similar to: Matlab's lsqnonlin

Hi! Maybe this is a silly question, but I need the column rank (http://en.wikipedia.org/wiki/Rank_matrix) of a matrix and R function 'rank()' only gives me the ordering of the elements of my matrix. How can I compute the column rank of a matrix? Is there not an R equivalent to Matlab's 'rank()'? I've been browsing for a time now and I can't find anything, so any

Hi there! Still struggling to translate Matlab code into R's tsDyn package. Here is my question: Is there in R an equivalent function to Matlab's prctile()? To the moment I thought it was quantile(), but I just realized I was wrong. The definition of the Matlab function: prctile Percentiles of a sample SyntaxY = prctile(X,p) Description Y = prctile(X,p) calculates a value that is

Hi there! I need some assistance here with vector orderings. I have a set of q vectors of length p, grouped by rows in a matrix A, q?p, that I need to order lexicographically (http://en.wikipedia.org/wiki/Lexicographical_order). I also have another matrix B, p?r, and a vector c, that should be ordered according to the order of A. So far, I was doing ordering <-

Hi! I want to plot three graphs (residuals, ACF and PACF of a model). Ideally I would use a c(2,2) disposition where the residuals plot would start at position 1,1 and span to position 1,2. Then I would plot the ACF in position 2,1 and the PACF in position 2,2. Maybe is clearer like this: -------------------------- | | | residuals | |

Hi there! I can't find any information about creating combinations with repetitions in R. The function combn() does create combinations, but _without_ repetitions. Here is what I need to do: svIter <- 1000 xx <- matrix(rnorm(m*n), c(m, n)) sequence <- seq(range(xx)[1], range(xx)[2], length.out = svIter^(1/q)) expand.grid(secuence, secuence, .../q times/..., secuence) That is,

Hi! I've been browsing through the last months' archive and I can't find an answer to my question, so here it is (let's hope it's not too obvious): I'm working on extensions of an R library, and I would be very surprised if everyone developing R packages is doing the following, as I do: 1.- Write down a modification of an R file 2.- Exit the current R

Dear all, I'm having trouble using the 'apply' function to multiply some arrays. Let A be a 500x2 array and B a 500x3 array. I need a vector C which has 6 columns being the first three the result of A[,1] * B and the second three the result of A[,2] * B. What is the most efficient way to express that? I'm trying to use 'apply' with no success... Thanks for your

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

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

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

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:

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

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

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

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

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

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

Estimados Reailizo una pregunta general, casi desconociendo. Se me ocurrió explorar lo siguiente: hay ejemplos de programación lineal o utilizando la herramienta solver de excel, donde se realizan algunos cálculos, lo más sencillo de comprender y documentado (todos lados) es una cantidad de productos, un costo de compra, un costo de venta, una cantidad para invertir y ¿cuánto me conviene

SAS has DUD (Does not Use Derivatives)/Secant Method for nonlinear regression, does R offer this option for nonlinear regression? I have read the helpfile for nls() and could not find such option, any suggestion? Thanks, Derek [[alternative HTML version deleted]]