similar to: Nonlinear constrains with optim

I'm looking for an example of a simple R script that impliments a contrained nonlinear function using nlm or optim. I'm not exactly sure how to impliment the constraints within the objective function that is passed to nlm/optim. obj.func <- function( p ) { x(p) <- unconstrained obj function value if( constraint1 > something ) { obj.func <- x(p) } else {

Hi, I have been brought back to the "R-Side" from MatLab. I have used R in graduate econometrics but only for statistics and regression (linear and nonlinear). But now I need to run general nonlinear optimization. I know about the add-in quadprog but my problem is not QP. My problem is a general nonlinear (obj funct) with linear constraints.I know about the "ms" and

Dear R list, From the lrm() binary logistic model we derived the G2 value or the likelihood-ratio chi-squared statistic given as L.R. model, in the output of the lrm(). How can this value be penalized for non-linearity (we used splines in the lrm function)? lrm.iRVI <- lrm(arson ~ rcs(iRVI,5), penalty=list(simple=10,nonlinear=100,nonlinear.interaction=4)) This didn’t work

Hi. I need some advises on how to use R to find pi (i is the index) with the following objective function and constraint: max (sum i)[ f(ai, bi, pi) * g(ci, di, pi) * Di ] s.t. (sum i)[ f(ai, bi, pi) * Di * pi] / (sum i)[ f(ai, bi, pi) * Di ] <= constant f and g are diffentiable. So, I am thinking of optim with method = "BFGS"? But wonder how to include the

Hello everyone,          Dr. wood told me that I can adapting his example to force cubic spline to pass through certain point.          I still have no idea how to achieve this. Suppose we want to force the cubic spline to pass (1,1), how can I achieve this by adapting the following code? # Penalized example: monotonic penalized regression spline ..... # Generate data from a monotonic truth.

All, In looking at `optim', it doesn't appear that it is possible to impose nonlinear constraints on Nelder- Mead. I am sufficiently motivated to try to code something in C from scratch and try to call it from R.... Does anyone have some good references to barrier and/or penalization methods for Nelder-Mead? I would ideally like some papers with pseudocode for method(s) that are in

Dear R users, I?m trying to optimize simultaneously two binomials inequalities (used in acceptance sampling) which are nonlinear solution, so there is no simple direct solution. Please, let me explain shortly the the problem and the question as following. The objective is to obtain the smallest value of 'n' (sample size) satisfying both inequalities: (1-alpha) <= pbinom(c, n, p1)

Dear all, I just know how to solve an eaquation by using optim() or nlm(). But, now, I have three nonlinear equations, how could we use optim() or nlm() to solve  a system of nonlinear equations in R?  Thank you so much. Sincerely, Joe ___________________________________________________ 您的生活即時通 － 溝通、娛樂、生活、工作一次搞定！ [[alternative HTML version deleted]]

R: I need to minimize a function such that the parameters when used in another function result in a particular value, which i fix. That is, I need min(f(a,b)) given g(a,b)=X, where min(.) is the minimum, f(.) and g(.) are functions (with unknown gradients) of parameters a and b and X is a fixed value. What optimization function(s) in R do you suggest? constrOptim looks like it will work

Dear all, I need an advice in the following problem. I have to maximize two functions of the form f1(x)=f(y1,x,alpha1,beta1) and f2(x)=(y2,x,alpha2,beta2), the maximization is with respect to alpha1, alpha2, beta1, beta2. I can maximize each function separately using nlm. The problem is that I have to add the constraint of the form g(alpha1)=g(alpha2). The total number of parameters is

Hello R community, I am using R for creating a model using optimization. I would like to ask if there is R-function/package for solving the problem below: Minimize sum(abs(exp^(Ai1 x1 + Ai2 x2 + ... + Aim xm - bi) - 1)), for each i = 1, ..., n. subject to Ai1 x1 + Ai2 x2 + ... + Ajm xm - bi <= c, where c is a scalar. (x is a vector of variables, A is nxm matrix, b is a vector)

Hello, I am trying to plot confidence bands on the mean and prediction bands for the following nonlinear regression, using maximum likelihood via optim. A toy example with data and code of what I am trying to accomplish is: VOL<-c(0.01591475, 1.19147935 ,6.34102460, 53.68809287, 91.90143074, 116.21397007, 146.41843056, 215.64535337, 256.53149673, 315.73609232) Age <-c(1.622222, 2.833333

Dear All, I couple of weeks ago, I’ve asked for a package recommendation for nonlinear optimization. In my problem I have a fairly complicated non-linear objective function subject to one non-linear equality constrain. I’ve been suggested to use the *Rdonlp2* package, but I did not get any results after running the program for 5 hrs. Is it normal to run this type of programs for hours? Also,

Dear R-Help, I am trying to use R to solve a nonlinear equation many times for different values. I am looking for a mathematical nonlinear equation solution which may not have a closed solution form. For example, I have equation: 2 = (t^2)/log(t) What is t? I am wondering how to solve it in R. Many thanks, Zhu Wang Statistical Science Department SMU.

I tried to ?ms, ?nls and apparently these aren't implemented on R yet. However I seem to remember postings on this list having to do with fitting nonlinear models (no I don't mean GLM type fits, I have a REAL nonlinear model: y=ax^b + c). So please tell me if it is possible to fit nonlinear models in R (by least squares or ML). Thanks! Bill Simpson

Dear R People: I am trying to duplicate the example from Dennis and Schnabel's "Numerical Methods for Unconstrained Optimization and Nonlinear Equations", which starts on page 149. My reason for doing so: to try to understand the "nlm" function. Here is the function: >mfun1 function(x) { z <- matrix(0,nrow=2,ncol=1) z[1,1] <- x[1]^2 + x[2]^2 -

Greetings, R-Wizards: I'm trying to find an extremum subject to a nonlinear constraint. (Yes, I have perused the archives but have found nothing positive.) The details of the problem are these: In a paper published some years ago in Technometrics, ("Confidence bands for cumulative distribution functions of continuous random variables" Technometrics, 25, 77-86. 1983), Cheng and

> > >From: robert-mcfadden w o2.pl > > >Date: 2008/04/02 Wed AM 09:58:28 CDT > > >To: r-help w r-project.org > > >Subject: [R] Nonlinear equation > > > > hi: you need to give an example and details or > > you won't get much response, if any. Equation e.g. (A, B are known constants): 3log(gamma(x))-log(gamma(x)*gamma(2x))+(x-1)*A+B=0

Dear all, I'm looking for a way to solve a simple optimization problem with a nonlinear constraint. An example would be max x s.t. y = x * T ^(x-1) where y and T are known values. optim() and constrOptim() do only allow for box or linear constraints, so I did not succedd here. I also found hints to donlp2 but this does not seem to be available anymore. Any hints are welcome,

Hi, I'm trying to use mars function in R to interpolate nonlinear multivariate functions. However, it seems that mars gives me a fit which uses only very few basis function and it underfits very badly. For example, I have tried the following code to test mars: require("mda") f <- function(x,y) { x^2-y^2 }; #f <- function(x,y) { x+2*y }; # Grid x <-