RE: Use of ShowProgress Option output

*To*: mathgroup at smc.vnet.net*Subject*: [mg34799] RE: [mg34783] Use of ShowProgress Option output*From*: "Annetts, Dave (E&M, North Ryde)" <David.Annetts at csiro.au>*Date*: Fri, 7 Jun 2002 01:09:04 -0400 (EDT)*Sender*: owner-wri-mathgroup at wolfram.com

Hi Andre, > ... may be my question was not clear in detail, but I realy > can't find a > solution. > I will show you an example from the help browswer to > ilustrate my problem. > > > data={{1.0,1.0,.126},{2.0,1.0,.219},{1.0,2.0,.076},{2.0, > 2.0,.126},{.1,.0,.186}}; > > NonlinearRegress[data, > theta1 theta3 x1/(1+theta1 x1+theta2 > x2),{x1,x2},{theta1,theta2,theta3}, > RegressionReport->BestFitParameters,ShowProgress->True] In essence, you want to retain the values of Chi^2 and your model parameters? Short of modifying the code in the package, I don't see how NonlinearRegress is going to give you this so you'll need to do things "manually". The idea is to loop, and append the results of each fit to a list which you can plot. Something like the following block should get you started. diff[chsq_, csqo_] := 100. * Abs[chsq - csqo]/chsq; Off[FindMinimum::fmlim]; prog = {}; csqo = 1.; i = 0; {ith1, ith2, ith3} = {1., 1., 1.}; While[diff[chsq, csqo] > 1., tmp = NonlinearRegress[data, th1 th3 x1 /(1 + th1 x1 + th2 x2), {x1, x2}, {{th1, ith1}, {th2, ith2}, {th3, ith3}}, RegressionReport -> {FitResiduals, BestFitParameters}, MaxIterations -> 1 ] ; i += 1; csqo = chsq; chsq = #^2 & /@ (FitResiduals /. tmp); chsq = Apply[Plus, chsq]; Print["it = ", i, " ", chsq, " ", csqo, " ", diff[chsq, csqo], " ", {ith1, ith2, ith3} ]; ith1 = th1 /. (BestFitParameters /. tmp); ith2 = th2 /. (BestFitParameters /. tmp); ith3 = th3 /. (BestFitParameters /. tmp); lst = {chsq, {BestFitParameters /. tmp}}; prog = AppendTo[prog, lst]; ] Points to note are:- 1. The code is in no sense elegant or efficient -- I recall something like this using FindMinimum in a FAQ on WRI's site so you may want to search there further; 2. For a given tolerance, altering MaxIterations gives you multiple "bites" per step -- at 1, convergence is in 29 its but at 2, convergence is in 5 steps.; 3. Convergence rates are dependent upon both the definition of "diff" and the tolerance; Regards, Dave. -------------------------------------------------------- Dr. David Annetts EM Modelling Analyst Tel: (+612) 9490 5416 CSIRO DEM, North Ryde Fax: (+612) 9490 5467 David.Annetts at csiro.au Include "usual_disclaimers" --------------------------------------------------------