Re: nonlinear regression curve fitting

*To*: mathgroup-adm at yoda.ncsa.uiuc.edu, mathgroup at yoda.ncsa.uiuc.edu*Subject*: Re: nonlinear regression curve fitting*From*: leon at extro.ucc.su.oz.au (L. Poladian)*Date*: Sat Nov 11 15:14:58 1989

In response to a recent enquiry about non-linear regression here are the definitions for a routine that can be used. I have not had time to set this all up correctly as a package with contexts etc. and have only briefly tested it. Therefore, use at your own peril. Given a function f = F[x,y,...,a,b,...] of several variables x,y,... which also depends on a number of parameters a,b,... and a list of data data = { {x1,y1,...,f1}, {x2,y2,...,f2}, ... } the routine attempts to find the values of a,b,... which minimizes the sum of the squares of the residuals. The only restriction on F[] is that Mathematica must be able to calculate symbolic derivatives of F with respect to a,b,.... An initial estimate of the paramters is required and the routine returns a "better" estimate of the parameters. If the original estimate is good enough, repeating the process will eventually produce a converged result. The definitions follow: LinearFit[list_,fun_,f0_,var_] :=Block[{n,n2,m,b}, n = Length[list]; n2 = Length[fun]; m = Table[ Sum[ (fun[[j]] fun[[k]] /. Thread[var -> Drop[list[[i]],-1]]), {i,1,n}], {j,1,n2}, {k,1,n2}]; b = Table[ Sum[ (fun[[j]] (list[[i,-1]]-f0) /. Thread[var -> Drop[list[[i]],-1]]), {i,1,n}], {j,1,n2}]; LinearSolve[m,b] ] NonLinearFit[list_,fun_,par_,init_,var_]:= Block[{npar,sub,linfun,f0,coeff}, npar = Length[par]; sub = Thread[par -> init]; linfun = Table[ D[ fun, par[[i]] ], {i,1,npar}] /.sub; f0 = fun /. sub; coeff = LinearFit[list,linfun,f0,var]; init+coeff ] The routine is used as follows: data = { {x1,y1,..,f1}, {x2,y2,...,f2}, ...}; function = F[x,y,...,a,b,...]; (* whatever function you are fitting *) pars = {a,b,... }; initialestimate = {a0, b0, ... }; (* reasonable values for the parameters *) vars = {x,y,...}; estimate = NonLinearFit[data,function,pars,initialestimate,vars] estimate = NonLinearFit[data,function,pars,estimate,vars] (*keep repeating*) Alternatively one can use FixedPoint estimate = FixedPoint[NonLinearFit[data,function,pars,#,vars]&,initialestimate] Leon Poladian Theoretical Physics University of Sydney leon at extro.ucc.su.oz