similar to: optim: problem with additional arguments (PR#493)

In the documentation for 'optim' it gives the following function: fr <- function(x) { ## Rosenbrock Banana function x1 <- x[1] x2 <- x[2] 100 * (x2 - x1 * x1)^2 + (1 - x1)^2 } optim(c(-1.2,1), fr) When I run this code I get: $par [1] 1.000260 1.000506 I am sure I am missing something but why isn't 1,1 a better answer? If I plug 1,1 in the function it seems

Hi, I think that there something wrong with the 'constrOptim' max/minimization function because she doesn?t send extra arguments to 'optim' call. Fact: When I use optim in a f(x,theta)-like function, everything goes ok. But using constrOptim with the same function leads to error... Proof: Make a small change in the 'Rosenbrock Banana function' (taken from the Examples

How can I use a function with some additional input parameters in constrOptim? For example, something like fr <- function(x,a) { ## Rosenbrock Banana function x1 <- x[1] x2 <- x[2] a * (x2 - x1 * x1)^2 + (1 - x1)^2 } where the optimum is to be found w.r.t. x. Calling optim(c(-1.2,1), fr, NULL, a=100) works as expected, but I fail to provide the a=100 in the constrained case:

For a solution that does not require any change to the original function being optimized, the following one-liner could be used, which converts existing functions to functions that return only the first element: returnFirst <- function(fun) function(...) do.call(fun,list(...))[[1]] Example: fr <- function(x) { ## Rosenbrock Banana function x1 <- x[1] x2 <- x[2] ans

Hi, I would like to know how to capture the console output from running an algorithm for further analysis. I can capture this using capture.output() but that yields a character vector. I would like to extract the actual numeric values. Here is an example of what I am trying to do. fr <- function(x) { ## Rosenbrock Banana function on.exit(print(f)) x1 <- x[1] x2 <- x[2]

Okay, looking at the docs, then it's not a bug, since the "..." argument is not actually documented as "other arguments passed to f or grad". However, that *is* how it's document in `optim', so one can see how this might cause some confusion. Now, it's not clear to me which other arguments need to be passed to `optim' except perhaps `hessian'. Am

Dear all, There appears to be a bug in how constrOptim handles ... arguments that are suppose to be passed to optim, according to the documentation. This means you can't get the hessian to be returned, for example (so this is a real problem, and not just a question of mistaken documentation). Looking at the code, it appears that a call to the user-defined f includes the ..., when the ...

I've moved this from r-help to r-bugs. If you reply, please be careful that replies go to the right place: r-bugs if your comment is specifically about the bug (and it contains the PR# in the subject that will be added when this is cc'd to r-devel), r-devel if general discussion, not both. On Wed, 14 Jul 2004 10:01:45 -0400, "Roger D. Peng" <rpeng@jhsph.edu> wrote :

GENERAL REFERENCE ON NONLINEAR OPTIMIZATION? What are your favorite references on nonlinear optimization? I like Bates and Watts (1988) Nonlinear Regression Analysis and Its Applications (Wiley), especially for its key insights regarding parameter effects vs. intrinsic curvature. Before I spent time and money on several of the refences cited on the help pages for "optim",

Dear R users I have tried to install the optimx but met problems. I have gone to the website you suggested: https://r-forge.r-project.org/R/?group_id=395 and tried to install it with the following method: install.packages("optimx", repos="http://R-Forge.R-project.org") I have received the following information: package 'numDeriv' successfully unpacked and MD5

I think SANN method in optim() is failing to report that it has not converged. Here is an example genrose.f<- function(x, gs=NULL){ # objective function ## One generalization of the Rosenbrock banana valley function (n parameters) n <- length(x) if(is.null(gs)) { gs=100.0 } fval<-1.0 + sum (gs*(x[1:(n-1)]^2 - x[2:n])^2 + (x[2:n] - 1)^2) return(fval) }

Hi I am trying to recover the hessian of a problem optimised with box-constraints. The problem is that in some cases, my estimates are very close to the boundary, which will make optim(..., hessian=TRUE) or optimHessian() fail, as they do not follow the box-constraints, and hence estimate the function in the unfeasible parameter space. As a simple example (my problem is more complex though,

Dear all, I have used optim a lot in contexts where it would useful to be able to iterate function myfun that, in addition to the primary objective to be minimized ('minimize.me'), could return other values such as alternative metrics of the minimization, informative intermediate values from the calculations, etc. myfun <- function() { ... return(list(minimize.me = minimize.me, R2 =

Dear all, I have found a bug in nlm() and would like to submit a report on this. Since nlm() is in the stats-package, which is maintained by the R Core team, bug reports should be submitted to R's Bugzilla. However, I'm not a member of Bugzilla. Could anyone be so kind to add me to R's Bugzilla members or let me know to whom I should send the bug report? Thank you in advance. Kind

Hi, my name is Marcel R. Lopes. My problem is, I made a code to calculate the estimates of a Cox model with random effects. Used to optimize the R command for this. The estimates were calculated correctly, but the Hessian matrix does not have good values. The same thing was done in SAS and gave good results for the Hessian Matrix. Where is the problem in R? As the Hessian is calculated?. How

Dear All, I am in the process of translating some of my functions into C and one such function sometimes get stuck when I applied optim. The pure R codes work fine but the translated C codes occasionally get stuck and I like to find out why. Is it possible to extract the parameters of function calculated in each step of the Nelder-Simplex algorithm, the trace option only produce the value of

Dear Friends, I'm using the optim command to maximize a likelihood function. My optim command is as follows estim.out <- optim(beta, loglike, X=Xmain, Y=Y, hessian=T, method="BFGS", control=c(fnscale=-1, trace=1, REPORT=1)) Setting the report=1, gives me the likelihood function value (if i'm correct) at each step. The output from running this is as follows initial value

I am trying to maximise a complicated loglikelihood function with the "optim" command. Is there some way to get to know the estiamtes at each iteration? When I put "control=list(trace=TRUE)" as an option in "optim", I just got the initial and final values of the loglikelihood, number of iterations and whether the routine has converged or not. I need to know the

Buenos días.Soy más bien nuevo en R, necesito saber ¿cómo encuentro el valor que maximiza una función? Existe alguna función de R que hace eso.Muchas gracias [[alternative HTML version deleted]]

With wine 1-3-7 I have the following errors when running wine msiexec /i stdp11.msi to install office 2003 err:msi:remove_tracked_tempfiles failed to delete L"C:\\users\\fra\\Temp\\msi28.tmp\\Microsoft.Office.Interop.Word.dll" err:msi:remove_tracked_tempfiles failed to delete L"C:\\users\\fra\\Temp\\msi26.tmp\\Microsoft.Office.Interop.Publisher.dll"