similar to: nls function

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

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

Dear list, I have encountered a problem with predict.nls (Windows XP, R.2.5.0), but I am not sure if it is a bug... On the nls man page, an example is: DNase1 <- subset(DNase, Run == 1) fm2DNase1 <- nls(density ~ 1/(1 + exp((xmid - log(conc))/scal)), data = DNase1, start = list(xmid = 0, scal = 1)) alg = "plinear", trace =

Hello, I am running a simple nls model (which a friend ran OK) but I get the following error: Error in nls(y ~ R * (1 - (x/K)^2), data = nls.dat, start = list(R = 0.3, : object 'R_nls_iter' not found Does anyone know what the 'R_nls_iter' error is? The data are: x=1:8 ; y=c(14,19,25,34,43,56,69,76) # starting values: R=.3, K=94 Thanks in advance. Jeff

Hi all, I need to use the parameter estimates of nls() for further analysis. I know how to do in S+, e.g. nls(...)$parameters. In R, the attributes(nls(...)) does not have parameters, how would one get the parameter values out of nls()? Thanks in advance. Nancy

I want to fit the model y=a*x^b using nls; where "a" should be different for each level of a factor. What is the easiest way to fit it? Can i do it with nls? I've looked the help pages and the MASS example in page 249 but the formula is different and I don't know how to specify it for my model. Thanks, Manuel [[alternative HTML version deleted]]

We would like to fit parameters using a simulation with stochastic processes as theoretical values. We generate a simple exemple with nls.lm to see the logic and the problem: First without stochasticity (it is a dummy example, the fited value is simple the mean of a set of 10 numbers): #Ten numbers x <- 1:10 #Generate 10 Gaussian random number with mean=3 sd=1 simy <- rnorm(length(x),

I'm running into problems trying to use the nls function to fit the some data. I'm invoking nls using nls(s~k/(a+r)^b, start=list(k=1, a=13, b=0.59)) but I get errors indicating that the step has been reduced below the minimum step size or an inifinity is generated in numericDeriv. I've tried to use a variety of starting values for a, b, k but get similar errors. Is there

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)

logLik.nls does not count the df's correct. I get df=1 although I fit a probit-model with 3 parameters. Example: x <- c(-2.3, -2.0, -1.3, -1.0, -0.7, -0.3, 0.0, 0.3) y <- c(80, 80, 54, 43, 24, 18, 12, 12) fit.nls <- nls(y ~ diff * pnorm(beta * (x - alpha)), start=c(alpha=-1, beta=-1, diff=100)) logLik.nls(fit.nls) # `log Lik.' -21.43369 (df=1) Sincerely

HI, i have some prob when i try to use nls(). my data is 1D vector, I tried to use a polynomial function(order is 3) to fit it. the data series is stored in x. the a0, a1, a2, a3 below is coefficient, which i hope i can get from calls "nls" > z <- nls( ~ a0 + a1 * x + a2 * x * x + a3 * x * x * x, data = x ) Error in match.call(definition, call, expand.dots) : .Primitive... is

Full_Name: Michael Montero Version: 1011 OS: NT4.0 Submission from: (NULL) (128.32.148.191) I haven't been able to use the nls() function because of the following error: Error in eval.with.vis(expr, envir, enclos) : couldn't find function "nls" Apparently nls() is no where to be found, in the /library directory nls exists with all its files.. need help.. thanks

Hi, I was trying to run the example of Indomethacin kinetics from the book: ## From Pinheiro/Bates, Mixed-Effects-Models in S and S-Plus, ## Springer, Second Printing 2001, Section 6.2 library(nlme) plot(Indometh) fm1Indom.nls <- nls(conc~SSbiexp(time,A1,lrc1,A2,lrc2), data=Indometh) summary(fm1Indom.nls) plot(fm1Indom.nls,Subject~resid(.),abline=0) ## .... the last plot command gives me the

Hello everyone, I'm trying to test the accurracy of R on the Eckerle4 dataset from NIST and I don't understand how the control option of the nls function works. I tought nls(...) was equivalent to nls(...control=nls.control()) i.e nls.control() was the default value of control, but here is the error I get : > n2=nls(V1~(b1/b2) *

Full_Name: Tadashi Kadowaki Version: 2.4.0 OS: Redhat Linux 9 Submission from: (NULL) (58.12.166.67) Doesn't nls function support subset? It seems not to work. And, there are no information in the online help. Has it sunk into oblivion?

Dear all, I noticed the following under R 1.8.1 (when nls was still a separate package) but the same problem occurs under R 1.9.0 (where most (all?) of nls is now in the stats package): > data(Puromycin) > fm <- nls(rate~SSmicmen(conc,b0,b1), Puromycin, subset = state=="treated") > coef(summary(fm)) NULL The problem seems to be that summary.nls uses the name

Dear R Community, I am attempting to perform a weighted non-linear least squares fit. It has already been noted that the weights option is not yet implemented for the nls function, but no one seems to offer any suggestions for getting around this problem. I am still curious if a) anyone has code they have written which includes a weight options for nls, or b) if there is another model which

Hello. I am trying to do a non-linear fit using the 'nls' command. The data that I'm using is as follows pH k 1 3.79 34.21 2 4.14 25.85 3 4.38 20.45 4 4.57 15.61 5 4.74 12.42 6 4.92 9.64 7 5.11 7.30 8 5.35 5.15 9 5.67 3.24 with a transformation of pH to H <- 10^-pH When using the nls command for a set of parameters - a, b and c, I receive two sets of errors: >

I need to make nonlinear regression with the posterior script, but how is the problem? I have error in library (nls), package 'nls' has been merged into 'stats'. I need help? What other forms I have to make nonlinear regression? and how I find to calculate statistics y residuals, scatterplot. thanks SCRIPT ros<-read.table("Dataset.csv",header=T,sep=",")

A colleague of mine is trying to use nls() to effect an optimization, and is encountering a scoping problem. I should know how to solve it for him but .... well, I just don't. I also had a quick scrounge of the archives --- I know I've seen this topic addressed before --- but I couldn't track it down. So here's a toy example that demonstrates the problem: hhh <-