similar to: Optimization Problem in R

Dear List, I'm trying to solve this simple optimization problem in R. The parameters are the exponents to the matrix mm. The constraints specify that each row of the parameter matrix should sum to 1 and their product to 0. I don't understand why the constraints are not satisfied at the solution. I must be misinterpreting how to specify the constrains somehow. library(alabama) ff <-

Hi, I apologize for the long message but the problem I encountered can't be stated in a few lines. I am having some problems with the function constrOptim. My goal is to maximize the likelihood of product of K multinomials, each with four catagories under linear constraints on the parameter values. I have found that the function does not work for many data configurations. #The likelihood

Hello, I'm trying to run constrOptim. It returns to me an error about the fact that constraints arguments (ui and ci) are non compatibles: > optout= constrOptim(startparams, f=ImpulseSS, grad=grImpulse, ui=UI, ci=CI, data=gexp[k,], t=t) Error in ui %*% theta : non-conformable arguments I would like to point out that I can calculate that product in the command line: > UI %*%

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 ...

Trying to use constrOptim to minimize the sum of squared deviations. I put the objective function in as: sum((x %*% Y - Z)^2) so i'm trying to get values for x to minimize the sum of the squared deviations between the product of x and Y and Z. Anyways i have no problem using this when x is a 3x1 test variable. it works great with the constraints and everything. when i actually use it on

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, 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]]

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 All, I would like to optimize a (log-)likelihood function subject to a number of linear constraints between parameters. These constraints are equality constraints of the form A%*%theta=c, ie (1,1) %*% 0.8,0.2)^t = 1 meaning that these parameters should sum to one. Moreover, there are bounds on the individual parameters, in most cases that I am considering parameters are bound between zero

How can I use a function with some additional input parameters in constrOptim? For example, something like fr <- function(x,a) { ## Rosenbrock Banana function x1 <- x[1] x2 <- x[2] a * (x2 - x1 * x1)^2 + (1 - x1)^2 } where the optimum is to be found w.r.t. x. Calling optim(c(-1.2,1), fr, NULL, a=100) works as expected, but I fail to provide the a=100 in the constrained case:

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 R users, I have been trying to obtain the MLE of the following model state 0: y_t = 2 + 0.5 * y_{t-1} + e_t state 1: y_t = 0.5 + 0.9 * y_{t-1} + e_t where e_t ~ iidN(0,1) transition probability between states is 0.2 I've generated some fake data and tried to estimate the parameters using the constrOptim() function but I can't get sensible answers using it. I've tried using

Hello everyone, I want to calculate type I error rate for modified F statistic for one way robust anova. I need to find the j group trimmed mean and winsorized sum of squared deviations. Here I attached my code for j=2 to make it simple. Originally I have j=4. Hope you can help. I need to run it for 1000 times My problem is: i) the value of F-test obtain from my simulation below is in negative

Full_Name: Haobo Ren Version: 2.1.1 OS: Windows 2000 Submission from: (NULL) (192.11.226.116) When running constrOptim, there is error message Error: subscript out of bounds

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)

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

Hello, and apologies for the upcoming naive questions. I am a biologist who is trying to teach himself the appropriate areas of math and stats. I welcome pointers to suggested background reading just as much as I do direct answers to my question. Let's say I have a function F() that takes variables (a,b,c,a1,b1,c1) and returns x, and I want to find the values of these variables that result in

Hello, I am trying to model a bivariate time series called 'residuals' as a dcc-garch model. I want to use the function dcc.estimation(a, A, B dcc.para, dvar, model) to estimate the parameters. No matter how I tried to define a, A and B, I always got the message "Error in constrOptim(theta = para, f = loglik.dcc2, gr = grad.dcc2, ui = resta, : initial value not

Dear helpers, I am using the function "constrOptim" to estimate a model with ML with an inequality constraint using the option method='Nelder-Mead'. When I specify the option: hessian = TRUE I obtain the response: Error in f(theta, ...) : unused argument(s) (hessian = TRUE) I guess the function "constrOptim" does not allow this argument which, on the other hand, is

Dear friends. I am using function constrOptim(c(0.5,0.3,0.5), fit.error, fit.error.grr, ui=-1*ui,ci=-1*ci) and I am confronted with error message "initial value not feasible" I plug in the initial value of (0.5,0.3,0.5) to function fit.error and fit.error.grr and have pretty reasonable result. I inequality "ui %*% theta - ci >= 0" as suggested in the R manual and it is