Re: nonlinearmodelfit problem

*To*: mathgroup at smc.vnet.net*Subject*: [mg126105] Re: nonlinearmodelfit problem*From*: "Dan O'Brien" <danobrie at gmail.com>*Date*: Wed, 18 Apr 2012 03:52:30 -0400 (EDT)*Delivered-to*: l-mathgroup@mail-archive0.wolfram.com*References*: <jm4vr2$2j0$1@smc.vnet.net> <201204171002.GAA08442@smc.vnet.net>

I have had the same sort of problems. How have you set up your simultaneous fit? If it is set up so that your data is in the form {{1,x11,y11},{1,x12,y12}......{2,x2n,y2n}} where the first index is for the dataset (1 or 2) and your function is set up with kroneckerdelta nlm[dataset,x,parameters]:=KroneckerDelta[dataset-1]nlm1[parameters]+KroneckerDelta[dataset-2]nlm2[parameters] Then you could use the Weights option in NonlinearModelFit Weights->(If[#1==2,100000,1]&) (if the larger ranged dataset is dataset 1) -Dan On 4/17/2012 5:02 AM, M.Roellig wrote: > On Thursday, April 12, 2012 12:16:02 AM UTC+2, L. Mattera wrote: >> Hi >> my name is Lorenzo Mattera, I am using since a short time Mathematica. >> I am trying to solve a fitting problem where two sets of data have to be >> fitted by two models depending on the same parameters. >> I found very helpful the suggestions reported at >> http://forums.wolfram.com/mathgroup/archive/2011/Sep/msg00555.html (and >> references therein) so that I was able to >> produce a working procedure. However, I have a "small" problem: >> the two set of data have quite different weights as one is in the >> 0-100000 range while the other is in the 0-1 range so that one of the two is >> irrelevant in the fitting procedure. >> It seems to me that NonlinearModelFit minimizes the sum of the squared >> residuals, >> any way to minimize the squared normalized residuals? >> Perhaps is one of the options, but so far I could not find it. >> Tank you for the help >> best regards >> L. Mattera > Hi, > > you could try to fit in the logarithmic domain. > > Markus >

**References**:**Re: nonlinearmodelfit problem***From:*"M.Roellig" <markus.roellig@googlemail.com>