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Re: Rationalized Fitting

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  • Subject: [mg125437] Re: Rationalized Fitting
  • From: Darren Glosemeyer <darreng at>
  • Date: Wed, 14 Mar 2012 00:35:52 -0500 (EST)
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  • References: <jhienb$b3o$> <>

On 3/13/2012 3:01 AM, Antonio Alvaro Ranha Neves wrote:
> No reply? Guess it's harder than it looks.
> On Thursday, February 16, 2012 9:28:27 AM UTC+1, Antonio Alvaro Ranha Neves wrote:
>> Hello group members and advanced users,
>> Recently, I was trying to obtain the best fitting function with rational parameters, without success. I tried something like,
>> NoisyParabola =
>>   Table[{x, (Prime[7]/Prime[8] + x Prime[9]/Prime[10] +
>>        Prime[11]/Prime[12] x^2)*RandomReal[{.95, 1.05}]}, {x, 1, 10,
>>     1/4}]
>> NLMFit = NonlinearModelFit[NoisyParabola,
>>    Rationalize[a, 10^-6] + x Rationalize[b, 10^-6] +
>>     Rationalize[c, 10^-6] x^2, {a, b, c}, x]
>> NLMFit["ParameterTable"]
>> The main idea is to obtain the fitting coefficients (a,b,c) whose standard deviation (da,db,dc), would yield a fitting result of a best fit rational Rationalize[a,da]. But I fail to see how I can get this interactively.
>> Hope I made myself clear,
>> Thanks,
>> Antonio

You could rationalize the end result, e.g.

Rationalize[NLMFit["ParameterTable"], 10^-6]

a, b and c in the model are inexact numbers which get rationalized in 
the formula. Since the model is actually linear in the parameters, you 
could use LinearModelFit with WorkingPrecision->Infinity to get an exact 
result but that will include huge Root object expressions, so you would 
still need to rationalize an inexact numeric approximation of the result 
like in the following to get rationals.

lm = LinearModelFit[NoisyParabola, {x, x^2}, x,
    WorkingPrecision -> Infinity];
Rationalize[N[lm["ParameterTable"], 20], 10^-6]

Darren Glosemeyer
Wolfram Research

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