similar to: Notification of false convergence with lmer()

Running the Chapter 4 examples in Pinheiro & Bates' "Mixed-Effects Models in S and S-PLUS" (2000), I get a message that the default optimizer doesn't converge, but using "optim" for the optimizer results in convergence: > > library(nlme) > > fm1IGF.lis <- lmList(IGF) > > fm1IGF.lme <- lme(fm1IGF.lis) > Error in lme.formula(fixed =

I'm resending this message because I did not include a subject line in my first posting. Apologies for the inconvenience! Tanja > Hello, > > I'm trying to fit a generalized linear mixed model to estimate diabetes prevalence at US county level. To do this I'm using the glmer() function in package lme4. I can fit relatively simple models (i.e. few covariates) but when

Hello- Is it possible to estimate standard errors for a multiple regression model using a randomization test approach? I have seen a lot on using the procedure to get a test statistic, but nothing that talks about getting actual standard errors. Is this possible? How might I do this in R? Thank you. -- View this message in context:

Dear R Community! We analyse the impact of climbing activity on cliff vegetation. During our fieldwork, we recorded 90 Transects in 3 climbing sites. The aim is to see, if the plant cover (response: Cover) is influenced only by crevice availability (predictor: Cracs), or, additional, by the distance to the climbing route (predictor: Distance). Six plots are nested within one Transect

I have fit a model with a more complex covariance structure, but the fit reports a false convergence. I have read from past posts that this can be an indication of over-specification. I went ahead and fit a model with a simpler covariance structure. It doesn't seem like I can compare the two likelihoods or the AIC or BIC to compare the two model since the one model had false convergence.

Dear R-users, I am using the most updated package of lme4 (lme4_0.999375-2). I have a data set consisting of ~900 observations at ~440 independent survey sites. There are an unbalanced number of surveys at each site. I am attempting to develop several models evaluating the presence/absence of a species (PRES) at these random sites (SITE) using a number of predictor variables. The

Hello, I've run 7 candidate models using mixed-effects logistic regression with the lmer function from the lme4 package, and I'm getting the following error for 5 of those models: Warning message: In mer_finalize(ans : false convergence (8). The candidate models all run with the same data, just slightly different formulas (for AIC model selection). Can anyone explain this error to

Hi, I am doing a binomial GLMM with a random intercept using the formula below, but I always get the same warning message. > m01 <- lmer(pres~ HT + DN + dtree + DNm + cmnhi + cmxes + cplan + craan + lfphal0100 + lfov0100 + lfop0100 + (1|plot), family=binomial, data=vphal, verbose=TRUE) 0: 6309.9448: 0.459924 -5.20747 -0.378722 0.558779 -0.200922 -0.0488451 -0.397844 0.367916 -2.09820

Dear members, I would like to switch from nlme to lme4 and try to translate some of my models that worked fine with lme. I have problems with the pdMat classes. Below a toy dataset with a fixed effect F and a random effect R. I gave also 2 similar lme models. The one containing pdLogChol (lme1) is easy to translate (as it is an explicit notation of the default model) The more parsimonious

Dear R users, I am trying to fit a GLMM to the following dataset; tab a b c 1 1 0.6 199320100313 2 1 0.8 199427100412 3 1 0.8 199427202112 4 1 0.2 199428100611 5 1 1.0 199428101011 6 1 0.8 199428101111 7 0 0.8 199527103011 8 1 0.6 199527200711 9 0 0.8 199527202411 10 0 0.6 199529100412 11 1 0.2 199626201111 12 2 0.8 199627200612 13 1 0.4 199628100111 14 1 0.8

Hi, I am new to this list. I have a question regarding including both spatial and temporal random factors in lmer. These two are not nested, and an example of model I try to fit is model1<-lmer(Richness~Y+Canopy+Veg_cm+Treatment+(1|Site/Block/Plot)+(1|Year), family=poisson, REML=FALSE), where richness = integer Y & Treatment = factor Canopy & Veg_cm = numerical, continous

Dear all, I want to estimate a crossed-random-effects model (i.e., measurements, students, schools) where students migrate between schools over time. I'm interested in the fixed effects of "SES", "age" and their interaction on "read" (reading achievement) while accounting for the sample design. Based on a previous post, I'm specifying my model as: fm1 <-

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 r-helpers, I can run the examples on the mcsamp help page. For example: **************************************** > M1 <- lmer (y1 ~ x + (1|group)) > (M1.sim <- mcsamp (M1)) fit using lmer, 3 chains, each with 1000 iterations (first 500 discarded) n.sims = 1500 iterations saved mean sd 2.5% 25% 50% 75% 97.5% Rhat n.eff beta.(Intercept)

Hi, I am trying to run a logistic regression to look at the risk of malaria infection in individuals. I want to account for intra household correlation and so want to include a household level random effect. I have been using the lmer command in lme4 package but am getting some strange results that are completely different to those I get using STATA. Can I just check that this is the correct

Dear R Users, I am having trouble with lmer. I am looking at recombinant versus non recombinant individuals. In the response variable recombinant individuals are coded as 1's and non-recombinant as 0's. I built a model with 2 fixed factors and 1 random effect. Sex (males/females) is the first fixed effect and sexual genotype (XY, YY, WX and WY) the second one. Sexual Genotype is

Hello list, I've been reading through the archives and it seems as though, as of right now, there is no way to specify the correlation structure in lmer. I was wondering if anyone knows if this is going to be implemented? I'm using mixed-effects models within a tree structure, so I make a lot of calls to lme to get the resulting deviance, and lmer2 is almost 5 times faster than lme

I am helping my wife do some statistical analysis. She is a biologist, and she has performed some measurements on various genotypes of mice. My background is in applied mathematics and engineering, and I have a fairly good statistics background, but I am by no means a PhD level expert in statistical methods. We have used the lmer package to fit various models for the various experiments that she

Hi all, I have a problem when running lmer. In my data set, Agree is a binary(0/1) response. WalkerID and ObsID is the identification number of the subjects. the description of the other variables are as follows: > levels(regdat$Display) [1] "Dynamic" "Static" > levels(regdat$Survey) [1] "HM1_A" "HM1_B" "HM1_C" "HM2_A"

Version 0.9975-11 of the lme4 package has been uploaded to CRAN. The source package should be available on the mirrors in a day or two and binary packages should follow soon after. There are several changes in this release of the package. The most important is the availability of a development version of lmer called, for the time being, lmer2. At present lmer2 only fits linear mixed models.