similar to: extracting residual variance from glmmPQL

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 users, I want to explain binomial data by a serie of fixed effects. My problem is that my binomial data are spatially correlated. Naively, I thought I could found something similar to gls to analyze such data. After some reading, I decided that lmer is probably to tool I need. The model I want to fit would look like lmer ( cbind(n.success,n.failure) ~ (x1 + x2 + ... + xn)^2 ,

Dear all, I am starting to use the lme package (and plan to teach a course based on it next semester...). To understand what lme is doing precisely, I used balanced datasets described in Pinheiro and Bates and tried to compare the lme outputs to that of aov. Here is what I obtained: > data(Machines) > summary(aov(score~Machine+Error(Worker/Machine),data=Machines)) Error: Worker

Dear R-helpers, I'd like to use glmmPQL to predict binary responses based on a data.frame data1 containing N entries (N<1000): target covariate1 covariate2 covariate3 ... covariateM cluster 134131 1 -0.30031885 0 0 -2.886870e-07 1 38370 1 -0.04883229 0 1 -1.105720e-07 1 19315 1 -0.11084267

[I think I've seen this reported before but can't locate it any more. I believe this oddity (glitch? feature?) is behind a query that Jean-Baptiste Ferdy asked a year ago <http://finzi.psych.upenn.edu/Rhelp08/2008-October/176449.html>] It appears that glmmPQL looks in the global workspace, not within the data frame specified by the "data" argument, for the variables

Hi all, I've been attempting to fit a logistic glmm using glmmPQL in order to estimate variance components for a score test, where the model is of the form logit(mu) = X*a+ Z1*b1 + Z2*b2. Z1 and Z2 are actually reduced rank square root matrices of the assumed covariance structure (up to a constant) of random effects c1 and c2, respectively, such that b1 ~ N(0,sig.1^2*I) and c1 ~

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 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

Ape is an R package for "analyses of phylogenetics and evolution". The first version (0.1) has been released on 27 August 2002 and is available on CRAN. >From the 'Description' file of version 0.1: Ape provides functions for reading, and plotting phylogenetic trees in parenthetic format (standard Newick format), analyses of comparative data in a

Hello R users- I am new to R, and tried searching the archives and literature for an answer to this - please be patient if I missed something obvious. I am fitting a logistic regression model, and would like to include variance functions (specifically the varIdent function). I cannot figure out how to do this either in glmmPQL (or something similar) for the model with random effects, or in glm

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 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

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

Dear R users, I have been using the pdMat class "pdIdnot" (from the mgcv package)instead of "pdIdent" to avoid overflow in GLMM fits with the MASS package function glmmPQL, of the following form: fit1 <- glmmPQL(fixed=y0~-1+xx0, random=list(gp=pdIdent(~-1+zz0)), family=binomial) # vulnerable to overflow fit2 <- glmmPQL(fixed=y0~-1+xx0,

Hi ! I try to compare some models obtained from glmmPQL. model1 <- glmmPQL(y~red*yellow+I(red^2)+I(yellow^2)+densite8+I(densite8^2)+freq8_4 +I(freq8_4^2), random=~1|num, binomial); model2 <- glmmPQL(y~red*yellow+I(red^2)+I(yellow^2)+densite8+I(densite8^2)+freq8_4 , random=~1|num, binomial); anova(model1, model2) here is the answer : Erreur dans anova.lme(model1, model2) : Objects must

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

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

Hi everybody, recently I had to teach a course on Cox model, of which I am not a specialist, to an audience of medical epidemiologists. Not a good idea you might say.. anyway, someone in the audience was very hostile. At some point, he sayed that Cox model was useless, since all you have to do is count who dies and who survives, divide by the sample sizes and compute a relative risk, and if there

Dear all, for a cross-validation I have to use predict.glmmPQL() , where the formula of the corresponding glmmPQL call is not given explicitly, but constructed using as.formula. However, this does not work as expected: x1<-rnorm(100); x2<-rbinom(100,3,0.5); y<-rpois(100,2) mydata<-data.frame(x1,x2,y) library(MASS) # works as expected model1<-glmmPQL(y~x1, ~1 | factor(x2),

Hi all, I try to make a split-plot with poisson errors using glmmPQL, but I have some doubts about the model simplification. Look my system: Block = 3 blocks Xvar1 = 2 levels Xvar2 = 13 levels Yvar = Count data Response I need know about the behaviour of Var1, Var2 and interaction Var1:Var2. Look the levels: > levels(Xvar1) [1] "A" "B" > levels(Xvar2) [1]