similar to: plotting glmmPQL function

Hello, I have a problem concerning estimation of GLMM. I used methods from 3 different packages (see program). I would expect similar results for glmm and glmmML. The result differ in the estimated standard errors, however. I compared the results to MASS, 4th ed., p. 297. The results from glmmML resemble the given result for 'Numerical integration', but glmm output differs. For the

Dear all, I have tried to calculate a GLMM fit with lmer (lme4) and glmmPQL (MASS), I also used glm for comparison. I am getting very different results from different functions, and I suspect that the problem is with our dataset rather than the functions, but I would appreciate help in deciding whether my suspicions are right. If indeed we are attempting the wrong type of analysis, some

Is the labeling/naming of levels in the documentation for the predict.glmmPQL function "backwards"? The documentation states "Level values increase from outermost to innermost grouping, with level zero corresponding to the population predictions". Taking the sample in the documentation: fit <- glmmPQL(y ~ trt + I(week > 2), random = ~1 | ID, family =

Dear Rusers, I have used R,S-PLUS and SAS to analyze the sample data "bacteria" in MASS package. Their results are listed below. I have three questions, anybody can give me possible answers? Q1:From the results, we see that R get 'NAs'for AIC,BIC and logLik, while S-PLUS8.0 gave the exact values for them. Why? I had thought that R should give the same results as SPLUS here.

I've got a colleague who's using a GLMM to analyse her data, and I've told her that she needs to include an offset. However, glmmPQL doesn't seem to allow one to be included. Is there anyway of doing this? Bob -- Bob O'Hara Rolf Nevanlinna Institute P.O. Box 4 (Yliopistonkatu 5) FIN-00014 University of Helsinki Finland Telephone: +358-9-191 23743 Mobile: +358 50 599

Hello, I am using the R function glmmPQL to fit a logistic GLMM, with weights. I am finding that I get fairly different parameter estimates in glmmPQL from fitting the full dataset (with no "weight" statement) and an equivalent, shorter dataset with the weights statement. I am using the weights statement in the 'glmmPQL' function exactly as in the 'glm' function. I

Dear listers, glmmPQL (package MASS) is given to work by repeated call to lme. In the classical outputs glmmPQL the Variance Structure is given as " fixed weights, Formula: ~invwt". The script shows that the function varFixed() is used, though the place where 'invwt' is defined remains unclear to me. I wonder if there is an easy way to specify another variance

Dear all, I am experiencing problems with glmmmPQL. I am trying to analyze binomial data with some spatial autocorrelation. Here is my code and some of the outputs > colnames(d.glmm) [1] "BV" "Longitude" "Latitude" "nb_pc_02" "nb_expr_02" [6] "pc_02" "nb_pc_07" "nb_expr_07"

Dear R community, I performed a generalized linear mixed model using glmmPQL (MASS library) to analyse my data i.e : y is the response with a poisson distribution, t and Trait are the independent variables which are continuous and categorical (3 categories C, M and F) respectively, ind is the random variable. mydata<-glmmPQL(y~t+Trait,random=~1|ind,family=poisson,data=tab) Do you think it

Dear R-users, I have conducted an experiment with a 2*2*2 factorial within-subjects design. All factors are binary and the dependent measure is a frequency of successes between 0 and 4. Treating this as a normally distributed variable, I would perform a repeated-measures ANOVA as follows: > aov(y ~ A*B*C + Error(subj/(A+B+C))) but since the distribution of the dependent measure is clearly

I have a longitudinal data analysis project. There are 10 observations on each of 15 units, and I'm estimating this with randomly varying intercepts along with an AR1 correction for the error terms within units. There is no correlation across units. Blundering around in R for a long time, I found that for linear/gaussian models, I can use either the MASS method glmmPQL (thanks to

This is a summary and extension of the thread "GLMM (lme4) vs. glmmPQL output" http://maths.newcastle.edu.au/~rking/R/help/04/01/0180.html In the new revision (#Version: 0.4-7) of lme4 the standard errors are close to those of the 4 other methods. Thanks to Douglas Bates, Saikat DebRoy for the revision, and to G?ran Brostr?m who run a simulation. In response to my first posting, Prof.

I have been looking into both of these approaches to conducting a GLMM, and want to make sure I understand model specification in each. In particular - after looking at Bates' Rnews article and searching through the help archives, I am unclear on the specification of nested factors in lmer. Do the following statements specify the same mode within each approach? m1 = glmmPQL(RICH ~ ZONE,

Hello All, Specifying 'type = "response"' when using predict() on a model fit using glm(...,family="binomial") returns fitted probabilities. Is it possible to get the same from a model object fit using glmmPQL() ? Thanks in advance, Rob _____________________________________________________ Rob Keefe Lab: (208) 885-5165 M.S. student

Hi! Can anyone let me know how can I get the stdDev of the random intercept from the output of glmmPQL? Thanks Salom?

Hi, I'm trying to perform a power simulation for a simple multilevel model, using the function glmmPQL in R version 2.8.1. I want to extract the p-value for the fixed-effects portion of the regression, but I'm having trouble doing that. I can extract the coefficients (summary(fit)$coeff), and the covariance matrix (summary(fit)$varFix), but I can't grab the p-value (or the

I use model comparison with glms without mixed effects with anova(modelA,modelB), with mixed effects glm (glmmPQL), this doesn't work. Is there a way to compare model fits with glmmPQL's? Paula M. den Hartog Behavioural Biology Institute of Biology Leiden Leiden University [[alternative HTML version deleted]]

Hi, My name is Jos?? Mar??a G??mez, and I am pretty new in R. Thus, I apologize deeply if my questions are extremmely na??ve.I have checked several available books and URL's, without finding any answer. I'm trying to fit Generalized Linear Mixed Models via PQL. Below I provide the structure of my data set. Year and Plot are random variables. Fate is the binomial dependent. I have severe

I am looking a risk factors for disease in cattle and am interested in modelling farm and sampling cluster as random effects (My outcome is positive or negative at the level of the farm). I am using R version 2.0.1 on a Mac and have identified glmmPQL as hopefully the correct function to use. I have run a couple of models using this but was hoping that you might be able to answer a few questions.

Dear All, I would like to analyse the residuals from a generalized linear mixed model (GLMM) that I estimated, with random effects, by means of the command glmmPQL, from the MASS package. It is not very clear to me what the actual residuals to analyse are (Y - Yhat): I obtain two columns of residuals, of which the first are population residuals, and the second refer to the grouping used in the