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

[Moshe Olshansky]

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" <yu.wang at pdf.com> 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
> 
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