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Niels
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levenberg-marquardt in lsqcurvefit

Asked by Niels
on 15 Aug 2013
Hello
How do I use the levenberg-marquandt algortime in lsqcurve fit instead of the default trust-region-reflective algorithm?
[parameters,resnorm,residual,exitflag,output] = lsqcurvefit(heightModel,initialValues,filteredData(:,1),filteredData(:,2));
is the formula I use in my function
Can anyone help me out?
Thanks in advance
Niels

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2 Answers

Answer by Matt Kindig on 15 Aug 2013
 Accepted Answer

Pass in an options structure:
opts = optimset('Algorithm', 'levenberg-marquardt');
[parameters,resnorm,residual,exitflag,output] = lsqcurvefit(heightModel,initialValues,filteredData(:,1),filteredData(:,2),[],[],opts);
You can see the various options in the Help docs for lsqcurvefit, at
doc lsqcurvefit

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Answer by Niels
on 15 Aug 2013

Thanks for answering
I have used this for individual fitting in the following construct:
CODE PART A
validFitPersons = true(nbValidPersons,1);
for i=1:nbValidPersons
personalData = data{validPersons(i),3};
personalData = personalData(personalData(:,1)>=minAge,:);
% Fit a specific model for all valid persons
try
opts = optimset('Algorithm', 'levenberg-marquardt');
[personalParams,personalRes,personalResidual] = lsqcurvefit(heightModel,initialValues,personalData(:,1),personalData(:,2),[],[],opts);
%catch
%x=1;
end
The intial starting values and the formula used to fit is given by:
CODE PART B
strcmpi(model,'jpa2')
% y = a.*(1-1/(1+(b_1(t+e))^c_1+(b_2(t+e))^c_2+(b_3(t+e))^c_3))
heightModel = @(params,ages) abs(params(1).*(1-1./(1+(params(2).* (ages+params(8) )).^params(5) +(params(3).* (ages+params(8) )).^params(6) +(params(4) .*(ages+params(8) )).^params(7) )));
modelStrings = {'a','b1','b2','b3','c1','c2','c3','e'};
% Define initial values
if strcmpi('male',gender)
initialValues = [175 0.35 0.12 0.076 0.43 2.8 20 0.34];
else
initialValues = [162 0.43 0.13 0.089 0.48 2.9 16 0.4];
end
Switching off the catch command in CODE PART A and adding the levenberg-marquandt command also in CODE PART A caused my data to be much closer towards the initial values then otherwise. This is a good thing.
How can I make my curve fitting stay looking much closer around my initial values and not spreading out too much?
Anyhelp?

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