search for: levenberg

Dear all, I would like to use the Levenberg-Marquardt algorithm for non-linear least-squares regression using function nls.lm. Can anybody help me to find a a way to compute confidence intervals on the fitted parameters as it is possible for nls (using confint.nls, which does not work for nls.lm)? Thank you for your help Michael

Hi, It seems to me that the most suitable method in R for curve-fitting is the use of nls, which uses a Gauss-Newton (GN) algorithm, while the use of the Levenberg-Marquardt (LM) algorithm does not seem to be very stressed in R. According to this [1] by Ripley, 'Levenberg-Marquardt is hardly competitive these days' which could imply the low emphasize on LM in R. The position of LM is, to some extend, confusing. Bonnans et al [2] introduce the trust-r...

Hi All, Is the Levenberg-Marquardt algorithm available in R. This method combines the steepest descent algorithm and Newton's method. Thanks in Advance, Dermot MacSweeney. ************************************************************** Dermot MacSweeney NMRC, Email: dsweeney at nmrc.ucc.ie Lee Maltings, Tel: +...

...Y 15 13 50 13 75 9 90 4 With the commercial software I obtain the following coefficients: Weibull Model: y=a-b*exp(-c*x^d) Coefficient Data: a = 1.31636909714E+001 b = 7.61325570579E+002 c = 2.82150000991E+002 d = -9.23838785044E-001 For fitting the Levenberg-Marquardt in R I'm using the following lines: pS<-list(a=1,b=1,c=1,d=1) model<-function(pS,xx){pS$a-pS$b*exp(-pS$c*xx^-pS$d)} resid<-function(observed,pS,xx){observed-model(pS,xx)} lin<-nls.lm(pS,resid,observed=Y,xx=X) Why I can't obtain the same results? Many thanks in advance...

..., cc) : singular matrix `a' in solve I have found in the mailinglist "https://stat.ethz.ch/pipermail/r-help/2001-July/012196.html" that is if t he data are artificial. But the data are from my measurment. The commercial software "Origin V.6.1" solved this problem with the Levenberg-Marquardt algorithm how i want. The reference results are: a = 9.6899E-6, b = 0.00689, c = 2.72982 What are the right way or algorithm for me to solve this problem and what means this error with alg="plinear"? Thanks in advance. -- Weitersagen: GMX DSL-Flatrates mit Tempo-Garantie! Ab...

Hello, I want to fit some non linear models with the Levenberg-Marquardt algorithm. It doesn''t seem to exist any function to do this in R ( well, maybe one does, but I''m a new user, and the only documentation I have is "An introduction to R"). I''d like to know if this function exists, maybe throught an additionnal package....

...t the beginning I thought it would be the same as R's 'optim'. But then I looked at the definition of 'lsqnonlin' and I don't quite see how to make 'optim' to do the same thing. Does anyone have an idea? This is apart from the fact that I would like to use the Levenberg Marquardt algorithm which is not implemented in R (some discussion about this: http://tolstoy.newcastle.edu.au/R/help/00b/2492.html). Thank you! All the best, -- -- Jose Luis Aznarte M. http://decsai.ugr.es/~jlaznarte Department of...

In the course of revising a paper I have had occasion to attempt to maximize a rather complicated log likelihood using the function nlm(). This is at the demand of a referee who claims that this will work better than my proposed use of a home- grown implementation of the Levenberg-Marquardt algorithm. I have run into serious hiccups in attempting to apply nlm(). If I provide gradient and hessian attributes to the returned function value (the ***negative*** of the log likelihood, since nlm() minimizes things) then nlm() wanders around for a very long time and reaches a...

...do not want Gauss-Newton. Since I am not an expert in the field of optimization, I am just conforming to what matlab documentation suggests, namely: "Algorithm used for the fitting procedure: Trust-Region -- This is the default algorithm and must be used if you specify coefficient constraints. Levenberg-Marquardt -- If the trust-region algorithm does not produce a reasonable fit, and you do not have coefficient constraints, you should try the Levenberg-Marquardt algorithm. Gauss-Newton --THIS ALGORITHM IS POTENTIALLY FASTER THAN THE OTHER ALGORITHMS, BUT IT ASSUMES THAT THE RESIDUALS ARE CLOSE TO...

...ssible to carry out in R, I would be grateful if you suggest to me some alternative procedure. I found that the "nls" function performs nonlinear least squares. The problem is that I do not want to implement the Gauss-Newton algorithm. In the worst case I would be contented with the "Levenberg-Marquardt" algorithm, if it is implemented in R. R nls's documentation mentions the "port" package and the ?nl 2sol? algorithm, but I could not find that package in the CRAN repository, so that I could read and judge whether that algorithm would be appropriate. Thank you very m...

The package minpack.lm allows nonlinear regression problems to be addressed with a modification of the Levenberg-Marquardt algorithm based on the implementation of 'lmder' and 'lmdif' in MINPACK. Version 1.0-8 of the package is now available on CRAN. Changes in version 1.0-8 include: o possibility to obtain standard error estimates on the parameters via new methods for the generic...

The package minpack.lm allows nonlinear regression problems to be addressed with a modification of the Levenberg-Marquardt algorithm based on the implementation of 'lmder' and 'lmdif' in MINPACK. Version 1.0-8 of the package is now available on CRAN. Changes in version 1.0-8 include: o possibility to obtain standard error estimates on the parameters via new methods for the generic...

...lgorithm e.g. use Nelder Mead. In particular, I'd be interested where this is done where the methods of the plinear algorithm are also used (the initial step using the projection matrix). If these don't exist why is the Gauss-Newton algorithm considered sufficient? Also can I implement the Levenberg Marquadt algorithm in nls? Regards Liam

Dear All, In the past I have often used minpack (http://bit.ly/zXVls3) relying on the Levenberg-Marquardt algorithm to perform non-linear fittings. However, I have always dealt with a function of a single variable. Is there any difference if the function depends on two variables? To fix the ideas, please consider the function f(R,N)=(a/(log(2*N))+b)*R+c*N^d, where a,b,c,d are fit parameters...

...t a "kind" of logistic model to small data-set using nonlinear least-squares regression. A transcript of R-script are reproduced below. Estimated B and T (the model's coeff, herein B=-8,50 and T=5,46) seem appropriate (at least visually) but are quite diff from those obtained w/ SPSS (Levenberg-Marquardt): B=-19,56 and T=2,37. Am I doing something wrong in R (or at least non-comparable "methodologies")? Please, feel free to comment/suggest. Regards, Eduardo Esteves # Dados CO2<-c(141,172,181,227,309,414,641,936) Prop<-c(0.25,0.34,0.34,0.68,0.85,0.99,0.98,0.99) # Diagram...

Hello guys. I've built and uploaded to CRAN an R interface to MINPACK Fortran library, which solves non-linear least squares problem by modification of the Levenberg-Marquardt algorithm. The package includes one R function, which passes all the necessary control parameters to the appropriate Fortran functions. The package location is http://cran.r-project.org/src/contrib/Descriptions/minpack.lm.html Best wishes, Timur. ____________________________________...

...he SNQ profit function. The "micEconCES" package (version 0.6-6) provides tools for estimating the Constant Elasticity of Scale (CES) function. Function cesEst() has been tremendously improved. It can estimate the CES function using much more estimation methods (optimizers) now, e.g. the Levenberg-Marquardt or the Differential Evolution algorithm. Furthermore a "vignette" (supplementary documentation) "Estimating the CES Function in R: Package micEconCES" (written by Geraldine Henningsen and Arne Henningsen) [1] has been added. The "micEcon" package (version 0....

Hello guys. I've built and uploaded to CRAN an R interface to MINPACK Fortran library, which solves non-linear least squares problem by modification of the Levenberg-Marquardt algorithm. The package includes one R function, which passes all the necessary control parameters to the appropriate Fortran functions. The package location is http://cran.r-project.org/src/contrib/Descriptions/minpack.lm.html Best wishes, Timur. ____________________________________...

...ssible to carry out in R, I would be grateful if you suggest to me some alternative procedure. I found that the "nls" function performs nonlinear least squares. The problem is that I do not want to implement the Gauss-Newton algorithm. In the worst case I would be contented with the "Levenberg-Marquardt" algorithm, if it is implemented in R. R nls's documentation mentions the "port" package and the ?nl2sol? algorithm, but I could not find that package in the CRAN repository, so that I could read and judge whether that algorithm would be appropriate. Thank you ver...

...he SNQ profit function. The "micEconCES" package (version 0.6-6) provides tools for estimating the Constant Elasticity of Scale (CES) function. Function cesEst() has been tremendously improved. It can estimate the CES function using much more estimation methods (optimizers) now, e.g. the Levenberg-Marquardt or the Differential Evolution algorithm. Furthermore a "vignette" (supplementary documentation) "Estimating the CES Function in R: Package micEconCES" (written by Geraldine Henningsen and Arne Henningsen) [1] has been added. The "micEcon" package (version 0....