similar to: Using the nls package

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'm having problems getting nls to agree that convergence has occurred in a toy problem. nls.out never gets defined when there is an error in nls. Reaching the maximum number of iterations is alway an error, so nls.out never gets defined when the maximum number of iterations is reched. >From ?nls.control: tol: A positive numeric value specifying the tolerance level for the

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]]

Hi, I am using the nls function and it stops because the number of iterations exceeded 50, but i used the nls.control argument to allow for 500 iterations. Do you have any idea why it's not working? fm1 <- nls(npe ~ SSgompertz(npo, Asym, b2, b3), data=f,control=nls.control(maxiter=500)) Error in nls(y ~ exp(-b2 * b3^x), data = xy, algorithm = "plinear", start = c(b2 =

Hello, to plot the result of a singular non linear regression (using nls) I usually use the function plotfit, for example: r.PTG.V<-nls(PTG.P~ fz1(Portata, a,b), data=dati, start=list(a=10, b=10), nls.control(maxiter=200), algorithm='port', trace=TRUE, na.action=na.omit, lower=list(a=0, b=10), upper=list(a=100, b=100)) plotfit(r.PTG.V) I tried to use the function plotfit on the

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

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 could use some help understanding how nls parses the formula argument to a model.frame and estimates the model. I am trying to utilize the functionality of the nls formula argument to modify garchFit() to handle other variables in the mean equation besides just an arma(u,v) specification. My nonlinear model is y<-nls(t~a*sin(w*2*pi/365*id+p)+b*id+int,data=t1,

Dear all, we are writing a wrapper for the nls function in library stats. We are having a problem with one of the arguments (weightsArgument) which seems not to reach nls even if we explicitly assign it in the function call. We are attaching the simplest code reproducing the error and the output calling the wrapper and calling nls directly. We are using R 2.9.0 library(stats) wrappernls <-

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

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

Subject: nls, step factor 0.000488281 reduced below 'minFactor' of 0.000976563 Hi there, I'm trying to conduct nls regression using roughly the below code: nls1 <- nls(y ~ a*(1-exp(-b*x^c)), start=list(a=a1,b=b1,c=c1)) I checked my start values by plotting the relationship etc. but I kept getting an error message saying maximum iterations exceeded. I have tried changing these

Saludos a todos, Quisiera saber como ocultar algunos componentes de una lista para que cuando crees una funcion solo se muestre una parte de esa lista, y que al resto tengas acceso cuando descompongas ese objeto, por ejemplo: Tengo la siguiente data: data=data.frame(Samples=1:10,Sobs=c(12.6,22.4,27.4,30.5,32.5,34.3,36,37.4,38.9,39.9)) Cuando uso la funcion nls: A <-

When I run gnls I get the error: Error in nls(y ~ cbind(1, 1/(1 + exp((xmid - x)/exp(lscal)))), data = xy, : step factor 0.000488281 reduced below 'minFactor' of 0.000976563 My first thought was to decrease minFactor but gnlsControl does not contain minFactor nor nlsMinFactor (see below). It does however contain nlsMaxIter and nlsTol which I assume are the analogs of

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…

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

Among others, datam contains the columns: logconc, tm, dose, subj, bilirubin. None of these are factor variables. The following compartment models work (the first still has not converged after 100 interations): res1 <- nlme(logconc~p2+p3+log(dose/(exp(p1)-exp(p2))* (exp(-exp(p2)*tm)-exp(-exp(p1)*tm))),start=list(fixed=c(5,-2,-0.1)), fixed=list(p1+p2+p3~1),control=list(maxIter=100),

Hi, everyone, My question is: It's not every time that you can get a converged result from the nls function. Is there any solution for me to get a reasonable result? For example: x <- c(-0.06,-0.04,-0.025,-0.015,-0.005,0.005,0.015,0.025,0.04,0.06) y <- c(1866760,1457870,1314960,1250560,1184850,1144920,1158850,1199910,1263850,1452520) fitOup<- nls(y ~ constant + A*(x-MA)^4 +

Hola Javier, Creo que comprendio mal mi pregunta, quiero saber como ocultar algunos componentes de una lista producida por una funcion que uno crea, pero que solo esten ocultos y que pueda tener acceso a esos componentes cuando uso la funcion "str", la cual despliega todos los componentes de la lista. Además como hacer para que salga ordenado o de una forma preferida los componentes

Hi all, I would like to predict some values for an nls regression function (functional response model Rogers type II). This is an asymptotic function of which I would like to predict the asymptotic value I estimated the paramters with nls, but can't seem to get predictions for values of m choice...... This is my script: RogersII_N <-