similar to: convergence error code in mixed effects models

Dear all, In a greenhouse experiment we tested performance of 4 different species (B,H,P,R) under 3 different water levels in 10 replications. As response variable e.g. the number of emerging sprouts were measured on three dates. A simple Anova considering every measurement date separately shows a higly significant effect of species and moisture (and partly the interaction of both). The

With a recent checkout of dovecot-2 I'm getting a few "BUG: Unknown internal error" messages. The log for one such incident: Jul 21 10:21:38 postamt dovecot: imap(ikurzweg): copy from INBOX: box=INBOX.Trash, uid=6385, msgid=<4DA3E334.000005.02956 at ILONA-PC>, size=90980 Jul 21 10:21:38 postamt dovecot: imap(ikurzweg): copy from INBOX: box=INBOX.Trash, uid=6386,

Dear list members, the following hlm was constructed: hlm <- groupedData(laut ~ design | grpzugeh, data = imp.not.I) the grouped data object is located at and can be downloaded: www.anicca-vijja.de/lg/hlm_example.Rdata The following works: library(nlme) summary( fitlme <- lme(hlm) ) with output: ... AIC BIC logLik 425.3768 465.6087 -197.6884 Random effects:

Hi all, I''ve been trying to fit a mixed effects model and I''ve been having problems. =>My aim: to know whether states atributes, political parties and individual atributes affect the electoral results of men and women candidates. I use candidates as replications for states and for political parties. =>Response: Percentage of valid votes casted to each

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

According to the R documentation for NLMINB, the returned value of convergence is 0 for successful convergence. When I got another code (1), I looked up the PDF that linked from the documentation (http://netlib.bell-labs.com/cm/cs/cstr/153.pdf), which said that a return code under 3 was impossible. Is there other documentation that gives the correct meanings of the NLMINB convergence codes in

Hi, I was trying to do a multi-level modeling of the data using the nlme package. I encounter the following message: > addRandomSlopeRange<- lme(FAN.range ~ CHN.range + Age + Block.T, data=data.block, random = ~CHN.range|CHI, method = "ML", na.action=na.omit) Error in lme.formula(FAN.range ~ CHN.range + Age + Block.T, data = data.block, : nlminb problem, convergence error code

Hello splus-users, I am trying to fit a regression model for an ordered response factor. So I am using the function polr in library(MASS). My data is a matrix of 1665 rows and 63 columns (one of the column is the dependent variable). The code I use is polr(as.ordered(q23p)~.,data=newdatap) but I am getting the following warning message singularity encountered in: nlminb.1(temp, p, liv, lv,

Hi, I'm trying to fit some data using a logistic function defined as y ~ a * (1+m*exp(-x/tau)) / (1+n*exp(-x/tau) My data is below: x <- 1:100 y <- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,1,1,1,2,2,2,2,2,3,4,4,4,5, 5,5,5,6,6,6,6,6,8,8,9,9,10,13,14,16,19,21, 24,28,33,40,42,44,50,54,69,70,93,96,110,127,127,141,157,169,

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

I've come into some code that produces different results under R 2.1.1 and R 2.2.1. I'm really unfamiliar with the libraries in question (MASS and nlme), so I don't know if this is a bug in my code, or a regression in R. If it's a bug on my end, I'd appreciate any advice on potential causes and relevant documentation. The code:

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 =

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 +

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

Hi R community! I've fitted three mixed-effects regression models to a thousand bootstrap samples (case-resampling regression) using the lme4 package in a custom-built for-loop. The only output I saved were the inferential statistics for my fixed and random effects. I did not save any output related to the performance to the machine learning algorithm used to fit the models (REML=FALSE).

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 users, I want to conduct a small simulation study and I have to use the lme function in a loop to save the restricted log likelihood. However, for one simulated data set the lme function gives this error Error en lme.formula(yboot ~ X[, -1], data = data.fr, random = Z.block) : nlminb problem, convergence error code = 1 message = singular convergence (7) and then, the

Hi Everybody, My problem is that nlminb doesn't converge, in minimising a logLikelihood function, with 31*6 parameters(2 weibull parameters+29 regressors repeated 6 times). I use nlminb like this : res1<-nlminb(vect, V, lower=c(rep(0.01, 12), rep(0.01, 3), rep(-Inf, n-15)), upper=c(rep(Inf, 12), rep(0.99, 3), rep(Inf, n-15)), control = list(maxit=1000) ) and that's the result :

Dear all, I am trying to fit a mixed-effects non linear regression, but I have some trouble with it. My data are a balanced panel of 904 subjects with 8 observations (at regular periods) per subject. The functional form of my model is Y=Aexp(-BX1)X2 +e. I want to allow parameters A and B to vary among subjects and also include an autocorrelation term. I have already fitted a standard nonlinear

Dear all,   I have data from the following experimental design and trying to fit a mixed model with lme function according to following steps but struggling. Any help is deeply appreciated.   1) Experimental design: I have 40 plants each of which has 4 clones. Each clone planted to one of 4 blocks. Phenotypes were collected from each clone for 3 consecutive years. I have genotypes of plants. I