similar to: Query: Could documentation include modernized references?

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 matter that some tools in the base R distribution are outdated, but that 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

>>>>> 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 base R distribution are outdated, but that so many 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

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",

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

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

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

I'll not be able to comment on the use of C like this, but will warn that I wrote the routines that became Nelder-Mead, CG, and BFGS in optim() in the mid 1970s. CG never did as well as I would like, but the other two routines turned out pretty well. However, in nearly 40 years, there are a few improvements, particularly in handling bounds and masks (fixed parameters). For all-R routines see

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

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 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>

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,

I have a Master's student working on a project which involves estimating parameters of a certain model via maximum likelihood, with the maximization being done via optim(). A phenomenon has occurred which I am at a loss to explain. If we use certain pairs of starting values for optim(), it simply returns those values as the ``optimal'' values, although they are definitely not

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))});

Hi! I'm trying to figure out how to use optim... I get some really strange results, so I guess I got something wrong. I defined the following function which should be minimized: errorFunction <- function(localShifts,globalShift,fileName,experimentalPI,lambda) { lambda <- 1/sqrt(147) # error <- abs(errHuber(localShifts,globalShift, #

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, 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

Dears useRs, I am using the mle function but this gives me the follow erros that I don't understand. Perhaps there is someone that can help me. thank you for you atention. Bernardo. > erizo <- read.csv("Datos_Stokes_1.csv", header = TRUE) > head(erizo) EDAD TALLA 1 0 7.7 2 1 14.5 3 1 16.9 4 1 13.2 5 1 24.4 6 1 22.5 > TAN <-

No, this is not about Rcpp, but a comment in that overly long discussion raised a question that has been in my mind for a while. This is that one may have work that is used in R in the base functionality and there are improvements that should be incorporated. For me, this concerns the BFGS, Nelder-Mead and CG options of optim(), which are based on the 1990 edition (Pascal codes) of my 1979 book

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,

Hi, I'm having a problem converting a Matlab program into R. The R code works almost all the time, but about 4% of the time R's optim function gets stuck on a local minimum whereas matlab's fminsearch function does not (or at least fminsearch finds a better minimum than optim). My understanding is that both functions default to Nelder-Mead optimization, but what's different about