similar to: glmmPQL in 2.3.1

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

Samba Folks: I'm having what seems like a relatively benign issue with Samba 3.0.2a. I'm running SuSE 9.0 (with all updates) on an Athlon XP, 512MB RAM, RieserFS on SATA hard drives. Linux kernel version 2.4.21-202-athlon (from SuSE). I can repeatedly and predictably get the following error in the log.smbd file when connecting via Mac OS X. (smb://serverIP/) I get the same error

Dear R-help, Can someone please help me discover what function or code will give me a p-value from the input: 1) R-square statistic from a simple linear regression, and 2) sample size, n This would be greatly appreciated. I need this because I am using a database that gives me R-square and sample size for multiple comparisons, and I wish to determine the false discovery rate using q-value.

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

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

>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

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 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

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 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)

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, +

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 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

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,

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.

Hi, I am trying to use glmmPQL package for Generalized linear mixed models. This package works by repeated calls to lme. lme uses by default REML method for estimation. Then, does glmmmPQL use REML too? In contrast, how can I change it? I have tried it, writing : method="REML", but the program says: invalid method REML. If somebody can answer me....thanks, Sonja

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

I'm a bit puzzled by how to write out additive random effects in glmmPQL. In my situation, I have a factorial design on two (categorical) random factors, A and B. At each combination, I have a binary response, y, and two binary fixed covariates, C and D. If everything were fixed, I would use glm(y ~ A + B + C + D, family = binomial) My first thought was to use glmmPQL(y ~ A + B, random

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

Hello R users, I am trying to run a model with a binary response variable (nesting success: 0 failure, 1 success) and 8 fixed terms. Nesting success was examined in 72 cases in 34 territories (TER) during a 6 study years. Territories are nested within 14 patches (PATCH). I want to run a model taking into account these nested factors and repeated observation. To do this, I assume that the best