similar to: Error: Gradient function might be wrong ----- in OPTIMX

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", >

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 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

Hello, I was asked to try the following code on R, gamma.mles function (xx,shape0,rate0) { n<- length(xx) xbar<- mean(xx) logxbar<- mean(log(xx)) theta<-c(shape0,rate0) repeat { theta0<- theta shape<- theta0[1] rate<- theta0[2] S<- n*matrix(c(log(rate)-digamma(shape)+logxbar,shape/rate-xbar),ncol=1) I<- n*matrix(c(trigamma(shape),-1/rate,-1/rate,shape/rate^2),ncol=2)

Hi, I am trying to determine the MLE of the following function: http://r.789695.n4.nabble.com/file/n3317341/untitled.bmp I have defined both parts of the equation as separate functions and looped over the t and G values to get summations of each part. The lamda function has 3 unknowns which I am trying to determine using MLE bub tin order to try and get the overall function working these

Hi, I've noticed I get different results fitting a function to some data on my laptop to when I do it on my computer at work. Here's a code snippet of what I do: ##------------------------------------------------------------------ require(circular) ## for Bessel function I.0 ## Data: dd <- c(0.9975948929787, 0.9093316197395, 0.7838819026947, 0.9096108675003, 0.8901804089546,

Hello Following some standard textbooks on ARMA(1,1)-GARCH(1,1) (e.g. Ruey Tsay's Analysis of Financial Time Series), I try to write an R program to estimate the key parameters of an ARMA(1,1)-GARCH(1,1) model for Intel's stock returns. For some random reason, I cannot decipher what is wrong with my R program. The R package fGarch already gives me the answer, but my customized function

In the following code I have created the posterior density for a Bayesian survival model with four parameters. However, when I try to use the adapt function to perform integration in four dimensions (on my old version of R I get an error message saying that I have applied a non-function, although the function does work when I type kernel2(param0, theta0), or on the newer version of R the computer

Dear R-help members Since this morning (5/2/2025), I get an error when I try to load optimx package. I solve it and I send my solution but if someone has a better idea or understand what's happened, I will be most happy to know. MacOSX 15.3 R 4.4.2 nlopt installed using brew install nlopt Here are the steps to produce the error and a solution to solve it. Some parts are in French. I

Hi, That looks like the dependency on nlopt for nloptr (used by several of the solvers) was somehow missing. There is SUPPOSED to be a check of what is installed and a warning issued, but possibly one got missed. Even some base packages do sometimes need the external libraries manually installed, and I find I'm doing that after various OS updates or resets when the long term support runs out.

I try to do a test for dirichlet process for Multivariate normal, but Winbugs always says "expected multivariate node", does that mean I miss something at initialization? I will really appreciate the help to solve this problem Here is the R code, and Winbugs code. model { for(i in 1:N){ y[i,1:2] ~ dmnorm(mu[i,],tau[i,,]) S[i] ~ dcat(pi[]) mu[i,1:2] <- mu.star[S[i],]

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, I am trying to estimate 4 parameters of a non-linear model using nls. My model function is a Fourier integral and is very expensive to calculate. I get the following error: > theta0 <- c(0.045, 1.02*10^(-4), 0.00169, 5.67*10^(-4)) > res <- nls(log(y) ~ log(model(theta,r,t)), data=dataModel, + start=list(theta=theta0), trace=TRUE, + control=nls.control(tol=1e-2))

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

Hi, I am looking for some help regarding the use of the mle function. I am trying to get mle for 3 parameters (theta0, theta1 and theta2) that have been defined in the the log-likelihood equation as theta0=theta[1], theta1=theta[2] and theta2=theta[3]. My R code for mle is: mle(Poisson.lik, start=list(theta=c(20,1,1), method="Nelder-Mead", fixed=list(w=w, t1=t1, t2=t2)) But I keep

Dear R-helper, I am trying to do maximum likelihood estimation in R. I use the "optim" function. Since I have no prior information on the true values of the parameters, I just randomly select different sets of starting values to feed into the program. Each time, I get the following error message: Error in optim(theta0, lf, method = "BFGS", hessian = T, Y = Y, X = X, :

Oh, sorry; I changed signs in the model, fitting theta0 + theta1*exp(theta2*x) So for theta0 - theta1*exp(-theta2*x) use theta1= -.exp(-1.8) and theta2 = +.055 as starting values. -- Bert On Sun, Aug 20, 2023 at 11:50?AM Paul Bernal <paulbernal07 at gmail.com> wrote: > Dear Bert, > > Thank you so much for your kind and valuable feedback. I tried finding the > starting

Dear Bert, Thank you for your extremely valuable feedback. Now, I just want to understand why the signs for those starting values, given the following: > #Fiting intermediate model to get starting values > intermediatemod <- lm(log(y - .37) ~ x, data=mod14data2_random) > summary(intermediatemod) Call: lm(formula = log(y - 0.37) ~ x, data = mod14data2_random) Residuals: Min

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.

Basic algebra and exponentials/logs. I leave those details to you or another HelpeR. -- Bert On Sun, Aug 20, 2023 at 12:17?PM Paul Bernal <paulbernal07 at gmail.com> wrote: > Dear Bert, > > Thank you for your extremely valuable feedback. Now, I just want to > understand why the signs for those starting values, given the following: > > #Fiting intermediate model to get