similar to: nlmin

Hello again Is there any way (or an alternative non-linear minimiser) that arguments to the function called in nlm can be passed in version 0.62.4? Like (I believe) nlmin in a well known other program or optimise in R. Do we use global variables? Shurely not! \John -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

R-help Digest Friday, December 3 1999 Volume 01 : Number 034 In this issue: [R] model.tables Re: [R] Installing R on Slackware Linux Re: [R] Installing R on Slackware Linux [R] nlmin Re: [R] nlmin Re: [R] nlmin Re: Summary: [R] Wanted: online Introduction to R [R] Applying a function of several variables to data Re: [R] R and XML -- a near perfect

Dear R users, I am trying to minimise a function using "nlm". I am getting the following error message: "Error: function is too long to keep source" The function is really very long (about 100 A4 pages). Is there anything I could do to solve this problem? At the moment I am using "nlmin" in S-Plus with no problems but I'd prefer to use R. Thank you very

Dear all, I am using the functions 'optim' and 'nlminb'. For both, you can provide a function which computes the gradient of the objective function (to enhance speed and precision). In my case, both the objective function and the gradient take time to be computed and share many common computations (similar matrix, products, etc...). Therefore, I have to compute these

dear R experts---sorry, second question of the day. I want to match some moments. I am writing my own code---I have exactly as many moment conditions as parameters, and I am leary of having to learn the magic of GMM weighting matrices (if I was to introduce more). the process sounds easy conceptually. (Seen it in seminars many times, so how hard could it possibly be?...me thinks) first

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]]

GENERAL REFERENCE ON NONLINEAR OPTIMIZATION? What are your favorite references on nonlinear optimization? I like Bates and Watts (1988) Nonlinear Regression Analysis and Its Applications (Wiley), especially for its key insights regarding parameter effects vs. intrinsic curvature. Before I spent time and money on several of the refences cited on the help pages for "optim",

Hi, I am currently looking for a program or programmng language easy to learn, easier to operate on.I heva heard about "R", However I understand that "R" is designed especially for statisticians. As an economist, working on applied econometrics, I am not sure if it can meet my needs. Will I be able to reach precise time series or panal data regression results with

Good morning to all, I should find the zero of a specific function with respect to a vector of arguments. Does it exist something similar in R? Thank you very much, Enrico Foscolo

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 {

Thanks very much Douglas for the pointer to nlm. Maybe the "Notes on R" maintainer can add at least a mention of nlm in the section on nonlinear fitting? I never did nonlinear fitting in S-Plus before, so I have nothing to unlearn, but I was hoping someone could show me how to do a least squares fit with nlm. example: x<-c(1,2,3,4,5,6) y<-.3*x^-.6 +.2 y<-y+rnorm(6,0,.01)

Thanks very much Douglas for the pointer to nlm. Maybe the "Notes on R" maintainer can add at least a mention of nlm in the section on nonlinear fitting? I never did nonlinear fitting in S-Plus before, so I have nothing to unlearn, but I was hoping someone could show me how to do a least squares fit with nlm. example: x<-c(1,2,3,4,5,6) y<-.3*x^-.6 +.2 y<-y+rnorm(6,0,.01)

I have a question about passing arguments to the function f that nlm minimizes. I have no problems if I do this: x<-seq(0,1,.1) y<-1.1*x + (1-1.1) + rnorm(length(x),0,.1) fn<-function(p) { yhat<-p*x+(1-p) sum((y-yhat)^2) } out<-nlm(fn,p=1.5,hessian=TRUE) But I would like to define fn<-function(x,y,p) { yhat<-p*x+(1-p) sum((y-yhat)^2) } so

Dear r-helpers, This might be an elementary question, but I have a hard time getting my head around it, so all help is much appreciated. I am working on a nonlinear regression model of the form if z > 0 y = f1(x,y), else y = f2(x,t) . In other words, the functional form of f(.) changes according to some criteria z. Natural approach would be to fit two models, i.e. model1 <- nlm(y ~ ...,

Simple-mindedly I tried getting MLE and SE for one-parameter model in the same way as for multi-param models. out<-nlm(fn,p=c(2),hessian=T) But sqrt(diag(solve(out$hessian))) gives the answer 1. The Hessian has only one entry, not really a matrix. diag(x) gives 1 if x is just a single number. Is this what I should be doing to get SE for MLE? sqrt(solve(out$hessian)) Thanks very much for

Simple-mindedly I tried getting MLE and SE for one-parameter model in the same way as for multi-param models. out<-nlm(fn,p=c(2),hessian=T) But sqrt(diag(solve(out$hessian))) gives the answer 1. The Hessian has only one entry, not really a matrix. diag(x) gives 1 if x is just a single number. Is this what I should be doing to get SE for MLE? sqrt(solve(out$hessian)) Thanks very much for

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

Please help getting the R squared value in asymptotic regression model I use the code below model1<-nls(GN1~SSasymp (nrate,a,b,c), data = data.1 ) and R produced the modell coefficients without the R squared value? -- Ahmed M. Attia Research Assistant Dept. Of Soil&Crop Sciences Texas A&M University ahmed <ahmedatia@zu.edu.eg>.attia@ag.tamu.edu Cell phone:

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)

Hello, I'm new to this and am trying to teach myself some R by plotting biological data. The growth curve in question is supposed to be fitted to the Verhulst equation, which may be transcribed as follows: f(x)=a/(1+((a-0.008)/0.008)*exp(-(b*x))) - for a known population density (0.008) at t(0). I am trying to rework the example from "An Introduction to R" (p. 72) for my case and