similar to: Weights in glmmPQL

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 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

hello all, i'm an R newbie struggling a bit with the glmmPQL function (from the nlme pack). i think i've managed to run the model successfully, but can't seem to plot the resulting function. plot(glmmPQL(...)) plots the residuals rather than the model... i know this should be basic, but i can't seem to figure it out. many thanks in advance. j -- View this message in context:

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

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"

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

I fit the following model using glmmPQL from MASS: fit.glmmPQL <- glmmPQL(ifelse(class=="Disease",1,0)~age+x1+x2,random=~1|subject,family=binomial) summary(fit.glmmPQL) The response is paired (pairing denoted by subject), although some subjects only have one response. Also, there is a perfect positive correlation between the paired responses. x1 and x2 can and do differ within each

Dear R-Helpers, I have a matrix where the first column is known. The second column is the result of multiplying this first column with a constant "const". The third column is the result of multiplying the second column with "const"..... So far, I did it like this (as a simplified example): nr.of.columns <- 4 myconstant <- 27.5 mymatrix <- matrix(numeric(0), nrow=5,

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 R-users, I'm struggling in R in order to "squeeze" a matrix without using a for-loop. Although my case is a bit more complex, the following example should help you to understand what I would like to do, but without the slow for-loop. Thanks in advance, Carlo Giovanni Camarda A <- matrix(1:54, ncol=6) # my original matrix A.new <- matrix(nrow=3, ncol=6) # a new

Dear R users I have built several glmm models using glmmPQL in the following structure: m1<-glmmPQL(dev~env*har*treat+dens, random = ~1|pop/rep, family = Gamma) (full script below, data attached) I have tried all the methods I can find to obtain some sort of model fit score or to compare between models using following the deletion of terms (i.e. AIC, logLik, anova.lme(m1,m2)), but I

Dear R Community, I?m relatively new in the field of R and I hope someone of you can help me to solve my nerv-racking problem. For my Master thesis I collected some behavioral data of fish using acoustic telemetry. The aim of the study is to compare two different groups of fish (coded as 0 and 1 which should be the dependent variable) based on their swimming activity, habitat choice, etc.

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

I am using the 'glmmPQL function in the 'MASS' library to fit a mixed effects logistic regression model to simulated data. I am conducting a series of simulations, and with certain simulated datasets, estimation of the random effects logistic regression model unexpectedly terminates. I receive the following error message from R: Error in lme.formula(fixed=zz + arm.long,random=~1 |

Hi, > [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Corey Powell > > Do you know of any references that verify the accuracy of R > for basic statistical calculations and tests. The results of > these studies should indicate that R results are the same as > the results of other statistical packages to a certain number > of decimal places on some benchmark

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'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

Hi, I'm running a GLMM on binomial choice data. The outputs I receive are sensible except for the degrees of freedom, which come out much larger than expected. Can anyone advise please? Exptl design: Response = Choice Fixed Factors = Position, Treatment and Sex Random Factor = ID nested within Treatment and Sex Covariate = Delay The model: glmmPQL(FreeChoice ~ Position * Treatment + Sex