similar to: Passing parameters to 'optim' fn function

Dear all, when using optim() for a function that uses the parent environment, I see the following unexpected behavior: makeFn <- function(){ ??? xx <- ret <- NA ??? fn <- function(x){ ?????? if(!is.na(xx) && x==xx){ ?????????? cat("x=", xx, ", ret=", ret, " (memory)", fill=TRUE, sep="") ?????????? return(ret) ?????? } ?????? xx

On 03/05/2019 10:31, Serguei Sokol wrote: > On 02/05/2019 21:35, Florian Gerber wrote: >> Dear all, >> >> when using optim() for a function that uses the parent environment, I >> see the following unexpected behavior: >> >> makeFn <- function(){ >> ???? xx <- ret <- NA >> ???? fn <- function(x){ >> ??????? if(!is.na(xx)

Yes, I think you are right. I was at first confused by the fact that after the optim() call, > environment(fn)$xx [1] 10 > environment(fn)$ret [1] 100.02 so not 9.999, but this could come from x being assigned the final value without calling fn. -pd > On 3 May 2019, at 11:58 , Duncan Murdoch <murdoch.duncan at gmail.com> wrote: > > Your results below make it look like a

Optim's Nelder-Mead works correctly for this example. > optim(par=10, fn=fn, method="Nelder-Mead") x=10, ret=100.02 (memory) x=11, ret=121 (calculate) x=9, ret=81 (calculate) x=8, ret=64 (calculate) x=6, ret=36 (calculate) x=4, ret=16 (calculate) x=0, ret=0 (calculate) x=-4, ret=16 (calculate) x=-4, ret=16 (memory) x=2, ret=4 (calculate) x=-2, ret=4 (calculate) x=1, ret=1

Dear R-helpers, The function optim implements algorithms that I would like to use. I have function implemented in R, which given the parameters of which minimization is to take place returns a scalar as well as the gradient. Unfortunately optim requires two function _fn_ and _gr_ where fn returns the function value and gr the gradient. Splitting my function in two functions would be easy,

Dear R users, I am trying to figure out the control parameter in "optim," especially, "fnscale" and "parscale." In the R docu., ------------------------------------------------------ fnscale An overall scaling to be applied to the value of fn and gr during optimization. If negative, turns the problem into a maximization problem. Optimization is performed on

I have constructed the function mml2 (below) based on the likelihood function described in the minimal latex I have pasted below for anyone who wants to look at it. This function finds parameter estimates for a basic Rasch (IRT) model. Using the function without the gradient, using either nlminb or optim returns the correct parameter estimates and, in the case of optim, the correct standard

Full_Name: Christina Merz Version: R version 2.5.0 (2007-04-23) OS: mingw32 Submission from: (NULL) (213.70.209.132) R> version _ platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status

Dear R users When I use OPTIM with BFGS, I've got a significant result without an error message. However, when I use OPTIMX with BFGS( or spg), I've got the following an error message. ---------------------------------------------------------------------------------------------------- > optimx(par=theta0, fn=obj.fy, gr=gr.fy, method="BFGS", >

Hello R Help, I am trying solve an MLE convergence problem: I would like to estimate four parameters, p1, p2, mu1, mu2, which relate to the probabilities, P1, P2, P3, of a multinomial (trinomial) distribution. I am using the mle2() function and feeding it a time series dataset composed of four columns: time point, number of successes in category 1, number of successes in category 2, and

Dear all, I am trying to optimize a logistic function using optim, inside the following functions: #Estimating a and b from thetas and outcomes by ML IRT.estimate.abFromThetaX <- function(t, X, inits, lw=c(-Inf,-Inf), up=rep(Inf,2)){ optRes <- optim(inits, method="L-BFGS-B", fn=IRT.llZetaLambdaCorrNan, gr=IRT.gradZL, lower=lw, upper=up, t=t, X=X)

Hi everyone, I am currently using the function optim() to maximize/minimize functions and I would like to see more output of the optimization procedure, in particular the numerical gradient of the parameter vector during each iteration. The documentation of optim() describes that the trace parameter should allow one to trace the progress of the optimization. I use the following command:

Dear R user fellows, I would like to ask you about the package 'rgenoud' which is a genetic optimization tool. I ran the function 'genoud' with two variables to be minimized by the following command. result<-genoud(fn,nvars=2,starting.values=c(0.5,0), pop.size=1000, max.generations=10, wait.generations=3) Then, I had the following error message. Error in

There seems to exist peculiar cases where optim does not take care of constraints on the parameters to be optimized over. The call to optim is of the form opt <- optim(cp, fn=sn.dev, gr=sn.dev.gh, method="L-BFGS-B", lower=c(-Inf, 1e-10, -0.99527), upper=c( Inf, Inf, 0.99527), control=control, X=X, y=y, hessian=FALSE) The code has worked fine

Dear R-devel During the last half a year I have several times encountered the following problem with optim() when using method= "L-BFGS-B". The function return a value which is clearly not the maximum (seen from printing the value each time the function is called). Some output is shown below. A few things I have observed (as I remember it): a. The problem seems to occur when the

Dear R user I follow the steps defined in Modern applied statistics page(453) to use optim. However, when I run the following code the parameters seems way off and the third parameter(p3) stayed as the initial value. below is the code: ## data da=c(418,401,416,360,411,425,537,379,484,388,486,380,394,363,405,383,392,363,398,526) ### initial values pars=c(392.25, 507.25, 0.80)

Hello, I'm trying to fminimize the following problem: You have a data frame with 2 columns. data.input= data.frame(state1 = (1:500), state2 = (201:700) ) with data that partially overlap in terms of values. I want to minimize the assessment error of each state by using this function: err.th.scalar <- function(threshold, data){ state1 <- data$state1 state2 <- data$state2

I'm trying to create a graph where different cells of a grid (a shapefile) will be painted with a color share scale, where the most easy way is to use gray(). Can I somehow get a vector (gradient) of colors, a vector of colors with other methods but gray()? I'm doing this until now quad_N_sp <-

Dear All, I am trying to solve the optimization problem below, but I am always getting the following error: Error in optim(rep(20, nvar), f, gr, method = "L-BFGS-B", lower = rep(0, : L-BFGS-B needs finite values of 'fn' Any ideas? Thanks in advance, Paul ----------------------------------------------- k <- 10000 b <- 0.3 f <- function(x) { n <- length(x)

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