search for: nonconverging

...which the singularity occurs, the results are output (when returnObject=TRUE). What I want is for nlme to simply output the current estimates both when the maximum number of iterations is reached and when a singularity issue arises. I know it isn't generally good to make use of the results of nonconverging results, but I'm interested in the estimates when the singularity is realized. Specifically I'll use the information in a simulation study and compare the results from the converging sets with the nonconverging sets, etc. Also, suppose one has a set of data where the values are generally s...

Hi - I am doing some monte carlo simulations comparing bayesian (using Plummer's jags) and maximum likelihood (using lmer from package lme4 by Bates et al). I would like to know if there is a way I can flag nonconvergence and exceptions. Currently the simulations just stop and the output reads things like: Error in optim(.Call("lmer_coef", x, 2, PACKAGE = "Matrix"), fn,

I was in a presentation of optimizations fitted with both MPlus and SAS yesterday. In a batch of 1000 bootstrap samples, between 300 and 400 of the estimations did not converge. The authors spoke as if this were the ordinary cost of doing business, and pointed to some publications in which the nonconvergence rate was as high or higher. I just don't believe that's right, and if some

I've been writing a bunch of simulation experiments to test models in MASS (glm.nb) and JK Lindsey's repeated library (gar and kalcount). If I generate data over and over, and estimate a model for each, I end have syntax like this: x <- rnorm(1000) for (i in 1: nOfRuns){ y <- getPhonyData(x) estim <- glm.nb(y~x,link="log") } Except for problems of nonconvergence

Dear Dr. Gilbert, Dr. Varadhan and all R-help list members, I'm using the function BBsolve( ) and I have some questions on the stopping criterion "maxit" and "noimp" specified in the option "control". Here are two such examples I'm having problem with. In these two examples, the function BBsolve( ) always stops at iteration 100, overlooking the values

Actually, I re-read the post and think it needs clarification. We may both be right. If the question is "I am building a model and want to know if I should retain this random effect?" (or something like that) then the LRT should be used to compare the fitted model against another model. This would be accomplished via anova(). In other multilevel programs, the variance components are

Hello list, I am dealing with a noisy function (gradient,hessian not available) with simple boundary constraints (x_i>0). I've tried constrOptim() using nelder mead to minimize it but it is way too slow and the returned results are not satisfying. simulated annealing is so hard to tune and it always crashes R program in my case. I wonder if there are any packages or functions can do

Dear All, With kind help from several friends on the list, I am getting close. Now here are something interesting I just realized: for random effects, lmer reports standard deviation instead of standard error! Is there a hidden option that tells lmer to report standard error of random effects, like most other multilevel or mixed modeling software, so that we can say something like "randome

CRAN (and crantastic) updates this week New packages ------------ * disclapmix (0.1) Maintainer: Mikkel Meyer Andersen Author(s): Mikkel Meyer Andersen and Poul Svante Eriksen License: GPL-2 http://crantastic.org/packages/disclapmix disclapmix makes inference in a mixture of Discrete Laplace distributions using the EM algorithm. * EstSimPDMP (1.1) Maintainer: Unknown Author(s):