similar to: glmmPQL:

Dear R users, I'm attempting to fit a GLM with random effects using the tweedie family for the error structure. I'm getting the error: iteration 1 Error in logLik.reStruct(object, conLin) : NA/NaN/Inf in foreign function call (arg 3) I'm running V2.1.0 I notice from searching the lists that the same error was reported in May 2004 by Spencer Graves, but no-one was able to

I'm using glmmPQL, and I still have a few problems with it. In addition to the issue reported earlier, I'm getting the following error and I was wondering if there's something I can do about it. Error in logLik.reStruct(object, conLin) : Na/NaN/Inf in foreign function call (arg 3) ... Warnings: 1: Singular precistion matrix in level -1, block 4 (...) 4: "" The

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, +

Dear listers, First let me appologize if the same mail arrives multiple times. Recently I had some probelms sending my e-mails to the list. I encountered a problem when running glmmPQL procuedure doing multilevel modeling with a dichotomous outcome. Those are the two error messages I usually get: Error in logLik.reStruct(object, conLin) : NA/NaN/Inf in foreign function call (arg 3)

Dear all, I try to fit a glmmPQL on a huge data with 384189 individuals id=1:384189: working in 1520 establishments est:1:1516. The minimum number of individuals in every establishment is 30. This works for a subsample excluding establishemnet cells smaller than 100, but fail when we include smaller cells: R> summary(glmmPQL(count ~ + I( age-ave(age,est) )* ave(age,est) + + I(

I know it's conventional to report bugs to the maintainer, but I'm not sure which package actually contains this bug(s), so I apologize for sending this to the list at large. I see the bug under both R 2.1.1, and R 2.2.1. (I sent a related message a while ago, but this one has more detail.) library(MASS) library(nlme) fit.model <- function(il, model.family) { cs <-

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

I'm trying to use GLMM in library(lme4), R 1.9.0pat, updated just now. I get an error message I can't decipher: library(lme4) set.seed(1) n <- 10 N <- 1000 DF <- data.frame(yield=rbinom(n, N, .99)/N, nest=1:n) fit <- GLMM(yield~1, random=~1|nest, family=binomial, data=DF, weights=rep(N, n)) Error in eval(expr, envir, enclos) : ..1 used in an incorrect

Dear R-ers, I am having trouble understanding why I am getting an error using glmmPQL (library MASS). I am getting the following error: iteration 1 Error in MEEM(object, conLin, control$niterEM) : Singularity in backsolve at level 0, block 1 The long story: I have data from an experiment on pairwise comparisons between 3 treatments (a, b, c). So a typical run of an experiment

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,

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, 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),

Greetings R-ers, A colleague and I have been exploring the behaviour of glmmPQL in R and S-PLUS 6 and we appear to get different results using the same code and the same data set, which worries us. I have checked the behaviour in R 1.7.1 (MacOS 9.2) and R. 1.9.0 (Windows 2000) and the results are the same, but differ from S-PLUS 6 with the latest Mass and nlme libraries (Windows XP). Here

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

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 all, I am trying to simulate data sets from a model fitted with glmmPQL, in order to compute the distribution of a summary statistics. My data are binomial and I have a correlation term in my model. My model is structured in the following way m <- glmmPQL( fixed = cbind(sucess,failure) ~ x1 + x2 + ... , random = ~ 1 | bidon, correlation = corGaus(form=~

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 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

Hi, I have the following type of data: 86 subjects in three independent groups (high power vs low power vs control). Each subject solves 8 reasoning problems of two kinds: conflict problems and noconflict problems. I measure accuracy in solving the reasoning problems. To summarize: binary response, 1 within subject var (TYPE), 1 between subject var (POWER). I wanted to fit the following model:

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