similar to: nonlinear least square optimization

http://r.789695.n4.nabble.com/file/n4619404/pic1.jpg cesres_ext <- nls(lnGDP85~ intercept + (alpha/(1-alpha-beta)) * lns_ikonngdelta + (beta/(1-alpha-beta)) * lns_ihonngdelta + 0.5 * ((sigma-1)/sigma) * (1/((1-alpha-beta)*(1-alpha-beta))) * (alpha * taylor1 + beta * taylor2 - alpha*beta*taylor3) ,start = list(intercept=8, alpha=0.2, beta=0.4, sigma=1.2),data=data) I have this model. I use

Hi to all, Is there a way we can fit a non linear model to a data using non linear least square method without necessarily initialising the parameters of the model. I find it hard to get the initial value of the parameter. Below is a sample of the code I have. *nachman<-nls(OARmedium$OCCUPANCY~1exp(-alpha*OARmedium$MEAN^beta),start=list(alpha=0.2,beta=0.1),data=OARmedium)summary(nachman)*

Hi, I have a set of twelve points and wonder how I can get a function that can then be used to calculate the area under the curve (most important). Thanks. Eric

Sorry, for disturbing the list again. > Also I got one suggestion of using ORDPACK at http://www.netlib.org/. It's ODRPACK at http://www.netlib.org/, not ORDPACK. Best, -- Etsushi Kato ekato at ees.hokudai.ac.jp -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send

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

Hi guys I have data that contains the variances vt of the yields of 1, 2, 3, 4, 5,10, 20 year bonds. Assuming the Hull-White model for the yield of a t-year zero-coupon bond, I have to estimate the ? of the Hull-White model using nonlinear least squares and give a 95% con?dence interval for each parameter. Please can you guys tell how to find out ? using R. Any suggestion regarding what functions

Hi, I am new to R, and am trying to solve the following optimization problem: This is a nonlinear least squares problem. I have a set of 3D voxels. All I need is to find a least squares fit to this data. The data model actually represent a cube-like structure, consisting of seven straight lines. The lines have some intersections (and at this intersection both of the participating lines end).

Hello, I am trying to optimise a nonlinear model to derive 'best-fit' parameter esimates using the genoud function. I have been using the genetic algorithm - gafit - in order to do this, but I am getting parameter estimates that do not always reach the global minimum. I am very keen to apply genoud to optimising this model to see if my results will improve, and also out of personal

Hello, I'd like to fit a user defined function to a data set, but I have problems to find my problem. The user defined function is a combination of two rectangular functions, and the listing below gives an example for what I want to do. The problem is, that I get the error message for fit1 and fit2 "Error in nlsModel(formula, mf, start, wts) : singular gradient matrix at initial

Dear all, I encounter some discrepancies when comparing the deviance of a weighted and unweigthed model with the AIC values. A general example (from 'nls'): DNase1 <- subset(DNase, Run == 1) fm1DNase1 <- nls(density ~ SSlogis(log(conc), Asym, xmid, scal), DNase1) This is the unweighted fit, in the code of 'nls' one can see that 'nls' generates a vector

Hi there, I'm trying to fit a growth curve to some data and need to use a weighted least squares estimator to account for heteroscedasticity in the data. A weights argument is available in nls that would appear to be appropriate for this purpose, but it is listed as 'not yet implemented'. Is there another package which could implement this procedure? Regards, Robert Brown

Following on my posting of this morning, concerning a problem that I am having constructing a self-starting function for use with nls (and eventually with nlsList and nlme), the following is the self-starting function called NRhyperbola: > NRhyperbola function (Irr,theta,Am,alpha,Rd) { # Am is the maximum gross photosynthetic rate # Rd is the dark resiration rate (positive value) #

Hello. I am having a problem setting up a self-starting function for use in nonlinear regression (and eventually in the mixed model version). The function is a non-rectangular hyperbola - called "NRhyperbola" - which is used for fitting leaf photosynthetic rate to light intensity. It has one independent variable (Irr) and four parameters (theta, Am, alpha and Rd). I have created this

Hello! I am running R-2.3.1-i386-1 on Slackware Linux 10.2. I am a former matlab user, moving to R. In matlab, via the cftool, I performed nonlinear curve fitting using the method "nonlinear least squares" with the "Trust-Region" algorithm and not using robust fitting. Is it possible to perform the same analysis in R? I read quite a lot of R documentation, but I could not find

I have used Mike Powell's optimization routine (VA05AD) from the Harwell Subroutine Library (HSL) for more than 20 years. It is no exaggeration to say that it has helped make my career (thanks Mike). I recently learned that I am not alone in this respect - apparently it still has a loyal following in all sorts of fields! It is an exceedingly fine piece of software - fast, reliable and easy to

Dear Mr Graves, Thank you very much for your response. Nobody else from this mailing list ventured to reply to me for the two weeks since I posted my question. "nlminb" and "optim" are just optimization procedures. What I need is not just optimization, but a nonlinear CURVE FITTING procedure. If there is some way to perform nonlinear curve fitting with the

I'm trying to fit a model to solve a biological problem. There are multiple independent variables, and also there are multiple responses. Each response is a function of all the independent variables, plus a set of parameters. All the responses depend on the same variables and parameters - just the form of the function changes to define each seperate response. Any ideas how I can fit

Here I think S3 dispatch is very natural. Try the following: page <- function(x, method = c("dput", "print"), ...) UseMethod("page") page.getAnywhere <- function(x, ..., idx=NULL) { name <- x$name; objects <- x$obj; if (length(objects) == 0) stop("no object named '", name, "' was found"); if (is.null(idx)) {

Hi, I am trying to use the nls() function to closely approximate a vector of values, colC and I'm running into trouble. I am not sure how if I am asking the program to do what I think its doing, because the same minimization in Excel's Solver does not run into problems. If anyone can tell me what is going wrong, and why I'm getting a singular convergence(7) error, please tell me. I

Hi, forget about the below details. It is not related to the fact that the function is returned from a function. Sorry about that. I've been troubleshooting soo much I've been shoting over the target. Here is a much smaller reproducible example: x <- 1:10 y <- 1:10 + rnorm(length(x)) sp <- smooth.spline(x=x, y=y) ypred <- predict(sp$fit, x) # [1] 2.325181 2.756166 ...