similar to: prediction for linear mixed model

Hello, I have compared the potentials of R and Stata about GLMM, analysing the dataset 'ohio' in the package 'faraway' (the same dataset is analysed with GEE in the book 'Extending the linear model with R' by Julian Faraway). Basically, I've tried the 2 commands 'glmmPQL' and 'glmer' of R and the command 'xtmelogit' of Stata. If I'm not

Hi all, I was trying to fit a Gamma hierarchical model using "glmer", but got weird error message that I could not understand. On the other hand, a similar call to the glmmPQL leads to results that are close to what I expect. I also tried to change tha "nAGQ" argument in "glmer", but it did not solve the problem. The model I was fitting has a simple structure - one

Has there been any follow up to this question? I have found myself wondering the same thing: How then does SAS fit a beta distributed GLMM? It also fits the negative binomial distribution. Both of these would be useful in glmer/lmer if they aren't 'illegal' as Brian suggested. Especially as SAS indicates a favorable delta BIC of over 1000 when I fit the beta to my data (could be the

Hello, I am fitting a gmler using poisson, and I was looking for a documentation to interpret correctly the output. I'm quite a beginner with these kind of models. I couldn't find something in the lme4 package manual. and on the internet neither... Thank you, Raphaelle -- View this message in context: http://www.nabble.com/glmer-documentation-tp21506036p21506036.html Sent from the R

Sub: using glmer to fit a mixed-effects model with gamma-distributed response variable Hello, I'm currently trying to fit a mixed effects model , i.e.: > burnedmodel1.2<-glmer(gpost.f.crwn.length~lg.shigo.av+dbh+leaf.area+ bark.thick.bh+ht.any+ht.alive+(1|site/transect/plot), family=gaussian, na.action=na.omit, data=rws30.BL) If I run this code, I get the error below: Error:

Hi, I have the following type of data: 86 subjects in three independent groups (high power vs low power vs control). Each subject solves 8 reasoning problems of two kinds: conflict problems and noconflict problems. I measure accuracy in solving the reasoning problems. To summarize: binary response, 1 within subject var (TYPE), 1 between subject var (POWER). I wanted to fit the following model:

Hi All, I am making the switch to R and uncertain which of the several packages for mixed models is appropriate for my analysis. I am waiting for Pinheiro and Bates' book to arrive via inter-library loan, but it will be a week or more before it arrives. I am trying to fit a generalized linear mixed model of survival data (successes/trials) as a function of several categorical fixed and

Hi, I have to analyse the number of provisioning trips to nestlings according to a number of biological and environmental factors. I was thinking of building a mixed-effects model with species and nestid as random effects, using a Poisson distribution, but the data are overdispersed (variance/mean = 5). I then thought of using a mixed-effects model with negative binomial distribution, but I have

Hola, Tengo una base de datos con estructura jerárquica en la que quiero clasificar observaciones en distintas categorías. En el caso más simple, tengo una variable con dos categorías (variable Y1) y dentro de cada una de ellas hay otras dos categorías (variable Y2). Además tengo una variable explicativa cuantitativa discreta X. El banco de datos sería de este tipo: X Y1 Y2 5 0 1 9 0 0 2

Hi, Is there any similar command to "predict" which can be used with a logistic random effects model? I have run a random effects model using "lme()", and then use "predict.lme()" with no problems. However, I would also like to run a logistic random effects model, and then also run a predict command on the logistic random effects model. If I use "lme()",

I have a linear mixed-effects model (from the package nlme) with a random effect; Is there something like an "r squared" for the whole model which I can state? I´d like to kown: How would I do anova for a linear mixed-effects model? Lic. Florencia BonattoUniversidad Nacional de Rio Cuarto, Cordoba. [[alternative HTML version deleted]]

I'm trying to figure out how to carry out a Poisson regression fit to longitudinal data with a gamma distribution with unknown shape and scale parameters. I've tried the 'lmer4' package's glmer() function, which fits the Poisson regression using: library('lme4') fit5<- glmer(seizures ~ time + progabide + timeXprog + offset(lnPeriod) + (1|id), data=pdata,

I want to analyze binary, multinomial, and count outcomes (as well as the occasional continuous one) for clustered data. The more I search the less I know, and so I'm hoping the list can provide me some guidance about which of the many alternatives to choose. The nlme package seemed the obvious place to start. However, it seems to be using specifications from nls, which does non-linear

Hi, can anyone tell me how to nest two fixed factors using glmer in lme4? I have a split-plot design with two fixed factors - A (whole plot factor) and B (subplot factor), both with two levels. I want to do GLMM as I also want to include different plots as a random factor. But I am interested on the effect of A a B and their interaction on the response variable. I tried

Hello everyone, I'm quite new to R and am trying to run a logistic model to look at how various measures of boldness in individual animals influences probability of capture, however I also want to include random effects and I'm not sure how to construct a model that incorporates both of these things. Data was collected from 6 different groups of 6 individuals with 10 replicates for

Hi Dears, When I introduce an interaciton in a piecewise model I obtain some quite unusual results. If that would't take u such a problem I'd really appreciate an advise from you. I've reproduced an example below... Many thanks x<-rnorm(1000) y<-exp(-x)+rnorm(1000) plot(x,y) abline(v=-1,col=2,lty=2) mod<-lm(y~x+x*(x>-1)) summary(mod) yy<-predict(mod)

After upgrading to R 2.6.0, I'm having trouble running lmer: model <- lmer(primed ~ log(dist.time)*role + 1|target.utt, data=data.utts) Error in UseMethod("as.logical") : no applicable method for "as.logical" So I thought I'd upgrade lme4 to the latest version, but unfortunately the compilation fails - perhaps there's a missing #include: R CMD INSTALL

Dear all, In order to be sure I understand glmm correctly, I try to reproduce by hand a simple result. Here is a reproducible code. The questions are in _________________ Of course I have tried to find the solution using internet but I was not able to find a solution. I have also tried to follow glmer but it is very complicated code! Thanks for any help. Marc # Generate set of df with nb

Hi, I'm new to R and GLMMs, and I've been unable to find the answers to my questions by trawling through the R help archives. I'm hoping someone here can help me. I'm running an analysis on Seedling survival (count data=Poisson distribution) on restoration sites, and my main interest is in determining whether the Nutrients (N) and water absorbing polymer Gel (G) additions to the

Dear all, I am looking for an R package that fits multivariate gaussian or non-gaussian longitudinal outcomes. I am especially interested to non-gaussian outcomes since the outcomes I've got are discrete (some are binomial and some are count data). Many thanks in advance, Abderrahim [[alternative HTML version deleted]]