search for: mer_finalize

...ckage library(lme4) # first fit a linear model with letter position nested in word length lm(rt ~ length + length:pos, data=df) # fit a mixed effects model, with subj (participant) as random effect lmer(rt ~ length + length:pos + (1 | subj), data=df) Using lmer() results in an error: Error in mer_finalize(ans) : Downdated X'X is not positive definite, 13. I don't experience any problems using lm(). Does anyone know where things go wrong? ~ Ben Meijering

...d 4 sets for each radii: 1) with both random effects, 2+3) only one (each) random effect, and 4) none. I have tried to model the relationships using glm, glmer and lmer. The problem is that in the full model (all 7 variables) of some sets with random effects I get the following error: *Error in mer_finalize(ans) : Downdated X'X is not positive definite, 8.* The only record of this error I have found in the internet is for data with missing observation. This doesn't comply with my data set. I think it might be a singularity problem but I don't know how to check it, and as a result, I obv...

...lt;-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 Site/Block/Plot= factor Year = integer I get the following warning message: Warning messages: 1: In mer_finalize(ans) : Cholmod warning 'not positive definite' at file:../Cholesky/t_cholmod_rowfac.c, line 432 2: In mer_finalize(ans) : singular convergence (7) Is this due to the nature of my fixed/random factors or the way I put up the random factors? In lme I could include a component for autocorr...

Hello, I have been attempting to run a poisson glmm using lme4 for some time now and have had a lot of trouble. I would say 9 times out of 10 I receive the following warning: CHOLMOD warning: %h Error in mer_finalize(ans) : Cholmod error `not positive definite' at file:../Cholesky/t_cholmod_rowfac.c, line 432 My data are counts of microbe colony forming units (CFUs) collected from termite cuticles and the surrounding environment over a 3 year period. I am attempting to analyze the effect of several fac...

...ce, and income structure, poverty=county poverty rate, fastfood=number of fastfood places per 100,000 people in the county, and a county random effect. > > If I leave out fastfood, the model gets at least fitted - although it doesn't converge (yet): > > Warning message: > In mer_finalize(ans) : false convergence (8) > > I would be grateful for any advice on what the problem could be and how to resolve it. > > Thanks, > Tanja Tanja Srebotnjak, PhD, MSc, Dipl. Stat. Postgraduate Fellow Institute for Health Metrics and Evaluation University of Washington 2301 5th...

Hello! I am trying to run a GLMM using LME4, and keep getting the warning message: "In mer_finalize(ans) : false convergence (8)" I am quite new to R, and in looking into this thus far, it appears that there are a variety of reasons why this might occur, such as needing to standardize some parameters or if all subjects in one combination of parameters all have the same outcome. I also unde...

.... x2 is the level 2 predictor variable. id.full is the conditioned variable. It runs beautifully when only x1 is centered (I subtracted the grand mean from each value). However, when I also centered x2 variable with the same procedure, it gives me the following error message: Warning message: In mer_finalize(ans) : singular convergence (7) I'd appreciate if someone could explain me what it means. One of the differences between "non-centered values" and "centered values" is that the "centered values" include negative values. Could it be the reason? If so, what shall...

...4_0.999375-32.zip; R-2.10.1). According to the information, I should be able to use a generalized linear mixed model. However when I try to fit a model with Gamma distribution of the errors, it gives me the following error model1<-lmer(Cmic~1+(1|FOREST/DEPTH),data=DATOS,family=Gamma) Error en mer_finalize(ans) : General form of glmer_linkinv not yet written The same occurs with the function glmer, although I have seen in the forum that other people has been able to implement this type of models. Does anyone know why is this happening? Thanks! Cristina IRNAS-CSIC Seville, Spain -- View th...

...ls(regdat$ObsGender) [1] "F" "M" > levels(regdat$WalkerGender) [1] "F" "M" the watning is: > fit1<-lmer(Agree~Display+Survey+Emotion+WalkerGender+ObsGender+(1|WalkerID)+(1|ObsID),family=binomial(link='logit'),data=regdat) Warning message: In mer_finalize(ans, verbose) : gr cannot be computed at initial par (65) Does anybody have some hint to solve this problem? I'd very much appreciate it! Thanks, Lan Wei

Dear R users: I am running lmer(), and I had the following warning message: "In mer_finalize(ans): iteration limit reached without convergence (9) What is the default limit for lmer()? And how do I increase the limit? Many thanks, Julia -- View this message in context: http://www.nabble.com/How-to-increase-iteration-limit-for-lmer%28%29--tp20252974p20252974.html Sent from the R help ma...

...Breslow and Clayton 1993 JASA paper. Model 3 of the epileptic dataset has two random effects, one subject specific, and one observation specific. Thus if we count random effects, there are more parameters than observations. When I try to run the following code, I get an error saying: "Error in mer_finalize(ans) : q = 295 > n = 236". require (lme4) require (glmmAK) data(epilepticBC) dat = epilepticBC dat$rand=1:nrow(dat) dat$V4=dat$visit==4 formula1 = Seizure ~ Base + Trt + I(Trt*Base) + Age + V4 fit=lmer (update (formula1, .~. + (1|id) + (1|rand)), family=poisson, data=dat, nAGQ=1) Is it tr...

Hey, I am having trouble with lmer. I am looking at the presence/absence of water shrews against habitat and other factors e.g so I used this: m1<-lmer(Presencebsence~Habitatype*Width+(1|Sitename))summary(m1) But i keep getting this error up Error in mer_finalize(ans) : Downdated X'X is not positive definite, 16.> summary(m1)Error in asMethod(object) : matrix is not symmetric [1,2] What does this mean and now can I fix it? Sophie [[alternative HTML version deleted]]

Hi dears while modeling an interaction random effect in lmer i receive the instantaneous error message > ldlM4<-lmer(ldl~rt*cd4+age+rf+pharmac+factor(hcv)+ + hivdur+(rt:cd4|id),na.action=na.omit,REML=F) *Warning message: In mer_finalize(ans) : false convergence (8) * I think the matter lies in syntax, 'cause i sistematically receive the same message even when changing response... PS: the above model runs quite well in lme and only rarely I recive : * Errore in lme.formula(fixed = ldl ~ rt + cd4 + age + rf + pharmac + factor(hc...

...and group, as the raw data suggests that different groups may behave differently in different seasons. Is there a way to do this in the lmer package? When i put it in directly: model<-lmer(binom~season*group+(1|group/individual),binomial,data=data1) I get an error messgae: Warning message: In mer_finalize(ans) : gr cannot be computed at initial par (65) The model runs with the following: modelb<-lmer(binom~season*(1|group)+(1|group/badger),binomial,data=data1) or modelb<-lmer(binom~season+(1|group/badger)+(1|group:season),binomial,data=data1) but here I guess I am modelling it as part of th...

...using the weights option. I am using the R version 2.7.2 (2008-08-25) and lmer version in lme4_0.999375-26, which I think it is the latest version! I am getting and error message when I add the option "weights" in the lmer function. This is the error message I get "Error en mer_finalize(ans) : Calculated PWRSS for a LMM is negative". I have been reading in the last few weeks all the forum questions and answers about this issue. Certainly this has been discussed MANY times before especially with older versions of lmer (much less in the newer versions). I have been trying...

Hello, I hope that you can bare with me. I am new to models, but I think I have a pretty godd understanding of how to run them now, including how to use AICc and Anova. The issue is that I have many factors that I wish to compare so doing each one at a time would take forever. I came across the MuMIn package and I was so excited, however I am getting an error message and i don't know why.

...<- lme(DV ~a+b+c+d+e+f+h+i, random = random = ~ e+f+h+i| group, data = mydata) # Using lmer mix.lmer <- lmer(DV ~a+b+c+d+(1|group)+(e|group)+(f|group)+(h|group)+(i|group), data = mydata) lme provided an output (fixed effects and random effects coefficients). lmer gave me an error: Error in mer_finalize(ans) : Downdated X'X is not positive definite, 10. I've rerun lmer with - but specifying the random effects for 2 fewer predictors. This time it ran and provided an output. (BTW, the random effects of lmer with 2 fewer predictors specified as random were very close to the output of lme). Q...

...i want to remove some, but the ideal model: GLM.1 <-lmer(fledgesucc ~ +lay +hatch +elev +seadist +nnd +npd +meanterranova +minpengS1 +minpengS2 +wchillpengS1 +wchillpengS2 +(1|territory), family=binomial(logit), data=fledge) doesn't work because of these errors: 'Warning message: In mer_finalize(ans) : gr cannot be computed at initial par (65)'. "matrix is not symmetric [1,2]" If i delete one or more of the T records (i.e. minpengS2 +wchillpengS2) the model works...below and example: GLM.16 <-lmer(fledgesucc ~ lay +hatch +elev +seadist +nnd +npd +meanterranova +minpe...

...OUNT[min. 1 to max. 37,mean = 10]). --------------------------------------------------------------------- PROBLEM 1: I have developed the following GLMM: mix.1<-glmer(PRES~AG+FOR+OPEN+LDEV+DEV+COUNT+(1|SITE),family=binomial,data=merge1) I receive the following error: Warning message: In mer_finalize(ans) : false convergence (8) There is model output, but I am worried it might be biased. I have tried: -Data transformation of the predictor variables (e.g., log(COUNT)), which does seem successful in some simpler models, but does not work consistently. -The most updated version of lme4 --...

...using this formula in lmer: m<-lmer(repro ~ OTHER FIXED EFFECTS + age*(age < 2) + age*(age >= 2 & age < 8) + age*(age >= 8) + (1|id) + (1|yr), data = reproduction, family = binomial, link = "logit", GHQ = TRUE) However, this gives the warning: Warning message: In mer_finalize(ans) : gr cannot be computed at initial par (65) which is not apparent if I use just two break points or I implement the model in glm. My question is essentially whether anyone can recommend a method for performing piecewise regression in lmer or another mixed model framework. Any advice wo...