Displaying 20 results from an estimated 2000 matches similar to: "How is the Gauss-Newton method compared to Levenberg-Marquardt for curve-fitting?"
2005 Jun 21
2
nls(): Levenberg-Marquardt, Gauss-Newton, plinear - PI curve fitting
Hello,
i have a problem with the function nls().
This are my data in "k":
V1 V2
[1,] 0 0.367
[2,] 85 0.296
[3,] 122 0.260
[4,] 192 0.244
[5,] 275 0.175
[6,] 421 0.140
[7,] 603 0.093
[8,] 831 0.068
[9,] 1140 0.043
With the nls()-function i want to fit following formula whereas a,b, and c
are variables: y~1/(a*x^2+b*x+c)
With the standardalgorithm
2007 Feb 21
1
Confindence interval for Levenberg-Marquardt fit
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
2001 Jan 10
2
Levenberg-Marquardt algorithm
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: +353 21 904178 Prospect Row,
Fax: +353 21 270271 Cork,
WWW:
2000 May 15
1
Non linear regression using Levenberg-Marquardt method
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.
I''d also like to know if if
2010 Aug 23
1
Fitting Weibull Model with Levenberg-Marquardt regression method
Hi,
I have a problem fitting the following Weibull Model to a set of data.
The model is this one: a-b*exp(-c*x^d)
If I fitted the model with CurveExpert I can find a very nice set of coefficients which create a curve very close to my data, but when I use the nls.lm function in R I can't obtain the same result.
My data are these:
X Y
15 13
50 13
75 9
90 4
With the commercial
2007 Sep 07
2
Matlab's lsqnonlin
Hi! I'm translating some code from Matlab to R and I found a problem.
I need to translate Matlab's function 'lsqnonlin'
(http://www-ccs.ucsd.edu/matlab/toolbox/optim/lsqnonlin.html) into R,
and at 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
2006 Sep 02
1
nonlinear least squares fitting Trust-Region"
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
2012 Jan 18
1
Non-linear Least Square Optimization -- Function of two variables.
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.
For
2006 Aug 23
2
nonlinear least squares trust region fitting ?
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
2008 Mar 13
0
new version of minpack.lm
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
2008 Mar 13
0
new version of minpack.lm
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
2007 Sep 16
1
Problem with nlm() function.
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
2011 Sep 14
1
Nonlinear Regression
I'm wondering what packages exist to implement nonlinear least squares
regression in R other than 'nls'. Are there packages which implement
methods to estimate the optimum values of the parameters which do not
use the Gauss-Newton algorithm 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
2007 Jun 19
1
help w/ nonlinear regression
Dear All,
I'd like to fit 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
2006 May 24
1
general Gauss-Newton or support for NSUR: contemporaneously correlated non-linear models
Dear r-Help readers,
1) Is there support for NSUR in some R package yet?
2) Is there a general function of applying the Gauss-Newton or Marquard
method, in which the function of calculating the partial derivatives can
be specified by the user?
Contemporaneously correlated non-linear models (NSUR) is a method to fit
a system of non-linear equations.
I want to use to fit several non-linear
2014 Dec 17
2
optimización - resolver sistema - general
Hola a todos,
Simplemente comentar que me tengo encontrado con muchos problemas
de optimización. Mi recomendación general, en el caso multidimensional y
si el tiempo de computación es importante, sería buscar un algoritmo
diseñado para el tipo de problema (evitar los algoritmos más generales
tipo optim si puede haber problemas de mínimos locales). Algunos casos
que tengo resuelto con R
2006 Aug 22
0
NonLinearLeastSquares Trust-Region
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
2013 Mar 14
2
question about nls
Hi,all:
I met a problem of nls.
My data:
x y
60 0.8
80 6.5
100 20.5
120 45.9
I want to fit exp curve of data.
My code:
> nls(y ~ exp(a + b*x)+d,start=list(a=0,b=0,d=1))
Error in nlsModel(formula, mf, start, wts) :
singular gradient matrix at initial parameter estimates
I can't find out the reason for the error.
Any suggesions are welcome.
Many thanks.
[[alternative HTML
2004 Oct 08
0
R interface for MINPACK least squares optimization library
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
2004 Oct 08
0
R interface for MINPACK least squares optimization library
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