search for: msmaxiter

...ot;LMEoptmize" and "LMEoptimize<-" are not exported from the namespace in Matrix, so I simply copied the LMEoptimize function and made it an ordinary function as shown below. Thanks in advance S?ren LMEoptimize <- function(x, value) { if (value$msMaxIter < 1) return(x) nc <- x at nc constr <- unlist(lapply(nc, function(k) 1:((k*(k+1))/2) <= k)) fn <- function(pars) deviance(.Call("mer_coefGets", x, pars, 2, PACKAGE = "Matrix"))...

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

...ize 1 of downdated X'X is indefinite Error in .local(object, ...) : Leading 2 minor of Omega[[1]] not positive definite In addition: Warning messages: 1: optim or nlminb returned message ERROR: ABNORMAL_TERMINATION_IN_LNSRCH in: "LMEoptimize<-"(`*tmp*`, value = list(maxIter = 200, msMaxIter = 200, 2: optim or nlminb returned message ERROR: ABNORMAL_TERMINATION_IN_LNSRCH in: "LMEoptimize<-"(`*tmp*`, value = list(maxIter = 200, msMaxIter = 200, thanks for any help. -eduardo

...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, niter=50, msTol=1e-5, nlmStepMax=500, ,msVerbose=TRUE ,returnObject=TRUE ) However, the lme-functions does not produce verbose output, nor does it return the best fit if lme is not converging. It returns only an error: Error in lme.formula(y ~ lndbh + I(lndbh^2) + lnh +...

...nd nlsTol which I assume are the analogs of maxiter and tol in nls.control. I would be happy to hear from anyone who has an idea on what parameters in gnlsControl to change to get convergence. Cheers Dan Coleman Genentech Inc. > gnlsControl function (maxIter = 50, nlsMaxIter = 7, msMaxIter = 50, minScale = 0.001, tolerance = 1e-06, nlsTol = 0.001, msTol = 1e-07, msScale = lmeScale, returnObject = FALSE, msVerbose = FALSE, apVar = TRUE, .relStep = (.Machine$double.eps)^(1/3), nlmStepMax = 100, opt = c("nlminb", "optim"), optimMethod = "BFGS&q...

Dear R community, I'm fitting a complex mixed-effects model that requires numerous iterations and function evaluations. I note that nlminb accepts a list of control parameters, including eval.max. Is there a way to change the default eval.max value for nlminb when it is being called from lme? Thanks for any thoughts, Andrew -- Andrew Robinson Department of Mathematics and Statistics

...of this email. I do have a nagging, unanswered question: What exactly does "Warning message: Step halving factor reduced below minimum in NLS step in: gnls(model = y ~ 5 + ...)" mean? I have tried to address this by specifying "control = list(maxIter = 1000, pnlsMaxIter = 200, msMaxIter = 1000, tolerance = 1e-06, pnlsTol = 1e-04, msTol = 1e-07, minScale = 1e-10, returnObject = TRUE)" in my model calls, but this does not entirely eliminate the problem (I am running gnls( ) 24 separate times on separate data sets). Much thanks in advance, david paul #Constructing Indi...

...If I 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 wonderi...

...mated random effects are identical, 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...

...). The parameters in the 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...

...del. The base model is parameterized in terms of CL, V1, V2 and Q. basemodel<-nlme(Conc ~TwoCompModel(CL,Q,V1,V2,Time,ID), data = data2, fixed=list(CL+Q+V1+V2~1), random = pdDiag(CL+V1+V2~1), start=c(CL=log(20),Q=log(252),V1=log(24.9),V2=log(120)), control=list(returnObject=TRUE,msVerbose=TRUE, msMaxIter=20,pnlsMaxIter=20,pnlsTol=1), verbose=TRUE). Now, I want to include covariates (age and weight) in the base model. The relationships between CL and covariate is linear and given as the following equation. CL=TVCL+a*wt-b*age, where TVCL is mean clearance, a and b are coefficients of weight and age...

All, I'm trying to create an augPred plot in the nlme library, similar to the plot on p.43 of Pinheiro & Bates (Mixed Effects Models in S and S-Plus) for their Pixel data. My data structure is the same as the example but I still get the error msg below. > comp.adj.UKV <- groupedData(adj.UKV ~ Time | Patient_no/Lisinopril, data = comp.adj.UKV.frm, order.groups = F) >

...n 'loadNamespace' for '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...

...I include two particular variables: date (from 23 to 34; 1 being to first sampling day) and Latitude (UTM/100 000, from 55.42 to 56.53). No "NA" is any of those variables. In those cases, I get the warning message: "nlminb failed to converge" I tried to modify lmer controls as : msMaxIter; maxIter... but I still get the message. Should I bother about it? If yes, what should I do to not get the message? Thank you all in advance for you answers. PS: My model writing is for example Fm<-lmer(alive~factor(sex)+mass+parasite+Latitude+(1|ID), family=binomial, method="AGQ",...

...ekt) , > + data = Lmm.spalten.corr, method = ("ML")) Warning messages: 1: In .local(x, ..., value) : Estimated variance-covariance for factor ?Site? is singular 2: In .local(x, ..., value) : nlminb returned message false convergence (8) For this one, i changed the maxIter and msMaxIter up to 50000000, but the Warning messages remained the same. I have many "0" values for "Cover" and "Cracks" and tried to exclude some of them in different ways (as all transects without vegetation cover), but this did not help. The current version deals with all transe...

...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,2000: fm1Rail.lme<-lme(...,control=list(maxIter=1,...)) ) so i have tried with option msMaxIter=... and this works. The other problem is, that i even can not see (in R !!!) the output from iterations, despite the msVerbose=TRUE specification and setting options(verbose=TRUE) (The S-plus can do it but also ignoring the maxIter=... specification) Thank you for your hint

...gt; n Error: subscript out of bounds Here the fm1Indom.nlme object is calculated with a formula somewhat different from the one given in the text, because that gives convergence problems: fm1Indom.nlme <- nlme(fm1Indom.lis, random=pdDiag(A1+lrc1+A2+lrc2 ~ 1) , control=list(msMaxIter=1000, msVerbose=TRUE,niterEM=100, returnObject=TRUE)) The algorithm says it has not converged, although the tracing output multiple times indicate that the current solution is probably a solution. The summary of the obtained object is very close to that given in the book....

...t, fixed = a.max + a.qe + lcp ~ trt, random = a.max + a.qe + lcp ~ 1 | bench/line, start = list(fixed=c(17.4305, 0.182444, 0.00928341, -0.00057221, 44.8384, 8.67678)), method="ML", verbose=T, control = nlmeControl(maxIter=250, msMaxIter=200, pnlsMaxIter=20, gradHess=TRUE, returnObject=TRUE, niterEM=100)) What I'd like to do is to compare reduced models with something like random = list(a.max ~ 1 | bench, a.qe + lcp ~ 1 | bench/line) i.e., to drop the nested term on each of the coefficient...

...gB)*exp(-exp(logC)*ti),c("A","logB","logC"), function(ti,A,logB,logC){}) Lact.nlme<-{nlme(model=MLKYLD~W3(DIM,A,logB,logC), fixed=A+logB+logC~1, random=A+logB+logC~1|ID, data=LacData.x0, start=c(13.41143,log(0.152792),log(0.002494)), control=nlmeControl(msMaxIter=200), verbose=T) } Any help or advice would be greatly appreciated, Evelyn -- Evelyn Hall PhD Student Faculty of Veterinary Science C01 JL Shute Building Camden University of Sydney

...age false convergence (8) in: LMEopt(x = mer, value = cv) 2: nlminb returned message false convergence (8) in: LMEopt(x = mer, value = cv) 3: nlminb returned message false convergence (8) in: LMEopt(x = mer, value = cv) I tried to increase maximum iteration limit by adding control=list(msMaxIter=600, maxIter=300, PQLmaxIt=100) That seems not help. What can I do next? Thanks all! Mingyu Feng ================================================================================ Department of Computer Science Tutoring Research Group Worcester Polytechnic Institute