Re: Rationalized Fitting
- To: mathgroup at smc.vnet.net
- Subject: [mg125446] Re: Rationalized Fitting
- From: Ray Koopman <koopman at sfu.ca>
- Date: Wed, 14 Mar 2012 00:39:00 -0500 (EST)
- Delivered-to: l-mathgroup@mail-archive0.wolfram.com
- References: <jhienb$b3o$1@smc.vnet.net> <jjmuv3$cen$1@smc.vnet.net>
On Mar 13, 1:02 am, Antonio Alvaro Ranha Neves <ane... at gmail.com> 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 Two things. First, the data you generate has multiplicative error, so you should specify Weights->(1/#&) in NonlinearModelFit. Second, it's not clear what you want or how the standard errors of the parameter estimates relate to it.