similar to: nls

Dear All, I may look ridiculous, but I am puzzled at the behavior of the nls with a fitting I am currently dealing with. My data are: x N 1 346.4102 145.428256 2 447.2136 169.530634 3 570.0877 144.081627 4 721.1103 106.363316 5 894.4272 130.390552 6 1264.9111 36.727069 7 1788.8544 52.848587 8 2449.4897 25.128742 9 3464.1016 7.531766 10 4472.1360 8.827367 11

hello, I created a package with my functions, and i wand to hide the code of some functions. Could you help me ? Gr?gory -------------------------------------------------------------- GAZ DE FRANCE Gr?gory Benmenzer DIRECTION DE LA RECHERCHE P?le Economie Statistiques et Sociologie 361 Avenue du pr?sident Wilson - BP 33 93211 La Plaine Saint Denis cedex tel : 01 49 22 55 07 fax : 01 49 22

Dear all, I'm looking for how can I estimate a linear model with ar(ma) errors : y(t)=a*X(t)+e(t) with P(B)e(t)=Q(B)u(t) where u is a white noise and P, Q are some polynomes. Could you help me ? Gr?gory Benmenzer

Hello, In doing a series of non-linear estimations of a function which is a sum of a varying number of sinusoids, I would like to "autogenerate" the arguments needed by nls() depending on that number. For example, when there are two sinusoids: > nls( y ~ mu + A1 * cos(2*pi*f1*x - P1) + A2 * cos(2*pi*f2*x - P2), data = some.xy.data, start = list( mu=some.value0,

Hello, I'm using the nls function and would like to increase the number of iterations. According to the documentation as well as other postings on R-help, I've tried to do this using the "control" argument: nls(y ~ SSfpl(x, A, B, xmid, scal), data=my.data, control=nls.control(maxiter=200)) but no matter how much I increase "maxiter", I get the following error

I typed the following commands but it still use maxiter=50 after the 2nd command: nls.control(maxiter = 1000) nls(......) Thanks! --------------------------------- [[alternative HTML version deleted]]

Dear friends. I use nls() and encounter the following puzzling problem: I have a function f(a,b,c,x), I have a data vector of x and a vectory y of realized value of f. Case1 I tried to estimate c with (a=0.3, b=0.5) fixed: nls(y~f(a,b,c,x), control=list(maxiter = 100000, minFactor=0.5 ^2048),start=list(c=0.5)). The error message is: "number of iterations exceeded maximum of

I'm a bit confused about the nls package, I'm trying to use it for curve fitting. First off, the documentation for nls says ``see `nlsControl' for the names of the settable control values'' -- this is wrong, it should be nls.control (minor point but had me confused for a moment). Now I'll try something very simple (maybe too simple):

Hello everyone, I have come across a bit of an odd problem: I am currently analysing data PCR reaction data of which part is behaving exponential. I would like to fit the exponential part to the following: y0 + alpha * E^t In which Y0 is the groundphase, alpha a fluorescence factor, E the efficiency of the reaction & t is time (in cycles) I can get this to work for most of my reactions,

Hello, I am trying to bootstrap a function that extracts the log-likelihood value and the nls coefficients from an nls object. I want to sample my dataset (pdd) with replacement and for each sampled dataset, I want to run nls and output the nls coefficients and the log-likelihood value. Code: x<-c(1,2,3,4,5,6,7,8,9,10) y<-c(10,11,12,15,19,23,26,28,28,30) pdd<-data.frame(x,y)

Hi, I have some data I want to fit with a non-linear function using nls, but it won't solve. > regresjon<-nls(lcfu~lN0+log10(1-(1-10^(k*t))^m), data=cfu_data, > start=(list(lN0 = 7.6, k = -0.08, m = 2))) Error in nls(lcfu ~ lN0 + log10(1 - (1 - 10^(k * t))^m), data = cfu_data, : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Tried to increase minFactor

Hello, I can`t figure out how can increase the velocity of the fitting data by nls. I have a long data .csv I want to read evry time the first colunm to the other colunm and analisy with thata tools setwd("C:/dati") a<-read.table("Normalizzazione.csv", sep=",", dec=".", header=F) for (i in 1:dim(a[[2]]]) { #preparazione dati da analizzare

Hello. I have a script in which I repeatedly fit a nonlinear regression to a series of data sets using nls and the port algorithm from within a loop. The general structure of the loop is: for(i in 1:n){ … extract relevant vectors of dependent and independent variables … … estimate starting values for Amax and Q.LCP…

The documentation for nls says the following about the starting values: start: a named list or named numeric vector of starting estimates. Since R 2.4.0, when 'start' is missing, a very cheap guess for 'start' is tried (if 'algorithm != "plinear"'). It may be a good idea to document that when algorithm = "port", if start is a named

Dear All, I have these problems: 1) How can use the function try in nls model: try(nls(...)) 2) I have 100 colun with data and I want ro prepare 99 file with the first colun with the others Time A1 A2 A3 A4 AN..... I want to have 99 files with a)Time and A1 b)Time and A2 n) Time AN thanks for any help M

Hello, I'm a beginner with R and it's the first time I'm using the R-help list... I hope I'm in the right place, if not: Sorry!! I need to do non linear regressions on a data set which columns are: "river.name" "Portata" "PTG.P" "PO4.P" "NT.N" "NH4.N" "NO3.N" "BOD5" "SiO2"

Hello colleagues, I am attempting to determine the nonlinear least-squares estimates of the nonlinear model parameters using nls. I have come across a common problem that R users have reported when I attempt to fit a particular 3-parameter nonlinear function to my dataset: Error in nls(r ~ tlm(a, N.fix, k, theta), data = tlm.data, start = list(a = a.st, : step factor 0.000488281

Hi R experts, I'm trying to use nls() for a piecewise linear regression with the first slope constrained to 0. There are 10 data points and when it does converge the second slope is almost always over estimated for some reason. I have many sets of these 10-point datasets that I need to do. The following segment of code is an example, and sorry for the overly precise numbers, they are just

Dear Friends. I tried to use nls.control() to change the 'minFactor' in nls( ), but it does not seem to work. I used nls( ) function and encountered error message "step factor 0.000488281 reduced below 'minFactor' of 0.000976563". I then tried the following: 1) Put "nls.control(minFactor = 1/(4096*128))" inside the brackets of nls, but the same error message

Colleagues, I am using "nls" successfully (2.11.1, OS X) but I am having difficulties retrieving part of the output - residual sum of squares. I have assigned the output to FIT: > > FIT > Nonlinear regression model > model: NEWY ~ PMESOR + PAMPLITUDE * cos(2 * pi * (NEWX - POFFSET)/PERIOD) > data: parent.frame() > PMESOR PAMPLITUDE POFFSET >