On Jun 12, 2011, at 18:57 , Diviya Smith wrote: > Hello there, > > I am trying to fit an exponential fit using Least squares to some data. > > #data > x <- c(1 ,10, 20, 30, 40, 50, 60, 70, 80, 90, 100) > y <- c(0.033823, 0.014779, 0.004698, 0.001584, -0.002017, -0.003436, > -0.000006, -0.004626, -0.004626, -0.004626, -0.004626) > > sub <- data.frame(x,y) > > #If model is y = a*exp(-x) + b then > fit <- nls(y ~ a*exp(-x) + b, data = sub, start = list(a = 0, b = 0), trace > = TRUE) > > This works well. However, if I want to fit the model : y = a*exp(-mx)+c then > I try - > fit <- nls(y ~ a*exp(-m*x) + b, data = sub, start = list(a = 0, b = 0, m= > 0), trace = TRUE) > > It fails and I get the following error - > Error in nlsModel(formula, mf, start, wts) : > singular gradient matrix at initial parameter estimates
If a==0, then a*exp(-m*x) does not depend on m. So don't use a=0 as initial value. > > Any suggestions how I can fix this? Also next I want to try to fit a sum of > 2 exponentials to this data. So the new model would be y = a*exp[(-m1+ > m2)*x]+c . That's not a sum of exponentials. Did you mean a*(exp(-m1*x) + exp(-m2*x)) + c? Anyways, same procedure with more parameters. Just beware the fundamental exchangeability of m1 and m2, so don't initialize them to the same value. > Any suggestion how I can do this... Any help would be most > appreciated. Thanks in advance. -- Peter Dalgaard Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 Frederiksberg, Denmark Phone: (+45)38153501 Email: pd....@cbs.dk Priv: pda...@gmail.com ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
