search for: niterem

Hi, I try to run the example of Crawley's Book on the page 661, but it fail, look > repmeasures <- read.table("../Packages/Crawley/data/repmeasures.txt",header=T) > attach(repmeasures) > rep <- as.factor(rep) > library(nlme) > model <- lme(height~seed,random=~time|rep/seed) Erro em lme.formula(height ~ seed, random = ~time | rep/seed) : iteration limit

...0, # used in ../src/lmer.c only tolerance = sqrt(.Machine$double.eps),# ditto msMaxIter = 200, ## msTol = sqrt(.Machine$double.eps), ## FIXME: should be able to pass tolerances to nlminb() msVerbose = getOption("verbose"), niterEM = 15, EMverbose = getOption("verbose"), PQLmaxIt = 30,# FIXME: unused; PQL currently uses 'maxIter' instead gradient = TRUE, Hessian = FALSE # unused _FIXME_ ) { list(maxIter = as.integer(maxIter), tolerance = as....

Hello! One note/question hier about specification of control-parameters in the lme(...,control=list(...)) function call: i tried to specify tne number of iteration needed via lme(....,control=list(maxIter=..., niterEM=...,msVerbose=TRUE)) but every time i change the defualt values maxIter (e.g. maxIter=1, niterEM=0) on ones specified by me, the call returns all the iterations needed until it's converged. and this is exactly the problem i will to get round. ( e.g. in example on p.81 of Pinheiro/Bates,...

I am trying to run a GLMM on some binomial data. My fixed factors include 2 dichotomous variables, day, and distance. When I run the model: modelA<-glmmPQL(Leaving~Trial*Day*Dist,random=~1|Indiv,family="binomial") I get the error: iteration 1 Error in MEEM(object, conLin, control$niterEM) : Singularity in backsolve at level 0, block 1 >From looking at previous help topics,( http://tolstoy.newcastle.edu.au/R/help/02a/4473.html) I gather this is because of the dichotomous predictor variables - what approach should I take to avoid this problem? Thanks, Elva. __________...

...try to increase the number of iterations (even to 1000) by increasing maxIter it still doesn't converge fit <- lme(LVDD~Age + Gender + Genotype + Gender.Age + Genotype.Age + Gender.Genotype.Age, + random=~ Age|Animal, data=VC, control=list(maxIter=1000, msMaxIter=1000, niterEM=1000)) NB. I changed maxIter value in isolation as well as together with two other controls with "iter" in their name (as shown above) just to be sure ( as I don't understand how the actual iterative fitting of the model works mathematically) I was wondering if anyone k...

...function nlmeControl() control the convergence criteria. These default values can be modified to make convergence critera more or less stringent. In an effort to get more converging results, I first increased the iterations for 'maxIter', 'pnlsMaxIter', 'msMaxIter', and 'niterEM' by a factor of 20. I don't believe there is any harm in doing this. There are a few parameters, however, in the nlme controls that I'm not quite sure what they mean or do. For example, exactly what is 'minScale?' The help file says that it is "the minimum factor by which...

...bservation consisting of a reaction-time measure (RT) and an activation maesure (betadlpcv). since we want to analyze the influence of (age-)group and RT on the activation, we call: lme(betadlpcv ~ RT*group, data=our.data, random=~ RT |subject) this yields: Error in MEEM(object, conLin, control$niterEM) : Singularity in backsolve at level 0, block 1 In addition: Warning message: Fewer observations than random effects in all level 1 groups in: lme.formula(betadlpcv ~ RT * group, data = patrizia.data, random = ~RT | what's the problem here? thanks for your kind help christoph -- Lus...

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 involves a rater choosing between a versus b, b versus c, or a versus c. The response is either 0 (not...

...and slopes for students and students within schools. The first model is fit using lme and the second is fit using lmer. fm3 <- lme(math ~ year, random=~year|schoolid/childid, egsingle) fm4 <- lmer(math ~ year +(year|schoolid:childid) +(year|schoolid), egsingle, control=list(gradient = FALSE, niterEM = 0)) Both result in parameter estimates that are exactly the same. The newest release of lme4 allows for an even easier transition between lme and lmer. Model syntax in lmer can be specified as: fm5 <- lmer(math ~ year +(year|schoolid/childid), egsingle, control=list(gradient = FALSE, niterEM...

...but the fixed effects are very different. Here is my R code and output, with some columns and rows deleted for space considerations: FOO.lme1 <- lme(fixed = Y ~ X1*X2, random = ~ X2 | X3, data = FOO, method = "REML", control = list(maxIter = 200, msMaxIter = 200, tolerance = 1e-8, niterEM = 200, msTol = 1e-8, msVerbose = TRUE, optimMethod = "Nelder-Mead")) 0 203.991: 0.924323 -0.0884338 0.493897 1 203.991: 0.924323 -0.0884338 0.493897 > summary(FOO.lme1) Linear mixed-effects model fit by REML Data: FOO AIC BIC logLik 357.484 373.6868 -...

Hi all, I'm having some difficulty with lme. I am currently trying to run the following simple model anova(lme(x ~ f1 + f2 + f1:f2, data=m, random=~1|r1)) Which is currently producing the error Error in MEEM(object, conLin, control$niterEM) : Singularity in backsolve at level 0, block 1 x is a numeric vector containing 194 observations. f1 is a factor vector containing two levels, and f2 is a different factor vector containing 5 different levels. R1 is a another factor vector containing 13 different levels, and it is again, unba...

...i==1 & j==2) results <- result if (i!=1 | j!=2) results <- rbind(results, result) } ----------------------------------------------------------------------------------------------------- after submitting this code, I got the error message saying "Error in MEEM(object, conLin, control$niterEM) : Singularity in backsolve at level 0, block 1", I know this might be some issue due to the missing values. what I want to do is to skip the 'bad' variable, for example, I got this error message when j=116, so I just wanna ignore variable116, is there a statement that can do this: wh...

...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 this statement do the job? Thank you very much. Cheers! Han-Lin Lai, Ph.D Han-Lin.Lai at noaa.gov 508-495-2312 -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.htm...

...tro 3 45 37.68560 <.0001 Variety:nitro 6 45 0.30282 0.9322 # Excellent! However --- > fit2 <- lme(yield ~ Variety*nitro, random=~1|Block/Variety, subset= + !(Variety == "Golden Rain" & nitro == "0")) Error in MEEM(object, conLin, control$niterEM) : Singularity in backsolve at level 0, block 1 -- ************************************************ * I.White * * University of Edinburgh * * Ashworth Laboratories, West Mains Road * * Edinburgh EH9 3JT *...

...39;Matrix' The only object in my .RData is last.warning, thus: > last.warning $"optim or nlminb returned message false convergence (8)" "LMEoptimize<-"(`*tmp*`, value = list(maxIter = 200, tolerance = 1.49011611938477e-08, msMaxIter = 200, msVerbose = 0, niterEM = 15, EMverbose = FALSE, PQLmaxIt = 30, analyticGradient = TRUE, analyticHessian = FALSE)) This was generated by a call, in a previous session,to lmer() There may be a problem with the package Matrix, but it seems a bit extreme that this prevents restoration of the session. John Maindonald...

...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 <- lmer2(read ~ SES*age +(1| schlid:idl) +(1| schlid), hamburg, control=list(gradient = FALSE, niterEM = 0)) where my data is "hamburg" and "idl" and "schlid" are the student and school ids, respectively. (1) Is this the specification I want to estimate to obtain those effects while accounting for...? I'm not sure about the grouping variables specification. If not...

...:16,each=2))) d$y <- rnorm(32) + rnorm(length(levels(d$subj)))[d$subj] anova(lme(y~x,random=~1|subj,data=d)) ## works anova(lme(y~as.factor(x),random=~1|subj,data=d)) ## works anova(lme(y~x+as.factor(x),random=~1|subj,data=d)) ## fails: # Error in MEEM(object, conLin, control$niterEM) : # Singularity in backsolve at level 0, block 1 summary(aov(y~x+as.factor(x)+Error(subj),data=d)) ## works: # Error: subj # Df Sum Sq Mean Sq F value Pr(>F) # x 1 8.434 8.434 4.780 0.0493 * # as.factor(x) 2 10.459 5.230 2.964 0.0900 . # Residuals 12 2...

...exp(eps)) * time), data = Parameterg, fixed=list(A0+Ea~1,A1~Process), random=eps~1, start=c(93, 92, 34.5,37), control=list(msVerbose=TRUE, maxIter = 200), verbose=TRUE, method="REML", na.action=na.pass) this fit give the following error: Error in MEEM(object, conLin, control$niterEM) : Singularity in backsolve at level 0, block 1 HOWEVER, in SAS the same model with covariate is WORKING! When I'm changing the fixed part in nmle as follows fixed=list(A0+Ea~1,A1~1|Process), then the following error is popping up: Error in contr.treatment(n = 0L) : not enough degrees...

...uot;,"Modality","logit","var.logit","dis","non.dis") data.bi<-rbind(data.S,data.E) require(nlme) lme(logit~dis*Modality+non.dis*Modality, random=~dis|id+non.dis| id,data=data.bi) but i recive a erro msg : Error in MEEM(object, conLin, control$niterEM) : Singularity in backsolve at level 0, block 1 How in solve this problem? Whats is wrong? Thanks in advance -- Bernardo Rangel Tura, M.D,Ph.D National Institute of Cardiology Brazil

...mes(cmat.r) <- c(".L",".Q",".mid",".inter",".extr") contrasts(ordered(datamic$WA)<- cmat.r When I run the lme models, for all models involving WA, I get an error message (approximate English translation): Error in MEEM(object, conLin, control$niterEM) : Singularity encountered in inverse resolution in level 0, block 1 When I run the lmer models, for all models involving WA, I get an error message (approximate English translation): Warning message: In mer_finalize(ans) : gr cannot be computed at initial par (65) What is/are the problems and...