One thing to note is that there are more than one model that can be called exponential. Two of the common ones are:
y = exp( a + b*x + error ) y = exp( a + b*x ) + error The common way to fit the first is to take the log of both sides and just fit a linear model with log(y), I expect (but am not sure) that that is what Excel does. It is likely that the reason you get different results is that you are fitting different models with the 2 programs. You first need to decide which is the correct model (and it may be different from the 2 already mentioned), then worry about fitting that model and what goes with that. On Mon, Jun 4, 2012 at 6:19 AM, Nerak <nera...@hotmail.com> wrote: > Hi all, > > Like a lot of people I noticed that I get different results when I use nls > in R compared to the exponential fit in excel. A bit annoying because often > the R^2 is higher in excel but when I'm reading the different topics on this > forum I kind of understand that using R is better than excel? > > (I don't really understand how the difference occurs, but I understand that > there is a different way in fitting, in excel a single value can make the > difference, in R it looks at the whole function? I read this: "Fitting a > function is an approximation, trying to find a minimum. Think of frozen > mountain lake surrounded by mountains. Excel's Solver will report the > highest tip of the snowflake on the lake, if it finds it. nls will find out > that the lake is essentially flat compare to the surrounding and tell you > this fact in unkind word." ) > > > I have several questions about nls: > > 1. The nls method doesn't give an R^2. But I want to determine the quality > of the fit. To understand how to use nls I read "Technical note: Curve > fitting with the R environment for Statistical Computing". In that document > they suggested this to calculate R^2: > > RSS.p<-sum(residuals(fit)^2) > TSS<-sum((y-mean(y))^2) > r.squared<-1-(RSS.p/TSS) > LIST.rsq<-r.squared > > (with fit my results of the nls: formula y ~ exp.f(x, a, b) : y : > a*exp(-b*x)) > > While I was reading on the internet to find a possible reason why I get > different results using R and excel, I also read lots of different things > about the "R^2 problem" in nls. > > Is the method I'm using now ok, or should someone suggest to use something > else? > > 2. Another question I have is like a lot of people about the singular > gradient problem. I didn't know the best way to chose my starting values for > my coefficients. when it was too low, I got this singular gradient error. > Raising the value helped me to get rid of that error. Changing that value > didn't change my coefficients nor R^2. I was wondering if that's ok, just to > raise the starting value of one of my coefficients? > > The only things that change are the Achieved convergence tolerance and > number of iterations to convergence. P values, residual standard error and > the coefficients have always exactly the same results. What does the > achieved convergence tolerance actually mean? What are its implications? (I > suppose the time to calculate it changes) > > (the most useful information about nls and singular gradient error i found > is this one (and that's why I started playing with changing the starting > values): > " if the estimate of the rank that results is less than the number of > columns in the gradient (the number of nonlinear parameters), or less than > the number of rows (the number of observations), nls stops.") > > > I hope someone can help me with this questions. I would like to know what's > happening and not just having to accept the results I get now :). > > Kind regards, > > Nerak > > > -- > View this message in context: > http://r.789695.n4.nabble.com/Non-linear-curve-fitting-nls-starting-point-and-quality-of-fit-tp4632295.html > Sent from the R help mailing list archive at Nabble.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. -- Gregory (Greg) L. Snow Ph.D. 538...@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.
