Hi, I am trying to fit a 4-parameter logistic model to my gradient data using nls. I tried to specify the model directly in the nls formula and also tried to use the self-start function SSfpl. For the following data, the first method worked, but the second didn't. I thought both ways were equivalent, can anyone tells me why?
> test=data.frame(cbind(conc=c(13294,3940,1170,346,102,30.20,8.94,2.65,13294,3940,1170,346,102,30.20,8.94,2.65), signal=c(2609,487,110,35,17.5,16,11,12.5,2682,292.5,51.5,25.5,14,11,14,15))) > nls(log(signal)~A+(B-A)/(1+exp((xmid-log(conc))/scal)),data=test,start = list(A=log(5), B=log(3000), xmid=log(6000),scal=0.8)) Residual sum of squares : 0.6649545 parameters: A B xmid scal 2.494311 10.92275 8.752043 1.308763 formula: log(signal) ~ A + (B - A)/(1 + exp((xmid - log(conc))/scal)) 16 observations > nls(log(signal)~SSfpl(log(conc),log(5),log(3000),log(6000),0.8), data=test) Error in nlsModel(formula, mf, start) : singular gradient matrix at initial parameter estimates Thanks! ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help