similar to: Non-linear Least Square Optimization -- Function of two variables.

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! 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

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 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

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

Can someone clarify what I cannot make out from the documentation? The function 'clogit' in the 'survival' package is described as performing a "conditional logistic regression". Its return value is stated to be "an object of class clogit which is a wrapper for a coxph object." This suggests that its usefulness is confined to the sort of data which arise in

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 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

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

Dear all, My question might be more of a statistics question than a question on R, although it's on how to apply the 'quantreg' package. Please accept my apologies if you believe I am strongly misusing this list. To be very brief, the problem is that I have data on only a random draw, not all of doctors' patients. I am interested in the, say, median number of patients of

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

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 am trying to install minpack.lm on R 3.3.3 (Debian version) on a Chromebook. But I get this error: ``` > install.packages("minpack.lm") Installing package into ?/home/marongiuluigi/R/x86_64-pc-linux-gnu-library/3.3? (as ?lib? is unspecified) trying URL 'https://cran.rstudio.com/src/contrib/minpack.lm_1.2-1.tar.gz' Content type 'application/x-gzip' length 43029 bytes

Thank you, it looks like I have already libisl: ``` apt search libisl Sorting... Done Full Text Search... Done libisl-dbg/oldstable 0.18-1 amd64 manipulating sets and relations of integer points bounded by linear constraints libisl-dev/oldstable 0.18-1 amd64 manipulating sets and relations of integer points bounded by linear constraints libisl15/oldstable,now 0.18-1 amd64

As some of you R-devel readers may know, the plot() method for "lm" objects is based in large parts on contributions by John Maindonald, subsequently "massaged" by me and other R-core members. In the statistics litterature on applied regression, people have had diverse oppinions on what (and how many!) plots should be used for goodness-of-fit / residual diagnostics, and to my

I don't know about the configuration. I installed R using the standard protocol for Chromebook http://blog.sellorm.com/2018/12/20/installing-r-and-rstudio-on-a-chromebook/ the rest, it was done by the system itself... On Tue, Jul 14, 2020 at 1:30 PM Dirk Eddelbuettel <edd at debian.org> wrote: > > > There is something wrong with your system / setup I did not notice first: >

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:

Hi, I am confused about how to select elements from a list. I'm trying to select all rows of a table 'crossRsorted' such that the mean of a related vector is > 0. The related vector is accessible as a list element l[[i]] where i is the row index. I thought this would work: > crossRsorted[mean(q[[ crossRsorted[,1] ]], na.rm = TRUE) > 0, ] Error in q[[crossRsorted[, 1]]] :

I have a puzzle as to how R is computing Cook's distance in weighted linear regression. In this case cook's distance should be given not as in OLS case by h_ii*r_i^2/(1-hii)^2 divided by k*s^2 (1) (where r is plain unadjusted residual, k is number of parameters in model, etc. ) but rather by w_ii*h_ii*r_i^2/(1-hii)^2 divided by k*s^2,

Hello Dear, I have a function, like z=f(x,y), and try a surface plot with this function. But, on the reference of "wireframe" requires data option, so I generated x and y, and computed z with them. But, still I have a problem to draw a surface plot. The code and errors are ################################################## mle_beta0=64.43707; mle_beta1=-24365.16; # generating for