You haven't told us how you are fitting the model; are you using nls(), and if so with what initial values? The models don't make sense at x=0, due to the inclusion of the log(x) term. Ignoring that, you have 5 observations and 5 parameters in your second model. What is the reason you are including both "b*log(x)" and "c*x" terms in the model?
regards albyn ----------------------------------------------------------------------- On Thu, Jan 12, 2006 at 07:11:12PM +0100, [EMAIL PROTECTED] wrote: > Hi! > > I have a problem of curve fitting. > > I use the following data : > > - vector of predictor data : > 0 > 0.4 > 0.8 > 1.2 > 1.6 > > - vector of response data : > 0.81954 > 0.64592 > 0.51247 > 0.42831 > 0.35371 > > I perform parametric fits using custom equations > > when I use this equation : y = yo + K *(1/(1+exp(-(a+b*ln(x))))) the > fitting result is OK > but when I use this more general equation : y = yo + K > *(1/(1+exp(-(a+b*log(x)+c*x)))) , then I get an aberrant curve! > > I don't understand that... The second fitting should be at least as good > as the first one because when taking c=0, both equations are identical! > > There is here a mathematical phenomenon that I don't understand!....could > someone help me???? > > Thanks a lot in advance! > > Nad?ge > > [[alternative HTML version deleted]] > > ______________________________________________ > [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 ______________________________________________ [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
