Below is one possibility: If you knew MA you would get a regular linear least-squares for parameters A,B and constant which can be easily solved. So now you can define a function f(MA) which returns that value. Now you must minimize that f - a function of one argument. It can have several local minima and so you must be careful but I believe that minimizing (even "bad") function of one argument should be easier than your original problem.
Regards, Moshe. P.S. if you do this I would be interested to know whether this works. --- "Yu (Warren) Wang" <[EMAIL PROTECTED]> wrote: > Hi, everyone, > My question is: It's not every time that you can > get a converged > result from the nls function. Is there any solution > for me to get a > reasonable result? For example: > > x <- > c(-0.06,-0.04,-0.025,-0.015,-0.005,0.005,0.015,0.025,0.04,0.06) > > y <- > c(1866760,1457870,1314960,1250560,1184850,1144920,1158850,1199910,1263850,1452520) > > fitOup<- nls(y ~ constant + A*(x-MA)^4 + B*(x-MA)^2, > > start=list(constant=10000000, A=100000000, > B=-1000000, MA=0), > control=nls.control(maxiter=100, minFactor=1/4096), > trace=TRUE) > > > > For this one, I cannot get the converged result, > how can I reach it? To > use another funtion or to modify some settings for > nls? > > Thank you very much! > > Yours, > > Warren > > ______________________________________________ > R-help@stat.math.ethz.ch 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. > ______________________________________________ R-help@stat.math.ethz.ch 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.