similar to: degrees of freedom and random effects in lmer

To whom it may concern: I am using R version 1.9.1 beta on a windows machine, and I am trying to use a function called lmer (in packages lme4 and Matrix). I am not sure if this version will support these packages (when I install them from the GUI, the function lmer does not work). I wondered if I need to install a newer version of R, and if so, which version should I install, and how should I

To whom it may concern: I am using lmer for a statistical model that includes non-normally distributed data and random effects. I used this same function in the most recent version of R as of fall 2005, and have re-done some of the same analyses using all of the same files, but with the newest version of R (2.2.1). I get answers that are not exactly the same (although I do get the same

To whom it may concern: I recently used lmer (for non-normally distributed data and mixed effects, using the Laplace method). All 3 of my fixed effects were categorical, including two ordered factors and one unordered factor. In my tables, I currently report the number of observations for the response variable, and both the degrees of freedom and Chi Square values from tests of reduced

To whom it may concern: I have a question about how to appropriately conduct an lmer analysis for negative binomially distributed data. I am using R 2.2.1 on a windows machine. I am trying to conduct an analysis using lmer (for non-normally distributed data and both random and fixed effects) for negative binomially distributed data. To do this, I have been using maximum likelihood,

Hi , Could someone pls help me about this topic, I dont know how can i extract them from my model!! Thanks, Sophie -- View this message in context: http://r.789695.n4.nabble.com/degrees-of-freedom-does-not-appear-in-the-summary-lmer-tp3741327p3741327.html Sent from the R help mailing list archive at Nabble.com.

Dear all, I'm curious about how to interpret the results of the following code. The first model is directly from the help page of lmer; the second is the same model but using the Gamma family with log link. The fixed effects make sense, because y = 251.40510 + 10.46729 * Days is about the same as log(y) = 5.53613298 + 0.03502057 * Days but the random effects seem quite

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

Hello all, I'm trying to do a nested linear model with a dataset that incorporates an observation for each of several classes within each of several plots. I have 219 plots, and 17 classes within each plot. data.frame has columns "plot","class","age","dep.var" With lm(dep.var~class*age), The summary(lm) function returns t-test and F-test values

Hello R-users: Could you please tell me how can I extract the "df (degree of freedom)" and "estfun (estimating functions)" for the following lmer (or lmer2) model? wtd.mixed<-lmer(ddimer~race+steroid+psi+sofa+apache + (1|subject), method="ML", data=final, cluster="id", weights=w) I tried the following codes: - for the degree of freedom (erorr

Dear all, I am performing a binomial glmm analysis using the lmer function in the lme4 package (last release, just downloaded). I am using the "Laplace method". However, I am not sure about what I should do to test for the significance of fixed effects in the binomial case: Is it correct to test a full model against a model from which I remove the fixed effect I want to test

Hello, I have a little problem about degree of freedom in R. if you can help me, I will be happy. I used nlme?function to analyze my data and run the linear mixed effects model in R. I did the linear mixed effect analysis in SAS?and SPSS as well. However, R gave?the different degrees of freedom than SAS?and SPSS did. Can you help me to learn what the reason is to obtain different degrees of

I wonder whether any of you know of an efficient way to calculate the approximate degrees of freedom of a gamm() fit. Calculating the smoother/projection matrix S: y -> \hat y and then its trace by sum(eigen(S))$values is what I've been doing so far- but I was hoping there might be a more efficient way than doing the spectral decomposition of an NxN-matrix. The degrees of freedom

I am fitting models to the responses to a questionnaire that has seven yes/no questions (Item). For each combination of Subject and Item, the variable Response is coded as 0 or 1. I want to include random effects for both Subject and Item. While I understand that the datasets are fairly small, and there are a lot of invariant subjects, I do not understand something that is happening here, and in

Greetings, I am performing a simple Pearson's correlation test. Length of both vectors is 40, therefore the resulting df is 38. Nevertheless, a colleague is asking me for the "effective degrees of freedom". As far as I understand, those degrees of freedom have to be estimated for more complex regressions, but I was not able to find detailed information about it. Does any one of

Dear all, I am starting to use the lme package (and plan to teach a course based on it next semester...). To understand what lme is doing precisely, I used balanced datasets described in Pinheiro and Bates and tried to compare the lme outputs to that of aov. Here is what I obtained: > data(Machines) > summary(aov(score~Machine+Error(Worker/Machine),data=Machines)) Error: Worker

Dear Dr. Bates, Thank you very much for your response. I had consulted the algorithm described in Pinheiro and Bates. However, what I don't understand (among other things) is why my two parameters appear to be estimated at different grouping levels (based on the DF values). Affect this different values of DF at the estimates parameters? The estimates fixed effects were get at the same level of

Hello, I am struggling to understand how denominator degrees of freedom and subsequent significance testing based upon them works in nlme models. I have a data set of 736 measurements (weight), taken within 3 different age groups, on 497 individuals who fall into two morphological catagories (horn types). My model is: Y ~ weight + horn type / age group, random=~1|individual I am modeling

Dear all, I want to do a F test, which involves calculation of the degrees of freedom for the residuals. Now say, I have a nlme object "mod.nlme". I have two questions 1.How do I extract the degrees of freedom? 2.How is this degrees of freedom calculated in an nlme model? Thanks. Jun Shen Some sample code and data =================================================================

Hi, Is there a way to provide starting values for the fixed effects in lmer()? I'd like to fit the following model, which requires starting values in the glm.fit() part of the code: lmer(dbh.sum ~ Treatment + (1|Site), nets, gaussian("log"), subset=Treatment!="sforest" & iocTreat!="forest") I tried tinkering with the code but I couldn't figure out the

I really hope sombody could help me with the following, I'm having problems accessing the random effect samples following the example on MCMCsamp: (fm1 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy)) set.seed(101); samp0 <- mcmcsamp(fm1, n = 1000, saveb=TRUE) str(samp0) Formal class 'merMCMC' [package "lme4"] with 9 slots ..@ Gp :