search for: nonconverg

...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 generall...

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, gr, method = "L-BFGS-B", : L-BFGS-B needs finite values of 'fn' In addition: Warning message: Leadin...

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 problem is posed so that the estimate is not obtained in such a large sample of applications, it either means the problem is badly asked or badly answered. But I've got no traction unless I can actually do...

...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 and other maximum likelihood estimation problems, which I gather are standard things you have to work around by fiddling initial values of parameters (?), this works OK, except I can't figure how to keep the estimate objects for later analysis. Inside the loop, I am able to grab coeffic...

...incerely yours, Tingting Zhan Example 1: The equation tst.fn() returns vector-0 after 2 seconds of running. I think I should have the BBsolve() function run about 2 seconds and then successfully converge. I specified maxit and noimp as large as 10000. However, the BBsolve function returns nonconvergence only after 0.25 seconds and after 100 iterations. > tst.fn = function(x) { if((proc.time() - R.ptm)[3] > 2) { cat("overtime.\n"); return(c(0,0)); } else { return(c(2,2)); } } > system.time( { R.ptm = proc.time(); tst = BBsolve(par=c(1,1), fn = tst.fn , control...

...tional use of "standard errors" to judge > statistical significance is subject to both intrinsic and parameter > effects errors, while likelihood ratio procedures such as anova are > subject only to the intrinsic curvature (assuming there are no > substantive problems with nonconvergence). Consequently, to judge > statistical significance of an effect, anova is usually substantially > better than the so-called Wald procedure using approximate standard errors, and is almost never worse. > If anyone knows of a case where this is NOT true, I'd like to know. >...

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):