similar to: Nealder Meade and nlm

As the original coder (in mid 1970s) of BFGS, CG and Nelder-Mead in optim(), I've been pushing for some time for their deprecation. They aren't "bad", but we have better tools, and they are in CRAN packages. Similarly, I believe other optimization tools in the core (optim::L-BFGS-B, nlm, nlminb) can and should be moved to packages (there are already 2 versions at least of LBFGS

I have a dataset that I am running non-linear regression on via the following code: Hill <- function(E0,Em,C50,g,C){ # # Hill is the hill interaction function. # # E0 Represents the minimum interaction Effect # # Em Represents the Maximum Interaction Effect # # C50 represents the concentration at which 50% of the effect occurs. # # gamma represents the cooperativity of the

On Mon, Mar 4, 2019 at 5:01 PM J C Nash <profjcnash at gmail.com> wrote: > As the original coder (in mid 1970s) of BFGS, CG and Nelder-Mead in > optim(), I've > been pushing for some time for their deprecation. They aren't "bad", but > we have > better tools, and they are in CRAN packages. Similarly, I believe other > optimization > tools in the core

I concur with Avraham that capabilities need to be ensured e.g., in recommended packages. I should have mentioned that. My concern is that the core should be focused on the programming language aspects. The computational math and some of the more intricate data management could better be handled by folk outside the core. JN On 2019-03-04 9:12 a.m., Avraham Adler wrote: > On Mon, Mar 4, 2019

I'm looking for an example of a simple R script that impliments a contrained nonlinear function using nlm or optim. I'm not exactly sure how to impliment the constraints within the objective function that is passed to nlm/optim. obj.func <- function( p ) { x(p) <- unconstrained obj function value if( constraint1 > something ) { obj.func <- x(p) } else {

Dear R People: I am trying to duplicate the example from Dennis and Schnabel's "Numerical Methods for Unconstrained Optimization and Nonlinear Equations", which starts on page 149. My reason for doing so: to try to understand the "nlm" function. Here is the function: >mfun1 function(x) { z <- matrix(0,nrow=2,ncol=1) z[1,1] <- x[1]^2 + x[2]^2 -

Dear R users, I'm trying to estimate the matrix of regression parameters. I need to do it numerically, so I used optim and nls. I got the initial parameter estimates from least squares, and input them into those functions. But when I run the optim function, it stops in 30 seconds and shows 'convergence=1'. And if I use the nlm function, then it runs for a while, and finally stops

Hi, It sometimes happens that some packages get included to R like for example the parallel package. I was wondering if there is a process to decide whether or not to include a package in the core implementation of R? For example, why not include the Rcpp package, which became for a lot of user the main tool to extend R? What is our view on the (not so well known) dotCall64 package which is an

Hi, I have written an R translation of C.T. Kelley's Matlab version of the Nelder-Mead algorithm. This algorithm is discussed in detail in his book "Iterative methods for optimization" (SIAM 1999, Chapter 8). I have tested this relatively extensively on a number of smooth and non-smooth problems. It performs well, in general, and it almost always outperforms optim's

Hi Thomas, This may be old news at this point, but I had 100% identical behavior to yours when I tried virtx7-44-testing. After looking around a bit I tried "virtx7-44-candidate", in a sibling directory to virtx7-44-testing. It has a more recent date, so I tried with that and it works fine. It resolves the issues listed in your email from 7/7/2015. I am now looking for a

Hello, I am trying to compute MLE for non-Gaussian AR(1). The error term follows a difference poisson distribution. This distribution has one parameter (vector[2]). So in total I want to estimate two parameters: the AR(1) paramter (vector[1]) and the distribution parameter. My function is the negative loglikelihood derived from a mixing operator. f=function(vector)

Dear useRs, I am analysing the behaviour of MLE for the two parameters of a kind of exponential distribution, leaving as initial values the estimators moments produced by the variation coefficient. I do using simulations, giving them an accountant, r. But running my codes remains a problem with the nlm function. To review details wearing On one of the lines put status

Dear expeRts, first of all I'd like to thank you for the quick help on my last which() problem. Here is another one I could not tackle: I have data on an absorption measurement which I want to fit with an voigt profile: fn.1 <- function(p){ for (i1 in ilong){ ff <- f[i1] ex[i1] <- exp(S*n*L*voigt(u,v,ff,p[1],p[2],p[3])[[1]]) } sum((t-ex)^2) } out <-

Dear list,   I am looking for an R-only implementation of a Nelder-Mead process that can find local maxima of a spatially distributed variable, e.g. height, on a spatial grid, and outputs the coordinates of the new point during each evaluation. I have found two previous threads about this topic, and was wondering if something similar has been implemented since those messages were posted.   Thank

Hi, Where can I find the source code for nlm()? I dowloaded the R2.8.1.tar.gz file and looked at all the .c and .f files, but couldn't find either nlm.c or nlm.f There is an nlm.r file, but that is not useful. Thanks for any help, Ravi. ---------------------------------------------------------------------------- ------- Ravi Varadhan, Ph.D. Assistant Professor, The Center on Aging

Hi, I tried to compute maximum likelihood under gamma distribution, using nlm function. The code is this: __BEGIN__ vsamples<- c(103.9, 88.5, 242.9, 206.6, 175.7, 164.4) mlogl <- function(alpha, x) { if (length(alpha) > 1) stop("alpha must be scalar") if (alpha <= 0) stop("alpha must be positive") return(- sum(dgamma(x, shape = alpha, log = TRUE)))

Dear R users, I am trying to find the mle that involves integration. I am using the following code and get an error when I use the nlm function d<-matrix(c(1,1,0,0,0,0,0,0,2,1,0,0,1,1,0,1,2,2,1,0),nrow=10,ncol=2) h<-matrix(runif(20,0,1),10) integ<-matrix(c(0),nrow=10, ncol=2) ll<-function(p){ for (k in 1:2){ for(s in 1:10){ integrand<-function(x)

Greetings! I want to follow the evolution of a Nelder-Mead function minimisation (a function of 2 variables). Hence each simplex will have 3 vertices. Therefore I would like to have a function which can output the coordinates of the 3 vertices after each new simplex is generated. However, there seems to be no way (which I can detect) of extracting this information from optim() (the

Hi R-Gurus, I've been cutting along quite nicely with nlm, until I threw in the following condition in the function that nlm is minimising: if (((term*bexp) < 0.0001)) { #warning(term*bexp, "=term*bexp",psi,"=psi") theta<-2000 } Now when I run this function anywhere else, there is no problem, whether or the if's condition is met. When

Hi all, I have been trying the nlm function but received an error message which reads: Error in nlm(intensities ~ f, c(epsilon.spec.start, epsilon.unspec.start, : invalid function value in 'nlm' optimizer The message is generated somewhere in the compiled part, apparently within the function static void fcn(int n, const double x[], double *f, function_info *state) where a jump