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Re: 3D fitting of data points
*To*: mathgroup at smc.vnet.net
*Subject*: [mg48499] Re: [mg48433] 3D fitting of data points
*From*: Daniel Lichtblau <danl at wolfram.com>
*Date*: Wed, 2 Jun 2004 04:22:12 -0400 (EDT)
*References*: <200405290706.DAA20433@smc.vnet.net>
*Sender*: owner-wri-mathgroup at wolfram.com
Tolonen, Ellen N wrote:
> Hello all,
>
> I am trying to fit a set of 3D data points to a polynomial function
> (a(1)x^2 + a(2)y^2+a(3)z^2 + a(4)xy + a(5)xz + a(6)yz + a(7)x + a(8)y +
> a(9)z - 1 = 0). Is there a way to do three-dimensional fitting with
> NonLinearFit? And, if so, how?
>
> Thanks,
>
> Ellen
> entolonen at utep.edu
You are probably better off using a linear fit. THis can be done as
below. We start with data points with random fluctuation from a
particular polynomial in three variables.
data = Table[Module[{x,y,z}, {x,y,z} = Table[Random[Real,{-5,5}],{3}];
{x,y,z,3*x^2+2*y^2-z^2+4*x*y-7*x*z-2*y*z-x+6*y+2*z-1+.05*Random[]}],
{50}];
Now do the fit.
vars = {x,y,z};
basis = {x^2,y^2,z^2,x*y,x*z,y*z,x,y,z,1};
ffit = Fit[data, basis, vars];
last normalize so the constant term is -1 as per specification.
const = ffit /. {x->0,y->0,z->0};
Expand[-ffit/const]
YOu might also use FindFit to get the results for the fit parameters in
a possibly more convenient form.
Daniel Lichtblau
Wolfram Research
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