similar to: gradient function in OPTIMX

Dear R users I am trying to use OPTIMX(OPTIM) for nonlinear optimization. There is no error in my code but the results are so weird (see below). When I ran via OPTIM, the results are that Initial values are that theta0 = 0.6 1.6 0.6 1.6 0.7. (In fact true vales are 0.5,1.0,0.8,1.2, 0.6.) -------------------------------------------------------------------------------------------- >

Dear R users When I use OPTIMX with BFGS, I've got the following error message. ----------------------------------------------------------------- > optimx(par=theta0, fn=obj.fy, gr=gr.fy, method="BFGS") Error: Gradient function might be wrong - check it! ----------------------------------------------------------------- So, I checked and checked my gradient function line by

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 Reuben, I am puzzled to note that the gradient check in "optimx" does not work for you. Can you send me a reproducible example so that I can figure this out? John - I think the best solution for now is to issue a "warning" rather than an error message, when the numerical gradient is not sufficiently close to the user-specified gradient. Best, Ravi.

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

This message is about a curious difference in timing between two ways of computing the same function. One uses expm, so is expected to be a bit slower, but "a bit" turned out to be a factor of >1000. The code is below. We would be grateful if anyone can point out any egregious bad practice in our code, or enlighten us on why one approach is so much slower than the other. The problem

I have not included the previous postings because they came out very strangely on my mail reader. However, the question concerned the choice of minimizer for the zeroinfl() function, which apparently allows any of the current 6 methods of optim() for this purpose. The original poster wanted to use Newton-Raphson. Newton-Raphson (or just Newton for simplicity) is commonly thought to be the

Dear R helpers, I need to estimate a probit model with box constraints placed on several of the model parameters. I have the following two questions: 1) How are the standard errors calclulated in glm (family=binomial(link="probit")? I ran a typical probit model using the glm probit link and the nlminb function with my own coding of the loglikehood, separately. As nlminb does not

Hi R-help! I'm trying to understand how R's nlm function updates its estimate of the Hessian matrix. The Dennis/Schnabel book cited in the references presents a number of different ways to do this, and seems to conclude that the positive-definite secant method (BFGS) works best in practice (p201). However, when I run my code through the optim function with the method as "BFGS",

With some chagrin after spending a couple of hours trying to debug a script, I realized I had typed in something like ans<-optimx(start, myfn, mygr, lower<-lo, upper=up) that is, the "<-" rather than "=". The outcome on my machine was a non-obvious error several layers deep in the call stack. For info, optim() seems to stop much more quickly. The error is

Dear All, I am running a simulation to obtain coverage probability of Wald type confidence intervals for my parameter d in a function of two parameters (mu,d). I am optimizing it using "optim" method "L-BFGS-B" to obtain MLE. As, I want to invert the Hessian matrix to get Standard errors of the two parameter estimates. However, my Hessian matrix at times becomes

Dear all, I want to min "integrate( (p1*dnorm+p2*dnorm+p3*dnorm)^(1.3))" for p, mu, and sigma. So, I have to estimate 8 parameters(p3=1-p1-p2). I got this warning-"Error in integrate(numint, lower = -Inf, upper = Inf) : non-finite function value." My questions are How could I fix it? I tried to divide into several intervals and sum up, but I got same message. My code is

This is mainly a reminder to others developing R packages to be careful not to supply control list items that are not used by the called package. Optimx is a wrapper package that aims to provide a common syntax to a number of existing optimization packages. Recently in extending optimx package I inadvertently introduced a new control for optimx which is NOT in any of the wrapped optimization

Dear R users, I used to "OPTIM" to minimize the obj. function below. Even though I used the true parameter values as initial values, the results are not very good. How could I improve my results? Any suggestion will be greatly appreciated. Regards, Kathryn Lord #------------------------------------------------------------------------------------------ x = c(0.35938587,

Dear R users, I used to "OPTIM" to minimize the obj. function below. Even though I used the true parameter values as initial values, the results are not very good. How could I improve my results? Any suggestion will be greatly appreciated. Regards, Kathryn Lord #------------------------------------------------------------------------------------------ x = c(0.35938587,

I'll not copy all the previous material on this thread to avoid overload. The summary is that all the methods Spencer has tried have some issues. The bad news: This is not uncommon with optimization methods, in part because the problems are "hard", in part because getting them implemented and linked to an interfacing approach like R is very tedious and prone to omissions and

Prof Nash, Prof Galanos Is it possible to use a generic code stub in front of packages that use optimx to improve optimx use or curtail it according to the requirements? Best Regards Amit +91 7899381263 ________________________________________________________________________ Please request Skype as available 5th Year FPM (Ph.D.) in Finance and Accounting Area Indian Institute

Dear all, this is my first time, and just begin to use R. But I've a question about optimization using optimx library. It could sound stupid by I'm a bit affraid with the problem, because anything I try, anything Error. The procedure was: optimx(par="10,71,1",fn=(Prey*Provisioning)/Risk, control=list("maximize)) well the problem lies in the initial value parameters, again

Does anybody know of a function that implements the derivative (gradient) of the multivariate normal density with respect to the *parameters*? It?s easy enough to implement myself, but I?d like to avoid reinventing the wheel (with some bugs) if possible. Here?s a simple example of the result I?d like, using numerical differentiation: library(mvtnorm) library(numDeriv) f=function(pars, xx, yy)

In re-factoring my optimx package, I'm finding that the pdf output has some lines that are outside the margins (and there are warnings in R CMD check optimx). Clearly I can fix this by editing the tex file that is generated, but the build infrastructure would still get things wrong in automatic processing. So that gives rise to 3 questions: 1) How do I adjust the DESCRIPTION file to avoid too