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Topic: lsqcurvefit with matrix "xdata"
Replies: 7   Last Post: May 11, 2013 9:58 AM

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Alan Weiss

Posts: 1,262
Registered: 11/27/08
Re: lsqcurvefit with matrix "xdata"
Posted: May 10, 2013 2:20 PM
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On 5/10/2013 11:29 AM, Jonathan wrote:
> Alan_Weiss <aweiss@mathworks.com> wrote in message
> <kminu8$35e$1@newscl01ah.mathworks.com>...

>> On 5/9/2013 3:54 PM, Jonathan wrote:
>> > I am trying to use lsqcurvefit to find a single parameter estimate
>> > with two x values per predicted y, but the result is always

>> "Function > value and YDATA sizes are incommensurate." Previously,
>> when using a > similar function with only one value for x data per
>> predicted y the > lsqcurvefit worked fine. The function that I am
>> using returns the > correct results, a single y value, when I supply
>> a parameter. I have > tried tinkering with the function thinking I
>> messed it up some how, > but to no avail.

>> >
>> > function = @(k,x) (100./(1+k.*x(1))) + (100./(1+k.*x(2)))
>> > x0 = .01
>> > ydata = <36,1> Double
>> > xdata = <36,2> Double
>> >
>> > I am trying to determine if the summation of two hyperbolas is

>> enough > to explain the ydata or if I need to consider another model.
>> >
>> > The documentation and help that I can find suggests that using a

>> two > variable function for lsqcurvefit is possible, but for the life
>> of me > I can not figure it out (where I screwed up). Thanks for any
>> help.

>> >
>> > Jon

>>
>> Did you try to run the documentation example
>> http://www.mathworks.com/help/optim/ug/nonlinear-curve-fitting-with-lsqcurvefit.html
>>
>> Does that example run for you?
>>
>> Alan Weiss
>> MATLAB mathematical toolbox documentation

>
> I have used the documentation, and it was instrumental to me figuring
> out how to use lsqcurvefit in the first place. The documentation does
> not address the specific issue I am having.
>
> In the example there is one predicted y per x value, and three
> parameters to fit. I have one predicted y per 2 x values, and only one
> parameter.
> I have not had trouble previously finding the parameters in situations
> like the one in the documentation.


I just ran a little experiment and found no problem fitting matrix data.
See if the following is anything like your case.

rng(5489,'twister') % reproducible
xdata = -2*log(rand(100,1));
ydata = (ones(100,1) + .1*randn(100,1)) + (3*ones(100,1)+...
0.5*randn(100,1)).*exp((-(2*ones(100,1)+...
.5*randn(100,1))).*xdata);
ydata2 = (.2*ones(100,1) + .1*randn(100,1)) + (2*ones(100,1)+...
0.5*randn(100,1)).*exp((-(0.5*ones(100,1)+...
.15*randn(100,1))).*xdata); % the second component of y
ydata = [ydata,ydata2]; % second column depends on a4, a5, a6

predicted = @(a,xdata) [a(1)*ones(100,1)+a(2)*exp(-a(3)*xdata),...
a(4)*ones(100,1)+a(5)*exp(-a(6)*xdata)];

a0 = [2;2;2;2;2;2];

[ahat,resnorm,residual,exitflag,output,lambda,jacobian] =...
lsqcurvefit(predicted,a0,xdata,ydata);

% My result:
ahat =

1.0169
3.1444
2.1596
0.2284
2.0523
0.5077

This is a good fit.

Alan Weiss
MATLAB mathematical toolbox documentation



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