Re: Re: Asking NonlinearRegression

*To*: mathgroup at smc.vnet.net*Subject*: [mg88815] Re: [mg88781] Re: Asking NonlinearRegression*From*: "Thomas Dowling" <thomasgdowling at gmail.com>*Date*: Sat, 17 May 2008 05:31:12 -0400 (EDT)*References*: <g0h4kd$la4$1@smc.vnet.net> <200805160933.FAA01135@smc.vnet.net>

Hello, Are you sure about this? Proceeding as you outlined ... In[375]:= d1 = {1, 0.00, 2, 0.00, 3, 0.00, 4, 0.00, 5, 0.00, 6, 0.00, 7, 0.00, 8, 0.00, 9, 0.00, 10, 0.00, 11, 0.00, 12, 0.00, 13, 0.00, 14, 0.00, 15, 0.00, 16, 0.00, 17, 0.00, 18, 0.00, 19, 0.00, 20, 0.00, 21, 0.00, 22, 0.00, 23, 0.00, 24, 0.00, 25, 0.00, 26, 0.00, 27, 0.00, 28, 0.00, 29, 0.00, 30, 0.00, 31, 0.00, 32, 0.00, 33, 0.00, 34, 0.00, 35, 0.00, 36, 0.00, 37, 0.00, 38, 0.00, 39, 0.00, 40, 0.00, 42, 0.00, 44, 0.00, 46, 0.00, 48, 0.00, 50, 0.00, 52, 0.04, 54, 0.06, 56, 0.08, 58, 0.09, 60, 0.16, 65, 0.24, 70, 0.32, 75, 0.43, 80, 0.49, 85, 0.58, 90, 0.80, 95, 0.91, 100, 1.00, 105, 1}; In[376]:= fun = Exp[(1 - Exp[a*(1 - Exp[-b*x])])*(Exp[-b*x])/(1 - Exp[-b*x])]; In[412]:= paramrules = FindFit[d1, fun, {a, b}, x] Out[412]= {a -> 0.648266, b -> 1.02124} If I now plot the (partitioned) data and the fitted function, as follows: In[419]= plot = Plot[fun /. paramrules, {x, 0, 105}, AxesOrigin -> {0, 0}]; listplot = ListPlot[Partition[d1, 2], PlotRange -> {-.05, 1.1 }, PlotStyle -> {Green, PointSize[Large]}]; Show[plot, listplot] This does not seem to be a reasonable fit to me. Furthermore, the following gives an overflow error message. Are you sure you are trying to fit to an appropriate function? In[422]= FindFit[Partition[d1, 2], fun, {a, b}, x] IGeneral::ovfl: Overflow occurred in computation. >> General::ovfl: Overflow occurred in computation. >> General::ovfl: Overflow occurred in computation. >> General::stop: Further output of General::ovfl will be suppressed \ during this calculation. >> FindFit::nrlnum: The function value <<1>> is not a list of real \ numbers with dimensions {59} at {a,b} = {-1.80801,-5.23877}. Out[422]= {a -> -1.80801, b -> -5.23877} I have not been able to obtain a reasonable fit to your data even using 'manual' values for 'a' and 'b' in 'fun'. Maybe fit to a different function? Tom Dowling On Fri, May 16, 2008 at 10:33 AM, dh <dh at metrohm.ch> wrote: > > > Hi Navri, > > NonlinearRegression as well as FindFit (should be used instead of the > > older NonlinearFit) seem to work as expected: > > fun=Exp[(1-Exp[a*(1-Exp[-b*x])])*(Exp[-b*x])/(1-Exp[-b*x])]; > > > d1={1,0.00,2,0.00,3,0.00,4,0.00,5,0.00,6,0.00,7,0.00,8,0.00,9,0.00,10,0.00,11,0.00,12,0.00,13,0.00,14,0.00,15,0.00,16,0.00,17,0.00,18,0.00,19,0.00,20,0.00,21,0.00,22,0.00,23,0.00,24,0.00,25,0.00,26,0.00,27,0.00,28,0.00,29,0.00,30,0.00,31,0.00,32,0.00,33,0.00,34,0.00,35,0.00,36,0.00,37,0.00,38,0.00,39,0.00,40,0.00,42,0.00,44,0.00,46,0.00,48,0.00,50,0.00,52,0.04,54,0.06,56,0.08,58,0.09,60,0.16,65,0.24,70,0.32,75,0.43,80,0.49,85,0.58,90,0.80,95,0.91,100,1.00,105,1}; > > FindFit[d1,fun,{a,b},x] > > NonlinearRegress[d1, fun, {a, b}, x] > > Could it bee that you had a syntax error? > > hope this helps, Daniel > > > > > > Navri Navri Bintang wrote: > > > Hi, > > > > > > I have a problem with nonlinear regression in Mathematica. I want to fit > my data (below) to this following equation: > > > > > > y = Exp[(1-Exp[a*(1-Exp[-b*x])])*(Exp[-b*x])/(1-Exp[-b*x])] > > > > > > and I would like to find a and b values and also determined r-squared > value. > > > > > > I try to use NonlinearRegression/NonlinearFit code, but its not working, > I hope someone could help me > > > > > > Thanks a lot > > > > > > DATA: > > > x y > > > 1 0.00 > > > 2 0.00 > > > 3 0.00 > > > 4 0.00 > > > 5 0.00 > > > 6 0.00 > > > 7 0.00 > > > 8 0.00 > > > 9 0.00 > > > 10 0.00 > > > 11 0.00 > > > 12 0.00 > > > 13 0.00 > > > 14 0.00 > > > 15 0.00 > > > 16 0.00 > > > 17 0.00 > > > 18 0.00 > > > 19 0.00 > > > 20 0.00 > > > 21 0.00 > > > 22 0.00 > > > 23 0.00 > > > 24 0.00 > > > 25 0.00 > > > 26 0.00 > > > 27 0.00 > > > 28 0.00 > > > 29 0.00 > > > 30 0.00 > > > 31 0.00 > > > 32 0.00 > > > 33 0.00 > > > 34 0.00 > > > 35 0.00 > > > 36 0.00 > > > 37 0.00 > > > 38 0.00 > > > 39 0.00 > > > 40 0.00 > > > 42 0.00 > > > 44 0.00 > > > 46 0.00 > > > 48 0.00 > > > 50 0.00 > > > 52 0.04 > > > 54 0.06 > > > 56 0.08 > > > 58 0.09 > > > 60 0.16 > > > 65 0.24 > > > 70 0.32 > > > 75 0.43 > > > 80 0.49 > > > 85 0.58 > > > 90 0.80 > > > 95 0.91 > > > 100 1.00 > > > 105 1 > > > > > > > > > Rgds, > > > Navri > > > > > > > > > > > > > > > > >

**References**:**Re: Asking NonlinearRegression***From:*dh <dh@metrohm.ch>

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**Re: Asking NonlinearRegression**

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