Re: NonlinearModelFit and assumptions on fit parameters
- To: mathgroup at smc.vnet.net
- Subject: [mg128208] Re: NonlinearModelFit and assumptions on fit parameters
- From: Bill Rowe <readnews at sbcglobal.net>
- Date: Mon, 24 Sep 2012 00:33:29 -0400 (EDT)
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- Delivered-to: l-mathgroup@wolfram.com
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On 9/23/12 at 3:01 AM, niels.martinsen at gmail.com (Niles) wrote:
>I have a set of data (x, y) that I can succesfully fit a nonlinear
>function to using NonlinearModelFit:
>data = {{1, 1}, {2, 2}, {3, 3.2}}; fitFuncExactNoLosses[a_, b_,
>x_]:= a*x^2 + b + x; nlm =
>NonlinearModelFit[data,fitFuncExactNoLosses[a, b, x],
>{ {a, 1}, {b, 1}}, x]
>However, the paramter "b" comes out negative and it *must* be
>positive. Is there a way to utilize assumptions such that b is
>constrained to be grater than zero?
Look up NonlinearModelFit in the Documentation Center and you
will find the second argument can be given in the form {model,
constrainte}. So, all you need do is
nlm = NonlinearModelFit[
data, {fitFuncExactNoLosses[a, b, x], b > 0}, {{a, 1}, {b,
1}}, x]