similar to: Predicted values based on fixed effects do not correspond with actual data in cross-classified generalized linear mixed model (lmer)

Dear R-users, I am currently trying to switch from SAS to R, and am not very familiar with R yet, so forgive me if this question is irrelevant. If I try to find the significance of the fixed factor "spikes" in a generalized linear mixed model, with "site" nested within "zone" as a random factor, I compare following two models with the anova function:

Hello all, I using lmer to develop a mixed effects model. I start with an overly parameterized model (as suggested in Zuur et al. Mixed Effects Models and Extension in Ecology with R) that looks something like this: m1 <- lmer( Y ~ aS + bS + c + d + e + (c|SpeciesId) + (d|SpeciesId) + (e|SpeciesId)) aS and bS are species level predictors an so do not vary within a SpeciesId. However, c, d, and

Greetings, I am interested in using a generalized linear mixed model with data that best fits a beta distribution (i.e., the data is bounded between 0 and 1 but is not binomial). I noticed that the beta distribution is not listed as an option in the "family objects" for glmmPQL or lmer. I found a thread on this listserve from 2006 ("[R] lmer and a response that is a

Dear All, I wonder if there is a way to fit a generalized linear mixed models (for repeated binomial data) via a direct Maximum Likelihood Approach. The "glmm" in the "repeated" package (Lindsey), the "glmmPQL" in the "MASS" package (Ripley) and "glmmGIBBS" (Myle and Calyton) are not using the full maximum likelihood as I understand. The

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

Dear R-users, Half a year ago we put out the R package "glmmADMB" for fitting overdispersed count data. http://otter-rsch.com/admbre/examples/glmmadmb/glmmADMB.html Several people who used this package have requested additional features. We now have a new version ready. The major new feature is that glmmADMB allows Bernoulli responses with logistic and probit links. In addition there

Craig A Faulhaber wrote: >I am interested in using a generalized linear mixed model with data > that best fits a beta distribution (i.e., the data is bounded between > 0 and 1 but is not binomial). .. >For clarification, here's what I'm trying to model: >I have a beta-distributed response variable (y). I have a fixed-effect >explanatory variable (treatment),

Hi, I am wondering if we can make prediction on a linear mixed model by lmer() from lme4 package? Specifically I am fitting a very simple glmer() with binomial family distribution, and want to see if I can get the predicted probability like that in regular logistic regression?   fit<-glmer(y~x+(1|id),dat,family=binomial)   where y is the response variable (0, 1), and x is a continuous variable.

Dear R-users, I'm using lmer to fit two-level logistic models and I'm interested in predicted probabilities that I get in this way (using "fitted"): glm1 = lmer(XY$T1~X1 + X2 + X3 + (1|Cind), family=binomial) #estimation of a two-level logit model fit1=fitted(glm1) # I get the fitted linear predictor ilog = function(x) { 1/(1 + exp(-x)) } ps1=ilog(fit1) # In

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 for a way to save the predicted values for this model. As far as I can see, there is no command in lme4 to save the predicted values (like the predict(model) function in e.g.

I have a dataset with 25 subjects and 25 items. For each subject-item combination, there's a 0/1 score for two parts, A and B. I'm thinking of this as a set of 2 x 2 tables, 25 x 25 of them. I'd like to fit a log-linear model to this data to test the independence of the A and B scores. If I ignore the subject and item parts, the following works just fine: glm(count ~ A * B,

Hi all, I'm new (post #1!) and I hope you'll forgive me if I'm acting like an idiot... I have been asked for some power analyses for some mixed-effects models I'm running using lmer. My studies nearly always contain mixes of repeated-measures and between-subjects predictor variables. As an example, suppose I want to see if men or women show a stronger word frequency effect. I

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

Hello R-users, I have been trying to fit what I think is a simple mixed-effects model using lmer (from lme4), but I've run into some difficulty that I have not been able to resolve using the existing archives or Pinheiro and Bates (2000). I am measuring populations (of birds) which change with time at a number of different sites. These sites are grouped into regions. Sites are not measured

All, I have several years of longitudinal test scores for students (many who switch schools at various points in time). I am using a mixed-effects model with crossed random effects to model student trajectories. The model includes time at level 1 and students crossed with schools at level 2. When I run the model I get the posterior variances on the intercepts and slopes for students and schools,

I'm using FC4 and R 2.3.1 to fit a mixed effects logistic regression. The response is 0/1 and both the response and the age are the same for each pair of observations for each subject (some observations are not paired). For example: id response age 1 0 30 1 0 30 2 1 55 2 1 55 3 0 37 4 1 52 5 0 39 5 0 39 etc. I get the

I saw different specifications for Random Effects and I'm confused about the use of "/" and the use of "(0+...|)" . Let say we have a nested structure where some countries have some several plants in different states and we measure the reaction to a drug. The list of Countries = USA, France, Italy The States for USA = Michigan, Florida, California The States for France

Dear people of the help list I am drying to analyze my data using the 'lmer' function and I keep having problems. This is the model: > fm1<-lmer(dbh~spec+scheme+(1|Plot),data=d, REML=FALSE). I analyse tree size (dbh) of 3 different species (spec) and 3 planting schemes (scheme). I have 5 plots, which I hope to model as a random factor. (However, the subsequent output is based on

Dear R users, I have a problem in plotting 3 dimensional graph using mixed models. My model is sur_prop ~ afr_c+I(afr_c^2)+I(afr_c^3)+byear_c+I(byear_c^2)+I(byear_c^3)+I(byear_c^4)+(1|Studyparish)+afr_c:byear_c +afr_c:I(byear_c^2)+afr_c:I(byear_c^3)+afr_c:I(byear_c^4)+I(afr_c^2):byear_c+I(afr_c^2):I(byear_c^2)+I(afr_c^2):I(byear_c^3)+I(afr_c^2):I(byear_c^4) This is a study on the effect of

Dear R users I have built several glmm models using glmmPQL in the following structure: m1<-glmmPQL(dev~env*har*treat+dens, random = ~1|pop/rep, family = Gamma) (full script below, data attached) I have tried all the methods I can find to obtain some sort of model fit score or to compare between models using following the deletion of terms (i.e. AIC, logLik, anova.lme(m1,m2)), but I