search for: controlv

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

Hello R users, I'm trying to do simulations for comparing cox and weibull I have come across this problem: Warning messages: 1: In survreg.fit(X, Y, weights, offset, init = init, controlvals = control, : Ran out of iterations and did not converge 2: In survreg.fit(X, Y, weights, offset, init = init, controlvals = control, : Ran out of iterations and did not converge what i did is fallowing simulasyon<-function(x,oran){ + y<-rexp(length(oran),rate=oran) + z<-rexp(le...

...s of the code that don't have a Stata license. The code is: require(AER) left = 3.218476 x = c(0,6,12,18) y = c(10.065819,7.803843,5.164786,3.218476) mod<-tobit(y~x, left = left, right = Inf) This gives back the following warning: Warning in survreg.fit(X, Y, weights, offset, init = init, controlvals = control, : Ran out of iterations and did not converge Has anyone come across this problem before or know a way to fix the problem? Thanks in advance, Jennifer -- View this message in context: http://r.789695.n4.nabble.com/tobit-regression-model-tp3345789p3345789.html Sent from the R help...

What's happening? > Celpe.Mixed <- lme(Nota~Tarefa + Corretor %in% Tarefa,random=~1|ID,data=Celpe.Dados11) Error in "EMsteps<-"(`*tmp*`, value = controlvals) : error code 6 from Lapack routine dtrtri > I've trying to fit a Mixed model using lme from package lme4 and the error above has occourred. What Should I Do to solve this? Best regards []s Leonard Assis Estatístico / CONFE 7439 UIN : 41-764-523 / Skype : lmassis / msn : lm...

...2.1 I-likelihood = -20098.2 Degrees of freedom for terms= 0.5 0.4 1039.4 Likelihood ratio test=232877 on 1039 df, p=0 n=4840 (57 observations deleted due to missingness) Warning message: Inner loop failed to coverge for iterations 2 in: survpenal.fit(X, Y, weights, offset, init = init, controlvals = control, > Could you please help me in finding the error in my code (if any) so that I will not get this error message? I would really appreciate if you could help me in this regard. Thank you so much. Sincerely, Sattar ______________________________________________________...

...ly generated from a Weibull distribution and the model parameters estimated. When this is embedded within a loop and the process repeated for different datasets, the program aborts after a few runs in most cases giving the following message: Error in survreg.fit(X, Y, weights, offset, init = init, controlvals = control, : NA/NaN/Inf in foreign function call (arg 9) In addition: Warning message: no non-missing arguments to max; returning -Inf I'm having similar problems when using the lognormal distribution as well. Does anyone have any suggestions\thoughts on how to circumvent this iss...

...For reasons that are not important here now, I wanted to look for the effects of 'randomly' re-allocating event types to the lifetime data. However, when I did this (keeping everything else the same), R returned this error message: Error in survreg.fit(X, Y, weights, offset, init = init, controlvals = control, : NA/NaN/Inf in foreign function call (arg 9) I am puzzled by this error message and can't find anything in the archives etc. Your input would be greatly appreciated. Best regards, Michael Michael Drescher Ontario Forest Research Institute Ontario Ministry of...

> I was wondering why survreg (in survival package) can not handle > three-way interactions. I have an AFT ..... You have given us no data to diagnose your problem. What do you mean by "cannot handle" -- does the package print a message "no 3 way interactions", gives wrong answers, your laptop catches on fire when you run it, ....? Also, make sure you read

Helpers. An instrument sends me data that is mostly nonlinear. I have a group of functions to manipulate this data so that it is useful to the user. One of them involves a nls model that called to fit a line to the data and returns the fits. This works well 99 out of 100 times. Occasionally, the sensor sends some bad data to which a nls model cannot be fit. When that happens, the nls function

Dear list members, In re-running with GLMM() from the lme4 package a generalized-linear mixed model that I had previously fit with glmmPQL() from MASS, I'm getting a warning of a convergence failure, even when I set the method argument of GLMM() to "PQL": > bang.mod.1 <- glmmPQL(contraception ~ as.factor(children) + cage + urban, + random=~as.factor(children) + cage +

...gt; important, importantV, NA complex <-> complex, complexV, NA difficult <-> difficult, difficultV, NA manydimensions <-> manydimensions, manydimensionsV, NA couldavoid <-> couldavoid, couldavoidV, NA equalinfo <-> equalinfo, equalinfoV, NA control <-> control, controlV, NA desired <-> desired, desiredV, NA howmuchinfo <-> howmuchinfo, howmuchinfoV, NA posoutcome <-> posoutcome, posoutcomeV, NA eval <-> eval, evalV, NA challenge <-> challenge, challengeV, NA informed <-> informed, informedV, NA --1660387551-150661043-1249684349...