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Re: Step-function fit using leasqr

From: Doug Stewart
Subject: Re: Step-function fit using leasqr
Date: Fri, 2 Sep 2016 13:43:29 -0400

On Fri, Sep 2, 2016 at 3:18 AM, Tweety <address@hidden> wrote:

Am 01.09.2016 um 22:03 schrieb Doug Stewart:

On Thu, Sep 1, 2016 at 3:21 PM, Tweety <address@hidden> wrote:
Dear all,

I am trying to fit measured data using a step function, please see the attachments. I get an error "weighted residuals are not real". Ok, additionally to a conceptual glitch I might have bad initial values but I thought Marquardt Levenberg was supposed to converge even in that case. What am I missing?

I appreciate any suggestions. I might be on the wrong track with the model function and the initial values, so if there are any better approaches please let me know.

Why do I want to fit this data? Firstly, I need some kind of look-up for various x-values which might not coincide with measured x-values. Secondly, I need to get rid of the scatter in the end of the measurement for further processing. Is there perhaps a simpler way to achieve this?

Code for reference w/o attachment:

E = dlmread('data.txt');
x = E(:,1);
y = E(:,2);
q = [1; .25; .25];
function res = f(x,q)
  res = q(1) - exp((q(2).*x).^q(3));
[fr,q,cvg,iter_r] = leasqr(x,y,q,'f',.0001,40);

Any help would be appreciated. Thanks and kind regards, Jan

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The data file that you attached is faulty.
try plotting just the x values



That is right, sorry. Please find attached the right files.
Thanks and kind regards,

You can overcome the error by doing
 res = q(1) - exp((abs(q(2)).*x).^q(3));
but the real problem is that the formula that you are trying to fit to the data does not model the long startup time. 
I don't think you will fit that formula to that data.


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