similar to: glmmPQL, log-likelihoods issue

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, +

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

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

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

>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

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

Greetings- I'm working with data that are multilevel in nature and have a dichotomous outcome variable (presence or absence of an attribute). As far as I can tell from reading archives of the R and S lists, as well as Pinheiro and Bates and Venables and Ripley, - nlme does not have the facility to do what amounts to a mixed-effects logistic regression. - The canonical alternative is

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():

Hope one of you could help with the following question/problem: We would like to explain the spatial distribution of juvenile fish. We have 2135 records, from 75 vessels (code_tripnr) and 7 to 39 observations for each vessel, hence the random effect for code_tripnr. The offset (‘offsetter’) accounts for the haul duration and sub sampling factor. There are no extreme outliers in lat/lon. The model

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

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

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)

> library(MASS) > attach(bacteria) > table(y) y n y 43 177 > y<-1*(y=="y") > table(y,trt) trt y placebo drug drug+ 0 12 18 13 1 84 44 49 > library(lme4) > model1<-lmer(y~trt+(week|ID),family=binomial,method="PQL") Error in match.arg(method, c("Laplace", "AGQ")) : 'arg' should be one of

I am analysing binomial data using a generalised mixed effects model. I understand that if I use glmmPQL it is not appropriate to compare AIC values to obtain a minimum adequate model. I am assuming that this means it is also inappropriate to use AIC values from lmer since, when analysing binomial data, lmer also uses PQL methods. However, I wasn't sure so please could somebody clarify

Dear friends, I have asked last few days about cross-random effects using PQL, but I have not receive any answer because might my question was not clear. My question was about analysing the salamander mating data using PQL. This data contain cross-random effects for (male) and for (female). By opining MASS and lme library. I wrote this code sala.glmm <- glmmPQL(fixed=y~WSf*WSM,

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 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 +

I have come across the previous communication on this list in September (copied below) because I had received the same error message. I understand from Brian Ripley's reply that anova should not be used with glmmPQL because it is not an adequate method, and that this is now shown with an error message. My question is, what method *should* be used? Using summary does not give me the result

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