similar to: ui and ci explanatory documentation

I''m looking for a R function which can maximise this logliklihood function, under the constraits a>0 e b>0 f<-function(param){ a<-param[1] b <-param[2] log(prod)-(a*s2)-(b*s)-n*log(1-((0.5*b/sqrt(a))*(exp((b^2)/(4*a)))*((sqrt(pi ))*(1-pnorm(-b/(2*sqrt(a)), mean=0, sd=1)))))} I''ve tried maxlik constrOptim e donlp2 but without success. Thanks so

Hi, I am a bit stuck on specifying ui and ci. I have read Lange's book ((1999) Numerical Analysis for Statisticians) to his approach and unfortunately his descriptions were not helpful for me. Here is what I have: ui <- rbind(c(0, -1, 0), c(0, 0, -1)) ci <- c(0, -1, -1)) theta <- c(0.5, 0.5, 0.1) My goal is to feed these into constrOptim

Hello All, I am stuck in the following problem. Cexpt=c(0,25,50,100,150,300,250,125,40) t=c(0,0.2,0.4,0.6,1,4,8,12,24) theta0= vector of 6 parms (My initial parameter) A=Constraint matrix (hopefully 6*6) B= Constraint vector of length 6) Cfit=function(t,theta){ J(t)=function(theta,t) Cfit=function(J(t),constant) return(Cfit) } loss=function(theta,t,Cexpt) {

Hi, Is there any way to get the hessian matrix from the "constrOptim" function without supplying gradient function? Thanks. --------------------------------- Bring words and photos together (easily) with [[alternative HTML version deleted]]

After a few days of work, I think I nearly have it. Unfortunately, theta is unchanged after I run this (as a script from a file). I thought that theta would contain the fitted parameters. The goal here is to find the least squares fit according to the function defined as "rss" subject to the constraints defined as ui and ci. I defined ui and ci to (hopefully) force par2 and par3

On Wednesday 18 June 2008 01:30, Evan Cheng wrote: > On Jun 17, 2008, at 1:36 PM, David Greene wrote: > > Can someone explain the Constraints system in X86*.td? > > > > For example: > > > > let Constraints = "$src1 = $dst" > > > > This replaces isTwoAddress (according to svn logs), which I gather > > is how > > two-address

I've moved this from r-help to r-bugs. If you reply, please be careful that replies go to the right place: r-bugs if your comment is specifically about the bug (and it contains the PR# in the subject that will be added when this is cc'd to r-devel), r-devel if general discussion, not both. On Wed, 14 Jul 2004 10:01:45 -0400, "Roger D. Peng" <rpeng@jhsph.edu> wrote :

Hi Everyone, I'm trying to minimize a function using constrOptim with the simulated annealing method SANN. If I understand constrOptim well, it basically passes most of its arguments to optim while somehow enforcing the constraints. My problem is, that since SANN does not need gradients, when using optim with SANN, the gr argument of optim is used to specify a function to create the next

Dear List, I have the code below, where I am using the constrained optimisation package, 'constrOptim.nl' to find the values of two values, b0 and b1. I have no problems when I enter further variable information DIRECTLY into the functions, fn, and heq. In this instance I require fn to have -0.0075 appended to it, and in the case of heq, h[1] has -0.2. library(alabama)

Hi, I'm having trouble using the constrOptim function to generate the 9-component vector argmin of the function ELfsds: ELfsds <- function(pvechat){ LG=0 for(i in 1:9){ LG=LG+log(pvechat[i]) } return(-LG) } with accompanying gradient function: gradfunc <- function(thetavec){ g=1/(9*thetavec) return(g) } The constraints on the optimization problem are: 1 - components of

Okay, looking at the docs, then it's not a bug, since the "..." argument is not actually documented as "other arguments passed to f or grad". However, that *is* how it's document in `optim', so one can see how this might cause some confusion. Now, it's not clear to me which other arguments need to be passed to `optim' except perhaps `hessian'. Am

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

Hi Everyone, I'm trying to minimize a function using constrOptim with the simulated annealing method SANN. If I understand constrOptim well, it basically passes most of its arguments to optim while somehow enforcing the constraints. My problem is, that since SANN does not need gradients, when using optim with SANN, the gr argument of optim is used to specify a function to create the next

I have a function myFunction(beta,x) where beta is a vector of coefficients and x is a data frame (think of it as a matrix). I want to optimize the function myFunction() by ONLY changing beta, i.e. x stays constant, with 4 constraints. I have the following code (with a separate source file for the function): rm(list=ls()) source('mySourceFile')

Dear List, I am using constrOptim to solve the following fr1 <- function(x) { b0 <- x[1] b1 <- x[2] ((1/(1+exp(-b0+b1))+(1/(1+exp(-b0)))+(1/(1+exp(-b0-b1)))))/3 } As you can see, my objective function is ((1/(1+exp(-b0+b1))+(1/(1+exp(-b0)))+(1/(1+exp(-b0-b1)))))/3 and I would like to solve for both b0 and b1. If I were to use optim then I would derive the gradient of the

Dear all, There appears to be a bug in how constrOptim handles ... arguments that are suppose to be passed to optim, according to the documentation. This means you can't get the hessian to be returned, for example (so this is a real problem, and not just a question of mistaken documentation). Looking at the code, it appears that a call to the user-defined f includes the ..., when the ...

Dear Helper, I used "constrOptim.nl" and got the value of par. The estimations looks good even if the number of iterations is only 16. But the values of hessian and convergence are both "NULL". I tested the objective function and gradient function by "optim" and didn't see any problem there. With these functions, "optim" gives the convergence value

Dear All, I am having trouble in using R function "constrOptim" to do constraint optimization. It seems that "constrOptim" calls function "optim" when it does the optimization, and "optim" allows us to set "method" to be "SANN" if we want to use simulated annealing. In "optim", the function allows us to set gradient to be

There is a contradiction between what the help page says and what constrOptim actually does with the constraints. The issue is what happens on the boundary. The help page says The feasible region is defined by ?ui %*% theta - ci >= 0?, but the R code for constrOptim reads if (any(ui %*% theta - ci <= 0)) stop("initial value not feasible") The following example

Hello, I am using constrOptim to maximize a likelihood function (the values of my parameter vector must be between zero and one and must sum up to <=1). I am getting the error 'initial value in 'vmmin' is not finite'. I've tracked it down to a problem in the function 'R' defined within the constrOptim function. It is performing a check on my values, and its not