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 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 . Any suggestion how I can do this... Any help would be most appreciated. Thanks in advance. Diviya [[alternative HTML version deleted]] ______________________________________________ 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.