similar to: glmmPQL in 2.3.1

Dear R-help, I recently tried implementing glmmPQL in 2.3.1, and I discovered a few differences as compared to 2.2.1. I am fitting a regression with fixed and random effects with Gamma error structure. First, 2.3.1 gives different estimates than 2.2.1, and 2.3.1, takes more iterations to converge. Second, when I try using the anova function it says, "'anova' is not available

Greetings- Using R 1.5.0 under linux and the latest MASS and nlme, I am trying to develop a three-level (two levels of nesting) model with a dichotomous oucome variable. The unconditional model is thus: > doubt1.pql<-glmmPQL(fixed = r.info.doubt ~ 1, random = ~1 | groupid/participantid, + family = binomial, data = fgdata.10statements.df) iteration 1 iteration 2 iteration 3 iteration 4

Greetings- a reviewer for a paper of mine noted an anomaly in some models I ran using glmmPQL (from the MASS package). Specifically, the models are three-level hierarchical probit models estimated using PQL under R. The anomaly is that the log-likelihoods decrease (or, alternatively -2logLik increases) as variables are added to the null model. This is unusual, and I'm trying to figure out

>On Wed, 3 Aug 2005, Bernd Weiss wrote: > >> I am trying to replicate some multilevel models with binary outcomes >> using R's "lmer" and "glmmPQL" and Stata's gllmm, respectively. > >That's not going to happen as they are not using the same criteria. the glmmPQL and lmer both use the PQL method to do it ,so can we get the same result by

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.

Can anyone shed any light on this? > doubt.demographic.pql<-glmmPQL(random = ~ 1 | groupid/participantid, + fixed = r.info.doubt ~ + realage + minority + female + education + income + scenario, + data = fgdata.df[coded.resource,], + na.action=na.omit, +

Hello, Might anyone know how to conduct Wald-type F-tests of the fixed effects estimated by glmmPQL? I see this implemented in SAS (GLIMMIX), and have seen it recommended in user group discussions, but haven't come across any code to accomplish it. I understand the anova function treats a glmmPQL fit as an lme fit, with the test assumptions based on maximum likelihood, which is inappropriate

Greetings. I'm running some models under R using glmmPQL from MASS. These are three-level models (two grouped levels and the individual level) with dichotomous outcomes. There are several statistics of interest; for the moment, I have two specific questions: 1.) This question refers to the following model (I present first the call, then the output of summary():

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

Dear R HELP, ABOUT glmmPQL and the anova command. Here is an example of a repeated-measures ANOVA focussing on the way starling masses vary according to (i) roost situation and (ii) time (two time points only). library(nlme);library(MASS)

Dear All Hopefully someone out there can point out what I am missing! I have a (large, several hundred) dataset of gardens in which over two years the presence/absence of a particular bird species is noted each week. I have good reason to believe there is a difference between the two years in the weekly proportion of gardens and would like to assess this, before going on to look in more detail at

Dear all, I have two questions concerning model simplification in GlmmPQL, for for random and fixed effects: 1. Fixed effects: I don't know if I can simply specify anova(model) and trust the table that comes up with the p value for each variable in the fixed effects formula. I have read that the only way to test for fixed effects is to do approximate wald tests based on the standard errors

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'm running into some computer issues when trying to run a binomial model for spatially correlated data using glmmPQL and was wondering if anyone could help me out. My whole dataset consists of about 300,000 points for which I have a suite of environmental variables (I'm trying to come up with a habitat model for a species of seal, using real (presence) and simulated dives

Dear R-listers, I am working with generalized linear mixed models to quantify the variance due to two nested random factors, but have hit a snag in the interpretation of variance components. Despite my best efforts with Venables & Ripley 2002, Fahrmeir & Tutz 2001, R-help archives, Google, and other eminent sources (i.e. local R gurus), I have not been able to find a definitive answer

Dear Wolfgang Viechtbauer and list members: I have discovered your "MiMa" function for fitting meta-analytic mixed-effects models through an earlier discussion on this list. I think it is extremely useful and fills an important gap. In particular, since it is programmed so transparently, it is easy to adapt it for one's own needs. (For example, I have found it easy to identify

I am analysing some binary data with a mixed effects model using glmmPQL. I am aware that I cannot use the AIC values to help me find the minimum adequate model so how do I perform likelihood ratio tests? I need to fix on the minimum adequate model but I'm not sure of the proper way to do this. Thank you very much, Elizabeth Boakes Elizabeth Boakes PhD Student Institute of Zoology

Dear list members, In re-running with GLMM() from the lme4 package a generalized-linear mixed model that I had previously fit with glmmPQL() from MASS, I'm getting a warning of a convergence failure, even when I set the method argument of GLMM() to "PQL": > bang.mod.1 <- glmmPQL(contraception ~ as.factor(children) + cage + urban, + random=~as.factor(children) + cage +

Dear R users, can anybody explain me why the function glmmPQL(.) behaves in different ways, depending on the number of measurements/individuals you use? To show you this, I generated two examples. The first one includes 20 indivduals with each 100 repeated measurements (binary response), the second one includes 40 individuals. The 'individuals' differ only in different x values. I