> -----Original Message-----
> On 11/24/2015 09:32 PM, Judson wrote:
> > I need to fit a sinusoidal curve to
> > x-y data that exhibits a sinusoidal
> > pattern. The curve will be:
> > y = a*sin(w*x +p) ;
> > where I need to get the best
> > fit choice for the parameters
> > a, w, and p. Could anyone
> > suggest which package and
> > routine I should use? I have
> > less than 1000 data points.
> > Can this problem be somehow
> > coerced into a linear fit?
> > ....... judson blake
>
> You may take a look at the nlme library.
> --
> Ulises M. Alvarez
nlme includes a nonlinear _mixed effects_ model, but non-linear least squares
fitting is well catered for already. nlm, nls and optim in the core
distribution all cover non-linear fitting.
But you'll need good starting values.
Life could be easier with a reformulation expanding sin(w*x + p) to
y = alpha sin(w*x) + beta * cos(w*x)
where alpha=a*cos(p) and beta = a * sin(p)
(if my mental trig is working)
Given a good starting value for w (eg from an FFT) that would allow an initial
linear (ie lm() ) fit to cos(w*x) + sin(w*x) to get alpha and beta, and hence
a and p. Those values could then be used as starting values for optim or
similar.
S Ellison
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