similar to: glmmPQL error in logLik.reStruct

Dear all, I'm using the correlogram function in the spatial library to calculate spatial correlograms of radar data. However, I'm finding that the resulting values are often outside the range [-1,1], usually only at larger distances of separation. I'm not sure whether to be overly concerned about this, or dismiss it as some artefact of the data. Has anyone had similar experiences?

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)

I'm getting a strange error from glmmPQL. Consider the following sample code: set.seed(8) N. <- 1000 z <- rnorm(N.) pr.good <- exp(-1e-4*exp(2+2*z)) quantile(pr.good) DF. <- data.frame(yield=rbinom(N., N., pr.good)/N., Offset=rep(-10, N.), nest=1:N.) fit <- glmmPQL(fixed=1-yield~offset(Offset), random=~1|nest, family=binomial(link="cloglog"),

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

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 <-

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-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

Hello, I am trying to design new variance structures by using fixed effects variables in combination with the VarPower function. That is, I would like to create and evaluate my own variance function in the data frame and then incorporate it into the model using varPower, with value=.5. As a start, I am trying to recreate the function of VarConstPower by introducing two new variables in the

Sorry if this is obvious, but my S skills aren't great and I haven't been able to find it documented anywhere. I want to write a new function for use with lme objects; the function will simply calculate an ICC (aka "rho") for each level of a mixed-effects model. What I need for this is pretty simple: (c(var1..varn, residual)) / sum(c(var1..varn, residual)) where var1..varn

Dear all, I am estimating a mixed-model in Ubuntu Raring (13.04¸ amd64), with the code: fm0 <- lme(rt ~ run + group * stim * cond, random=list( subj=pdSymm(~ 1 + run), subj=pdSymm(~ 0 + stim)), data=mydat1) When I check the approximate variance-covariance matrix, I get: > fm0$apVar [1] "Non-positive definite

Hi I have found some postings referring to the fact that one can try and understand why a particular model is failing to solve/converge from the verbose output one can generate when fitting a nonlinear mixed model. I am trying to understand this output and have not been able to find out much: **Iteration 1 LME step: Loglik: -237.4517 , nlm iterations: 22 reStruct parameters: subjectno1

Hi folks, I am tearing my hair out on this one. I am using an example from Pinheiro and Bates. ### this works data(Orange) mod.lis <- nlsList(circumference ~ SSlogis(age, Asymp, xmid, scal), data=Orange ) ### This works mod <- nlme(circumference ~ SSlogis(age, Asymp, xmid, scal), data=Orange, fixed = Asymp + xmid + scal ~ 1, start =

Hi, lme() users, Can some one tell me how to do this. I model Orthodont with the same G for random variables, but different R{i}'s for boys and girls, so that I can get sigma1_square_hat for boys and sigma2_square_hat for girls. The model is Y{i}=X{i}beta + Z{i}b + e{i} b ~ iid N(0,G) and e{i} ~ iid N(0,R{i}) i=1,2 orth.lme <- lme(distance ~ Sex * age, data=Orthodont, random=~age|Subject,

Dear all, Here is the question: For example, using the "petrol" data offered with R. pet3.lme<-lme(Y~SG+VP+V10+EP,random=~1|No,data=petrol) pet3.lme$sigma gives the residual StdDev. But I can't figure out how to extract the "(intercept) StdDev", although it is in the print out if I do "summary(pet3.lme)". In

I'm getting an error from nlme that has me stymied. I have a data set ,'mydata', with variables: AChE, Dose, sex, set, and mrid; 'set' and 'mrid' indicate two levels of nesting, with 'set' nested within 'mrid'. I want to fit the model: mod <- nlme(AChE ~ Cexp(Dose, A, B, m), data=mydata, fixed = A+B+M~sex, random=A+B+m~sex | mrid/set,

My apologies if this is obvious: Is there a simple way (other than simulation or bootstrapping) to obtain a (approximate)confidence interval for the ratio of 2 variance components in a fitted lme model? -- In particular, if there are only 2 components (1 grouping factor). I'm using nlme but lme4 would be fine, too. -- Bert Gunter Genentech Non-Clinical Statistics South San Francisco, CA

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