Hi all, I'm trying to fit a nonlinear (logistic-like) regression, and I'd like to get some recommendations for which package to use.
The expression I want to fit is something like: y ~ A * exp(X * Beta1) / (1 + exp(-(x + X * Beta2 - xmid)/scal)) Basically, it's a logistic function, but I want to be able to modify the saturation amplitude by a few parameters (Beta1) and shift the inflection point around with a few other parameters (Beta2). I have a ton of data, but I often have trouble getting the routine to fit. (I've been using nlin in SAS, which seems sloppier in terms of accepted convergence.) Now, from what I can tell, I can use nls, gnls, or gnm to fit something like this, but I can't tell which would be better, or if there's something else I should be trying. To do this right, though, I have to do a lot more reading, but I'd like to know where to start. (I have more of a physics/computer background, so I immediately jump to thinking of regression as minimizing some cost function across a multidimensional space and then start mumbling about simulated annealing or some such, but this isn't helping me much in interpreting the available literature.) So, does anyone have any suggestions? I imagine I'm going to have to pick up a book, but should it be Pinheiro & Bates on nlme, Bates & Watts, the pdf manual to gnm, or what? Thanks for any suggestions, Johann ______________________________________________ [email protected] 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.
