Re: Model fitting

*To*: mathgroup at smc.vnet.net*Subject*: [mg128783] Re: Model fitting*From*: Dmitry Zinoviev <dzinoviev at gmail.com>*Date*: Sun, 25 Nov 2012 05:00:51 -0500 (EST)*Delivered-to*: l-mathgroup@mail-archive0.wolfram.com*Delivered-to*: l-mathgroup@wolfram.com*Delivered-to*: mathgroup-newout@smc.vnet.net*Delivered-to*: mathgroup-newsend@smc.vnet.net*References*: <k8nc4m$dfl$1@smc.vnet.net> <k8psuu$irk$1@smc.vnet.net>

On Saturday, November 24, 2012 2:30:51 AM UTC-5, Ray Koopman wrote: > On Nov 23, 12:31 am, dzinov... at 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)]). Than you! I assume that b=b2/b1. How do I calculate a?