similar to: Competing with one's own work

>>>>> Duncan Murdoch >>>>> on Sun, 26 Mar 2023 12:41:03 -0400 writes: > On 26/03/2023 11:54 a.m., J C Nash wrote: >> A tangential email discussion with Simon U. has >> highlighted a long-standing matter that some tools in the >> base R distribution are outdated, but that so many >> examples and other tools may use

John, thanks for starting (or restarting) this thread.? I'd like to add to the discussion with another concrete example, about as simple as it gets, which may help focus at least part of this discussion. I have worked with Taylor Arnold to implement a method developed in Conover (1972) for Kolmogorov-Smirnov goodness-of-fit tests for discrete distributions (one-sample only).? We needed this

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

Hi, dear R users I am a newbie in R and I wantto use the method of meximum likelihood to fit a Weibull distribution to my survival data. I use "optim" as follows: optim(c(1, 0.25),weibull.like,mydata=mydata,method="L-BFGS-B",hessian = TRUE) My question is: how do I setup the constraints so that the two parametrs of Weibull to be pisotive? Or should I use other function

A tangential email discussion with Simon U. has highlighted a long-standing matter that some tools in the base R distribution are outdated, but that so many examples and other tools may use them that they cannot be deprecated. The examples that I am most familiar with concern optimization and nonlinear least squares, but other workers will surely be able to suggest cases elsewhere. I was the

On 26/03/2023 11:54 a.m., J C Nash wrote: > A tangential email discussion with Simon U. has highlighted a long-standing > matter that some tools in the base R distribution are outdated, but that > so many examples and other tools may use them that they cannot be deprecated. > > The examples that I am most familiar with concern optimization and nonlinear > least squares, but

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

Hi, I'm having some difficulties formulating this question. But what I want, is to extract the options associated with a parameter for a function. e.g. method = c("Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN") in the optim function. So I would like to have a vector with c("Nelder-Mead", "BFGS", "CG",

While working on 'random walk' applications, I got interested in optimizing noisy objective functions. As an (artificial) example, the following is the Rosenbrock function, where Gaussian noise of standard deviation `sd = 0.01` is added to the function value. fn <- function(x) (1+rnorm(1, sd=0.01)) * adagio::fnRosenbrock(x) To smooth out the noise, define another

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 am trying to estimate a gamma function using real data and I am getting the following error messages. When I set a lower limit; the error message is "L-BFGS-B needs finite values of fn" ? For other method the error message is: Error in optim(x = c(0.105286666666667, 0.3472275, 2.057625, 0.329675,? : ? non-finite finite-difference value [1] The codes works fine for simulated data

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

Dear all, I am using the optim() function which it stops with the following error messagge: error in optim(...) non-finite finite-difference value I was wondering if somebody might suggest me a way to fix it please. Thanks in advance to all of you. Kind regards, Tom __________________________________________________ [[alternative HTML version deleted]]

Hello! I am getting the following errors when running optim() [I tried optim() with 3 different methods as you can see]: Error in optim(c(0.66, 0.999, 0.064), pe, NULL, method = "L-BFGS-B") : objective function in optim evaluates to length 6 not 1 > out <- optim( c(0.66, 0.999, 0.064), pe, NULL, method = "Nelder-Mead") Error in optim(c(0.66, 0.999, 0.064),

With the exception of "L-BFGS-B", all of the other optim() methods return the value of the function when they are given a trivial function (i.e., one with no variable arguments) to optimize. I don't think this is a "bug" in L-BFGS-B (more like a response to an undefined condition), but it leads to a bug in stats4::mle -- a spurious error saying that a better fit has been

Dear R users, I am new to R. I would like to find *maximum likelihood estimators for psi and alpha* based on the following *log likelihood function*, c is consumption data comprising 148 entries: fn<-function(c,psi,alpha) { s1<-sum(for(i in 1:n){(c[i]-(psi^(-1/alpha)*(lag(c[i],-1))))^2* (lag(c[i],-1)^((-2)*(alpha+1)) )}); s2<- sum(for(m in 1:n){log(lag(c[m],-1)^(((2)*alpha)+2))});

I am using R 2.7.0 on a linux platform. I am trying to reproduce a 2002 example using lme from the nlme library. I want to change the otimizer from the default (nlminb) to optim. Specifically, this is what I am trying to do: R> library(nlme) R> library(car) # for data only R> data(Blackmoor) # from car R> Blackmoor$log.exercise <- log(Blackmoor$exercise + 5/60, 2) R>

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

Hello all, I am looking for some help in understanding how to change the way R optimizes the smoothing constant selection process for the HoltWinters function. I'm a SAS veteran but very new to R and still learning my way around. Here is some sample data and the current HoltWinters code I'm using: rawdata <- c(294, 316, 427, 487, 441, 395, 473, 423, 389, 422, 458, 411, 433, 454,

Dear R-users, I am trying to port to R something that I wrote in Matlab to perform model parameter optimization using the Nelder-Mead simplex method (fminsearch). I read the help on ?optim (which seems to be the way to go) as well as a bunch of posts on the topic, but I would like to make sure about something before I spend to much time trying to reproduce something that is not possible. The