similar to: Levenberg-Marquardt algorithm

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

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

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

Hi, I upgraded to version 1.2.1 and now the up/down arrows for recalling commands no longer works. Is there a work around for this? > version _ platform sparc-sun-solaris2.7 arch sparc os solaris2.7 system sparc, solaris2.7 status major 1 minor 2.1 year

Hi, I would like to be able to access the calculated results from the nls package. Using the example in R, fm3DNase1 we can reurn certain parts of the calculations: > coef(fm3DNase1) Asym xmid scal 2.345179 1.483089 1.041454 > resid(fm3DNase1) [1] -0.0136806237 -0.0126806237 0.0089488569 0.0119488569 -0.0025803222 [6] 0.0064196778 0.0026723396 -0.0003276604

Hi All, What I would like to do is the following: a) fit a probability function to a measured data set. This would be user specified, e.g., normal, lognormal, etc. and then b) take the probability function and plot it with the histogram of the measured data set. This function would be displayed as a smooth curve. This would involve "re-sizing" the probability function to match

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

Hi, I would like to have four plots plotted in the one figure and under each plot the caption (a) ... (d). Using mtext I can get so far: > plot(1:10, exp(1:10), log = "y", xlab = "xlab") > mtext("(a)",side=1,line=4.5) > plot(1:10, exp(1:10), log = "y", xlab = "xlab") > mtext("(b)",side=1,line=4.5) > plot(1:10,

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

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

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

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

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

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

I need to completely remove row.names from a dataframe. Are there other ways to remove them (and not anything else) besides: mydataframe<-data.frame(mydataframe, row.names=NULL) I realize that this doesn't really remove the row.names; it merely replaces the current row.names vector with the numbers 1..nrow (in quotes). ===================== Dr. Marc R. Feldesman Professor and

I need to completely remove row.names from a dataframe. Are there other ways to remove them (and not anything else) besides: mydataframe<-data.frame(mydataframe, row.names=NULL) I realize that this doesn't really remove the row.names; it merely replaces the current row.names vector with the numbers 1..nrow (in quotes). ===================== Dr. Marc R. Feldesman Professor and

Dermot MacSweeney pointed out to me that after my "fix" of legend(), points were no longer coming out placed in the middle of the lines, but at the right-hand edge. It turns out that naively swapping the order of point-drawing and line-drawing also messes up the bookkeeping that legend() does on the current x-location. Here's my patch, which fixes that bookkeeping (and incidentally

Hi All, Two quick questions. 1) I am using write.table to output data.frames to ascii files, e.g., test <- data.frame(rnorm(2)) write.table(test,file="output") "rnorm.2." "1" -0.718560808193286 "2" -1.07965693020656 Is it possible to output the data without the first column, i.e., "rnorm.2." -0.718560808193286 -1.07965693020656 2) Is

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