[R] question about non-linear least squares in R

[apjaworski at mmm.com]

Here is one way of getting a reasonable fit:

1.  Scale your y's by dividing all values by 1e6.
2.  Plot x vs. y.  The plot looks like a quadratic function.
3.  Fit a quadratic const. + B*x^2 - this a linear regression problem so
use lm.
4.  Plot the predictions.
5.  Eyball the necessary shift - MA is around 0.01.  Refit const. +
B*(x-.01)^2.  Should get const.=1.147 and B=139.144
6.  Use start=list(const.= 1.147, A=0, B=1.147, MA=.01).  nls should
converge in 4 iterations.

In general, good starting points may be crucial to nls convergence.
Scaling the y's to reasonable values also helps.

Hope this helps,

Andy

__________________________________
Andy Jaworski
518-1-01
Process Laboratory
3M Corporate Research Laboratory
-----
E-mail: apjaworski at mmm.com
Tel:  (651) 733-6092
Fax:  (651) 736-3122


                                                                           
             "Yu (Warren)                                                  
             Wang"                                                         
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                                                                   Subject 
             09/05/2007 02:51          [R] question about non-linear least 
             AM                        squares in R                        
                                                                           
                                                                           
                                                                           
                                                                           
                                                                           
                                                                           




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

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