similar to: Question regarding nonlinear regression

Hi, I'm trying to solve the following system of nonlinear equations P1 - F2 = x[1] + (1/2) * x[3] * x[1]^2 P2 - F2 = x[2] + (1/2) * x [3] * x[2]^2 F1 - F2 = -(1/2) * x[1] - (1/2) * x[2] + (1/8) * x [3] * (x[1] + x[2])^2 B1 - F2 = (1/4) * x[1] - (1/4) * x[2] + (1/16) * x[3] * (x[1] - x[2])^2 B2 - F2 = (1/4) * x[1] + (1/4) * x[2] + (1/16) * x[3] * (x[1]

Hi It is preferable to echo your posts to r-help, you usually get more answers and some definitelly superb to mine. It is also better to start a new mail if your question has nothing to do with original subject "Maura E Monville" <maura.monville at gmail.com> napsal dne 01.10.2007 17:44:43: > Unluckily I do not have the privilege of practising with R all day > long. I

How to fit a nonlinear regression model with power exponentially distributed errors? I know gnlm has a function gnlr3 that could work, but I would be grateful if example R code is provided. Daniel [[alternative HTML version deleted]]

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)

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",

I am trying to fit a nonlinear model using nlm(). My application of nlm() is a bit complicated. Here is the story behind the model being fit: The observer is trying to detect a signal corrupted by noise. On each trial, the observer gets stim=signal+rnorm(). In the simulation below I have 500 trials. Each row of stim is a new trial. On each trial, if the cross-correlation between the stim and the

Hi, I found this email on the R website. I am trying to figure out how to analyse a data set that I believe will need to be run through a procedure involving repeated measures, regression and mixed models. The data is of insect populations (dependent variable - either 0/1=binomial, or as counts=poisson) in sites with different characteristics (multiple independent variables which are both

Hello to everyone, I found some problems using R in the estimation of systems of nonlinear equations like SURE (Seemingly Unrelated Regression Equations) with mutual parameters as the following system example: EQ1 PQ1=P1*G1+B1*(Y-P1*G1-P2*G2-P3*G3) EQ2 PQ2=P2*G2+B2*(Y-P1*G1-P2*G2-P3*G3) where G1,G2,G3,B1,B2 are the coefficients to estimate. command "nls" accept only single

I have a complicated nonlinear function, myfun(a,b,c), that I want to fit to data, allowing one or more of the parameters a, b, and c in turn to have linear dependence on other covariates. In other words, I'd like to specify something like nls(y~myfun(a,b,c),linear=list(a~f1,b~1,c~1)) I know would this work in nlme *if I wanted to specify random effects as well*, but I don't -- and

After many months, I am now banging my head against the wall because I can't find a solution to this seemingly trivial problem.&nbsp; Any help would be appreciated: I am trying to apply a nonlinear fitting routine to a 3D MR image on a voxel-by-voxel basis.&nbsp; I've tested the routine using simulated data and things went well.&nbsp; As for the real data, the fitting routine

Hello everyone, I am looking into time series data compression at the moment. The idea is to fit a curve on a time series of n points so that the maximum deviation on any of the points is not greater than a given threshold. In other words, none of the values that the curve takes at the points where the time series is defined, should be "further away" than a certain threshold from the

Hi All, Could any one tells me if R or S has the capacity to fit nonlinear regression with Huber's M estimation? Any suggestion is appreciated. I was aware of 'rlm' in MASS library for robust linear regression and 'nls' for nonlinear least squares regression, but did not seem to be able to find robust non-linear regression function. Thanks and regards, Ray Liu

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 am trying to run the following nonlinear regression model. > nreg <- nls(y ~ exp(-b*x), data = mydf, start = list(b = 0), alg = "default", trace = TRUE) OUTPUT: 24619327 : 0 24593178 : 0.0001166910 24555219 : 0.0005019005 24521810 : 0.001341571 24500774 : 0.002705402 24490713 : 0.004401078 24486658 : 0.00607728 24485115 : 0.007484372

Hello R-helpers, I would like to fit a non-linear function to data (Discrete X axis, over-dispersed Poisson values on the Y axis). I found the functions gnlr in the gnlm package from Jim Lindsey: this can handle nonlinear regression equations for the parameters of Poisson and negative binomial distributions, among others. I also found the function nls2 in the software package

Dear all, When doing nonlinear regression, we normally use nls if e are iid normal. i learned that if the form of the variance of e is not completely known, we can use the IRWLS (Iteratively Reweighted Least Squares ) algorithm: for example, var e*i =*g0+g1*x*1 1. Start with *w**i = *1 2. Use least squares to estimate b. 3. Use the residuals to estimate g, perhaps by regressing e^2 on

Dear R users, I?m trying to optimize simultaneously two binomials inequalities used to 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)

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