similar to: LME without convergence

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 R-help list readers, I am fitting mixed models with the lme function of the nlme package. If I get convergence depends on how the method (ML/REM) and which (and how much) parameters will depend randomly on the cluster-variable. How get the bist fit without convergence? I set the parameters msVerbose and returnObject to TRUE: lmeControl(maxIter=50000, msMaxIter=200, tolerance=1e-4,

Hello, all, I am running some simulations to estimate power for a complicated epidemiological study, and am using lme and lmer to get these estimates. I have to run a few thousand iterations, and once in a great while, an iteration will create fake data such that the model won't converge. I see from Google searches that this is not an uncommon situation. My question: is there a way to

Dear all, Edmond Ng (http://multilevel.ioe.ac.uk/softrev/reviewsplus.pdf) provides an example to fit the mixed effects meta-analysis in Splus 6.2. The syntax is: lme(fixed=d~wks, data=meta, random=~1|study, weights=varFixed(~Vofd), control=lmeControl(sigma=1)) where d is the effect size, study is the study number, Vofd is the variance of the effect size and meta is the data frame.

Hi R-users, I'm attempting to fit a number of mixed models, all with the same structure, across a spatial grid with data points collected at various time points within each grid cell. I'm trying to use a 'for' loop to try the model fit on each grid cell. In some cells lme does not converge, giving me the error: Error message: In lme.formula(logarea ~ year + summ_d, data =

Dear R developers, is there a preferred format or strategy for making a patch to contribute to a package that is maintained by R-core? Berwin Turlach and I have written a very minor extension to lmeControl to allow it to pass an argument to nlminb for the maximum number of evaluations of the objective function. I've edited the nlme/R/lme.R and nlme/man/lmeControl.Rd files. I can diff the

Dear R-Users, Is it possible to get the covariance matrix from an lme model that did not converge ? I am doing a simulation which entails fitting linear mixed models, using a "for loop". Within each loop, i generate a new data set and analyze it using a mixed model. The loop stops When the "lme function" does not converge for a simulated dataset. I want to

Dear list members, In re-running with GLMM() from the lme4 package a generalized-linear mixed model that I had previously fit with glmmPQL() from MASS, I'm getting a warning of a convergence failure, even when I set the method argument of GLMM() to "PQL": > bang.mod.1 <- glmmPQL(contraception ~ as.factor(children) + cage + urban, + random=~as.factor(children) + cage +

I tried to use LME (on a fairly large dataset, so I am not including it), and I got this error message: Error in lme.formula(formula(paste(c(toString(TargetName), "as.factor(nodeInd)"), : nlminb problem, convergence error code = 1 message = false convergence (8) Is there any way to get more information or to get the potentially wrong estimates from LME? (Also, the page in the

Hi. I was wondering if anyone might have some suggestions about how I can overcome a problem of "iteration limit reached without convergence" when fitting a mixed effects model. In this study: Outcome is a measure of heart action Age is continuous (in weeks) Gender is Male or Female (0 or 1) Genotype is Wild type or knockout (0 or 1) Animal is the Animal ID as a factor

I am using R 2.7.0 on a linux platform. I am trying to reproduce a 2002 example using lme from the nlme library. I want to change the otimizer from the default (nlminb) to optim. Specifically, this is what I am trying to do: R> library(nlme) R> library(car) # for data only R> data(Blackmoor) # from car R> Blackmoor$log.exercise <- log(Blackmoor$exercise + 5/60, 2) R>

Dear all, We are trying to do a meta regression in R using the lme function. The reason for doing this with lme function is that we have covariates and studies within references. In S-Plus this is possible by using the following command: lme(outcome ~ covars, random = ~1 | reference/study, weights = varFixed(~var.outcome), data = mydata, control = lmeControl(sigma = 1)) This means that the

hello all, I am trying to use the function getCovariateFormula(nlme) in conjunction with the library lme4. When I load both packages I get the following message and the getCovariateFormula function no longer works: library(nlme) library(lme4) Attaching package 'lme4': The following object(s) are masked from package:nlme : contr.SAS getCovariateFormula

Hi, I am performing meta-regression using linear mixed-effect model with the lme() function that has two fixed effect variables;one as a log transformed variable (x) and one as factor (y) variable, and two nested random intercept terms. I want to save the predicted values from that model and show the log curve in a plot ; predicted~log(x) mod<-lme(B~log(x)+as.factor(y),

Hi, I would like to do a meta-analysis, i.e., a mixed-effects regression, but I don't seem to get what I want using both the nlme or metafor packages. My question: is there indeed no way to do it? And if so, is there another package I could use? Here are the details: In my meta-analysis I'm comparing different studies that report a measure at time zero and after a certain followup

I have a question about the lme function in R. My question is: After I got the object from function lme, why the numIter value of the object is always NULL? Following is my code: jjww<-lme(y~x*zz,data=simul,random=~x|group, control=lmeControl(returnObject=TRUE)) attributes(jjww) jjww$numIter the first 20 observation of data simul are: > simul y

Hi, I have a simple random-coefficients model for m subjects: y = b0 + b1 x + r0 + r1 x + e where b0 and b1 are fixed parameters, r0 and r1 are random, e ~ N(0,s2 I) and R' = [r0, r1] ~ N(0,T). I try to obtain the EM-estimates of s2 and the elements of T by lme(y~x,data=mydata,random= list(group=~x), control=lmeControl(maxIter = 0, niterEM=100,msVerbose = TRUE)) Does

Dear Alina If I understand you correctly you cannot just have a single predicted curve but one for each level of your factor. On 09/08/2017 16:24, Alina Vodonos Zilberg wrote: > Hi, > > I am performing meta-regression using linear mixed-effect model with the > lme() function that has two fixed effect variables;one as a log > transformed variable (x) and one as factor (y)

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

Hello, I am working from a linux 64 machine on a server with R-2.12 (I can't update to 2.13). I am iterating through many linear mixed models for longitudinal data and I occasionally receive the following convergence error: > BI.lme <- lme(cd4 ~ time + genBI + genBI:time + C1 + C2 + C11 + C12, random =~ 1 + time | IID, data = d) Error in lme.formula(cd4 ~ time + genBI + genBI:time +