search for: bobyqa

...r.end.group)+t2(ResidualQ95, Hms_Rsctned, by=super.end.group)+Year +Hms_Poaching +X.broadleaved_woodland +X.urban.suburban +X.CapWks, data=dat, random=~(1|WATERBODY_ID/SITE_ID)) #Warning messages: #1: In optwrap(optimizer, devfun, getStart(start, rho$lower, rho$pp), : # convergence code 1 from bobyqa: bobyqa -- maximum number of function evaluations exceeded #2: In optwrap(optimizer, devfun, opt$par, lower = rho$lower, control = control, : # convergence code 1 from bobyqa: bobyqa -- maximum number of function evaluations exceeded I also tried the following but I got the same message as be...

...and other documentation" -> "Ecdat::AverageIncomeModels". I've found other optimizers that will get around the problem in this case but none that performs as well as optim with many other problems. Thanks, Spencer Graves p.s. I've also tested bobyqa{minqa} or nloptr{nloptr}, recommended in a vignette in the lme4 package. These did better than optim in this example but worse in others I tried.

I'm planning to try to use Mike Powell's new BOBYQA optimization routine to see if it is a good candidate to eventually replace Nelder-Mead as the optim() default. (Don't panic -- this won't happen quickly.) Mike writes f77 code. After some chasing about, I've realized that because optimization codes need to call the objective function w...

...is can imply that users need to both install an R package and one or more dependencies that must be separately obtained and licensed. One such situation is where a new program is still under development and the license is not clear, as in the recent work we pursued with respect to Mike Powell's BOBYQA. We wanted to verify if this were useful before we considered distribution, and Powell had been offering copies of his code on request. Thus we could experiment, but not redistribute. Recently Powell's approval to redistribute has been obtained. We believe that it is important that non-redistr...

Hi, I would like to write a function that finds parameters of a log-normal distribution with a 1-alpha CI of (x_lcl, x_ucl): However, I don't know how to optimize for the two unknown parameters. Here is my unsuccessful attempt to find a lognormal distribution with a 90%CI of 1,20: prior <- function(x_lcl, x_ucl, alpha, mean, var) { a <- (plnorm(x_lcl, mean, var) - (alpha/2))^2 b

SAS has DUD (Does not Use Derivatives)/Secant Method for nonlinear regression, does R offer this option for nonlinear regression? I have read the helpfile for nls() and could not find such option, any suggestion? Thanks, Derek [[alternative HTML version deleted]]

Hello! I am trying to create an R optimization routine for a task that's currently being done using Excel (lots of tables, formulas, and Solver). However, otpim seems to be finding a local minimum. Example data, functions, and comparison with the solution found in Excel are below. I am not experienced in optimizations so thanks a lot for your advice! Dimitri ### 2 Inputs:

Hej, Calling newuoa (from the minqa package) makes R crash when the package rgl is loaded first. This however only on certain selected data. The data used for testing (saved to 'bugs.R'): xvals = c(1,2,4,5,7,8,9,10,11,12,14,15,16,18,19,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36) yvals =

...t;. > > > I've found other optimizers that will get around the problem > in this case but none that performs as well as optim with many > other problems. > > > Thanks, > Spencer Graves > > > p.s. I've also tested bobyqa{minqa} or nloptr{nloptr}, > recommended in a vignette in the lme4 package. These did better > than optim in this example but worse in others I tried. > > ______________________________________________ > R-devel at r-project.org <mailto:R-devel at r-project.org>...

...to | > both install an R package and one or more dependencies that must be | > separately obtained and licensed. One such situation is where a new | > program is still under development and the license is not clear, as in | > the recent work we pursued with respect to Mike Powell's BOBYQA. We | > wanted to verify if this were useful before we considered distribution, | > and Powell had been offering copies of his code on request. Thus we | > could experiment, but not redistribute. Recently Powell's approval to | > redistribute has been obtained. | > | > We beli...

...es and other documentation" -> "Ecdat::AverageIncomeModels". I've found other optimizers that will get around the problem in this case but none that performs as well as optim with many other problems. Thanks, Spencer Graves p.s. I've also tested bobyqa{minqa} or nloptr{nloptr}, recommended in a vignette in the lme4 package. These did better than optim in this example but worse in others I tried. ______________________________________________ R-devel at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel

...cdat::AverageIncomeModels". > > > I've found other optimizers that will get around the problem in this > case but none that performs as well as optim with many other problems. > > > Thanks, > Spencer Graves > > > p.s. I've also tested bobyqa{minqa} or nloptr{nloptr}, recommended in a > vignette in the lme4 package. These did better than optim in this example > but worse in others I tried. > > ______________________________________________ > R-devel at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo...

Hi i´m starting to work with free software (with free as in freedom) i've been working with GAMS (General Algebraic Modeling System) to solve static and dynamic optimization problems and non-linear large systems of equations i'm wondering if there is any free software and documentation on the subject, wether in R, Maxima, or something else i will appreciate any help and any references

...ay to know it is finished. As a derivative-free method, it is "not too bad", particularly in the nmk version in the dfoptim package. Indeed, I wish this version were put in optim() as the default, since it can deal with bounds constraints, though slightly less generally and less well than bobyqa or some other methods, and there are a couple of minor details it handles better than N-M in optim() that give it better performance and reliability. Readers should notice that there are lots of conditional statements above. It's a matter of selecting the right tool for the job. If you have lo...

Hi, Currently I am trying to solve a minimization problem using optim as method Nelder-Mead. However, Neldel-Mead needs many iterations until it finally converges. I have set $control.trace and $control.report such that I can see the value of the function at each iteration. I do see that I set the convergence criteria to strict in the sense that the function value does not change much. However,

[This email is either empty or too large to be displayed at this time]

...lock + nletter.std + (1 | subj) +? ... ?$ residuals?? : Named num [1:1742] 0.713 0.498 -0.361 -0.101 2.594 ... ? ..- attr(*, "names")= chr [1:1742] "1" "2" "3" "4" ... ?$ fitMsgs???? : chr(0) ?$ optinfo???? :List of 7 ? ..$ optimizer: chr "bobyqa" ? ..$ control? :List of 1 ? .. ..$ iprint: int 0 ? ..$ derivs?? :List of 2 ? .. ..$ gradient: num [1:4] 9.81e-06 -5.34e-06 -1.60e-05 7.06e-05 ? .. ..$ Hessian : num [1:4, 1:4] 245.9 28.5 3.3 -13.7 28.5 ... ? ..$ conv???? :List of 2 ? .. ..$ opt : int 0 ? .. ..$ lme4: list() ? ..$ fev...

Dear R-er, For a non-linear optimisation, I used optim() with BFGS method but it stopped regularly before to reach a true mimimum. It was not a problem with limit of iterations, just a local minimum. I was able sometimes to reach better minimum using several rounds of optim(). Then I moved to optimx() to do the different optim rounds automatically using "Nelder-Mead" and

Hi all, I am trying to rebuild the results of a study using a different data set. I'm using about 450 observations. The code I've written seems to work well, but I have some troubles minimizing the negative of the LogLikelyhood function using 5 free parameters. As starting values I am using the result of the paper I am rebuiling. The system.time of the calculation of the function is

Hola, Soy nuevo manejando R y no tengo mucha experiencia. Estoy intentando modelar una función que me relacione el nº de cebas (nº de presas que los padres traen a los pollos) con el tamaño de parche de un bosque (factor categórico; 2 niveles= grande y pequeño). Al ser un conteo (nº de cebas) he pensado utilizar familia= poisson con link= logarítmico. He construido un GLMM con: Nº de cebas