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Re: Model fitting
Posted:
Nov 24, 2012 2:28 AM
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On Nov 23, 12:31 am, dzinov...@gmail.com wrote:
> I have an array of 3D data in the form {xi,yi,0/1} (that is, the z coordinate is either 0 or 1). The points are not on a rectangular grid. The 0 and 1 areas are more or less contiguous, though the boundary between them can be somewhat fuzzy. The boundary is expected to be described by the equation y=a x^b. How can I adapt NonlinearModelFit or any other standard function to find the best fit values for a and b? Thanks!
y = a x^b is linear in log-log coordinates, so use LogitModelFit
with Log@x and Log@y as the predictors; i.e., the probability of
observing z == 1 is 1/(1 + Exp[-(b0 + b1*Log@x + b2*Log@y)]).
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