similar to: difference between lme and lmer in df calculation

I have a slight conundrum. I'm attempting to write a scrip that will take a number of objects (lm, glm, and lmer) and return AIC scores and weights. I've run into 3 problems, and was wondering if anyone had any pointers. 1) is there any convenient way to extract the name of the objects? Simply, if I have a vector of objects c(my.lm, my.lmer) and I want to get a character

I'm attempting to write a general function to implement Faraway's bootstrapping algorithm for mixed models with lmer, but have run into a curious problem. I'm comparing two models model.1<-lmer(Response ~ Treatment + (1|Trial), data=exp.data, method="ML") model.2<-lmer(Response ~ 1 + (1|Trial), data=exp.data, method="ML") When I attempt to update

Dear useRs, I am fitting a mixed model using the function lmer from the package lme4, but I have some problems when I try to test the effect of my factors of interest. First let me explain the structure of the model: I'm measuring animal movements. Explicitly, I am interested in displacement (straight-line distance from an initial point). Displacements are measured longitudinally, with one

I'm migrating to lmer() from lme(). I have noticed that AIC values from lmer() are 2 units smaller than those reported by lme(). Could someone please explain why? The issue can be replicated with the first example from Pinheiro & Bates (2000) Mixed-effects models in S and S-plus. library(nlme) Rail2 <- Rail summary(lme(travel~1,data=Rail2,random=~1|Rail)) # AIC = 128.177

Hello R-help, I'm studying an example in the R book.? The data file is available from the link below.http://www.bio.ic.ac.uk/research/mjcraw/therbook/data/fertilizer.txt Could you explain Why the results from lme() and lmer() are different in the following case? In other examples, I can get the same results using the two functions, but not here...? Thank you.Miya library(lme4)library(nlme)#

Hi, I asked this on mixed model mailing list, but that list is not very active, so I'd like to try the general R mailing list. Sorry if anyone receives the double post. Hi, I have a dataset of animals receiving some eye treatments. There are 8 treatments, each animal's right and left eye was measured with some scores (ranging from 0 to 7) 4 times after treatment. So there are

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 know it's conventional to report bugs to the maintainer, but I'm not sure which package actually contains this bug(s), so I apologize for sending this to the list at large. I see the bug under both R 2.1.1, and R 2.2.1. (I sent a related message a while ago, but this one has more detail.) library(MASS) library(nlme) fit.model <- function(il, model.family) { cs <-

Hi, this is not a new doubt, but is a doubt that I cant find a good response. Look this output: > m.lme <- lme(Yvar~Xvar,random=~1|Plot1/Plot2/Plot3) > anova(m.lme) numDF denDF F-value p-value (Intercept) 1 860 210.2457 <.0001 Xvar 1 2 1.2352 0.3821 > summary(m.lme) Linear mixed-effects model fit by REML Data: NULL AIC BIC

Hi, I performed an experiment where I raised different families coming from two different source populations, where each family was split up into a different treatments. After the experiment I measured several traits on each individual. To test for an effect of either treatment or source as well as their interaction, I used a linear mixed effect model with family as random factor, i.e.

Dear Colleagues, I run this model: mod1 <- lmer(x~category+subcomp+category*subcomp+(1|id),data=impchiefsrm) obtain this summary result: Linear mixed-effects model fit by REML Formula: x ~ category + subcomp + category * subcomp + (1 | id) Data: impchiefsrm AIC BIC logLik MLdeviance REMLdeviance 4102 4670 -1954 3665 3908 Random effects: Groups Name Variance

I'm running lmer repeatedly on artificial data with two fixed factors (called 'gender' and 'stress') and one random factor ('speaker'). Gender is a between-speaker variable, stress is a within-speaker variable, if that matters. Each dataset has 100 rows from each of 20 speakers, 2000 rows in all. About 5% of the time I get a strange result, where the lmer() model with

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 +

Hi, all, I have a question about random effects model. I am dealing with a three-factor experiment dataset. The response variable y is modeled against three factors: Samples, Operators, and Runs. The experimental design is as follow: 4 samples were randomly chosen from a large pool of test samples. Each of the 4 samples was analyzed by 4 operators, randomly selected from a group of

list, i am using lmer to fit multilevel models and trying to use anova to compare the models. however, whenever i run the anova, the AIC, BIC and loglik are different from the original model output- as below. can someone help me out with why this is happening? (i'm hoping the output assocaited with the anova is right!). thank you, darren > unconditional<-lmer(log50 ~ 1 + (1 |

Dear R Helpers, I have noticed that when I use lmer to analyse data, the summary function gives different values for the AIC, BIC and log-likelihood compared with the anova function. Here is a sample program #make some data set.seed(1); datx=data.frame(array(runif(720),c(240,3),dimnames=list(NULL,c('x1','x2','y' )))) id=rep(1:120,2); datx=cbind(id,datx) #give x1 a

I need to extract the degrees of freedom and log likelihoods from a series of mixed models computed using lmer/lme4. If I ask for logLik (lmer.object), I get something like > logLik(lmer.object) 'log Lik.' -177.1000 (df=10) Can I easily get that df from there (or elsewhere) into an object? Thank you for any ideas. George

Hey, all. I had a quick question about fitting new glm values and then looking at the error around them. I'm working with a glm using a Gamma distribution and a log link with two types of treatments. However, when I then look at the predicted values for each category, I find for the one that is close to 0, the error (using se.fit=T with predicted) actually makes it overlap 0.

Hello all, Is AIC calculated incorrectly in lmer? It appears as though it uses AIC = -2*logLik - 2*#parms, instead of -2*LogLik + 2*#parms? Below is output from one of many models I have tried: Generalized linear mixed model fit using PQL Formula: cswa ~ pcov.ess1k + (1 | year) Data: ptct50.5 Family: poisson(log link) AIC BIC logLik deviance 224.8466 219.19 -114.4233 228.8466

Dear all, I try to consider overdispersion in a lmer model. But using family=quasibinomial rather than family=binomial seems to change the fit but not the result of an anova test. In addition if we specify test="F" as it is recomanded for glm using quasibinomial, the test remains a Chisq test. Are all tests scaled for dispersion, or none? Why is there a difference between glm and lmer