similar to: Error using glmmPQL

Greetings- Using R 1.5.0 under linux and the latest MASS and nlme, I am trying to develop a three-level (two levels of nesting) model with a dichotomous oucome variable. The unconditional model is thus: > doubt1.pql<-glmmPQL(fixed = r.info.doubt ~ 1, random = ~1 | groupid/participantid, + family = binomial, data = fgdata.10statements.df) iteration 1 iteration 2 iteration 3 iteration 4

Greetings. I'm running some models under R using glmmPQL from MASS. These are three-level models (two grouped levels and the individual level) with dichotomous outcomes. There are several statistics of interest; for the moment, I have two specific questions: 1.) This question refers to the following model (I present first the call, then the output of summary():

For an analysis of data from twins, I'd like to do some rearranging in my dataframe. Currently, each twin has his or her own row. I'd like to add several columns of the co-twin's data to each row. In other words, this is the current situation: FamilyID ParticipantID IQ Digit_span 1 1 95 6 1 2 93 7 2 3 102 8 2

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

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

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'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"),

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 <-

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

Is anyone using R with postgres? I'd like to do so, but cannot seem to find any reasonable explanation of how to do so. Joshua Gramlich Chicago, IL

Hi - I am using R version 2.13.0. I have run several GLMMs using the glmmPQL function to model the proportion of fish caught in one net to the total caught in both nets by length. I started with a polynomial regression full model with three length terms: l, l^2, and l^3 (l=length). The length terms and intercept were the fixed effects and the random effect was a paired haul (n=18).

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

Hi, I'm having trouble with glmmPQL from the MASS package. I'm trying to fit a model with a binary response variable, two fixed and two random variables (nested), with a sample of about 200,000 data points. Unfortunately, I'm getting an error message that is difficult to understand without knowing the internals of the glmmPQL function. > model <- glmmPQL(primed ~

Greetings- I need to extract, programatically, the standard deviations of the intercept and residuals from an lme model. These are presented by print.lme as: ... (Intercept) Residual StdDev: 1.410635 0.7800512 ... (data taken from ?lme's examples section) I can get the residuals with x$sigma where x is the fitted lme object. I can't find the intercept, though. The closest

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

Greetings- I'm working with data that are multilevel in nature and have a dichotomous outcome variable (presence or absence of an attribute). As far as I can tell from reading archives of the R and S lists, as well as Pinheiro and Bates and Venables and Ripley, - nlme does not have the facility to do what amounts to a mixed-effects logistic regression. - The canonical alternative is

After upgrading to 1.7.0 under debian linux, I can't get e1071 working properly. The first problem I had was that g++-3.0 was the standard compiler but wasn't installed, so I installed it. e1071 then installed correctly, but I get the following: aperrin at perrin:~/afshome/papers/authoritarian/R$ R R : Copyright 2003, The R Development Core Team Version 1.7.0 (2003-04-16) R is free

Returning after the long weekend, I get the following: aperrin at perrin:~/afshome/papers/microcultures/R$ R R : Copyright 2003, The R Development Core Team Version 1.7.0 (2003-04-16) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type `license()' or `licence()' for distribution details. R is a collaborative