype*matingtype + (1|poptype/pop/fam)) 2. lmer(y~poptype*matingtype + (poptype|/pop/fam)) thanks in advance Agnes -- Marie-Agn?s Coutellec UMR INRA-Agrocampus Ouest 985

Hi All, I was wondering if someone could help me to solve this issue with lmer. In order to understand the best mixed effects model to fit my data, I compared the following options according to the procedures specified in many papers (i.e. Baayen <http://www.google.it/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0CDsQFjAA