Ruqiang Liang <ruqiang.liang <at> gmail.com> writes: > > I have a table like the following. I want to fit Cm to Vm like this: > Cm ~Cl+Q1*b1*38.67*exp(-b1*(Vm-Vp1)*0.03867)/(1+exp(-b1*(Vm-Vp1)*0.03867))^2+ Q2*b2*38.67*exp(-b2*(Vm-Vp2)*0.03867)/(1+exp(-b2*(Vm-Vp2)*0.03867))^2 > > I use nls, with start=list(Q1=2e-3, b1=1, Vp1=-25, Q2=3e-3, b2=1, > Vp2=200). But I always get 'singular gradient' error like this. But > in SigmaPlot I can get the result. How can I get with R.
I remember a similar case in gastric emptying fitting where my colleague tried to show me that SigmaPlot is superior to R/nls. When he looked at the standard deviations of the coefficients, giving estimated gastric empty times of 40 minutes plus minus 800 minutes, I was reminded of Douglas Bates' philosophy: better nothing than wrong. (Even if I would love to have estimated p-values back in lmer fits.) This said, there are two solutions: first, simplify your expression to see what you are doing. The first part of the expression can be written as R1*exp(-c1*(Vm-Vp1))/(1+exp(-c1*(Vm-Vp1))^2 where you are free to compute Q1 and b1 later. You will also note that your fit cannot have a unique solution because of the symmetry in the second term. As a next step, try to fit R1*exp(-exp(d1)*(Vm-Vp1))/(1+exp(-exp(d1)*(Vm-Vp1))^2 This gives a working solution in most cases, effectively forcing a positive c1=exp(d1). However, even that may fail in degenerate cases. If it is not a single fit, but from a planned pharmacology related experiment, you should try to fit the whole experiment with all repeats instead of single curves. This often gives excellent results even when some fits are disastrous. Check package nlme, the book by Pinheiro/Bates, and some related examples on http://www.menne-biomed.de/gastempt Dieter ______________________________________________ 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.
