Comparing multiple curves to an optimal curve
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I am collecting twice a total of 240 Trials of 4 secs each. Subjects are required to apply a certain amount of force (depending on the day's max force) at the time point 3secs with their thumb. This results in a 240 different curves per day. I manage to compute the graphs for the 240 trials. My Question now is: How can I fit an additional curve to the 240 curves which depicts the perfect curve, usinf on only a handfull of data-points based on the day's max force(the program I am using currently collects 4000 data points during the 4 secs, which is way to much to write down by hand)? Furthermore how do I calculate the average difference between each trial and the optimal curve? So far I am only using plot() to generate the graphs.
Thank you very much
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Shashank Prasanna
on 31 Jan 2013
Depends on how you define your "perfect curve" there should be some criterion. Do you want to average it? If you want to fit the average curve, then depending on the curve choose an equation or model that best describes your curve and fit it using curve fitting or optimization.
Once you have your "optimal curve" you can always find a sum or squares error or MSE
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Shashank Prasanna
on 8 Feb 2013
Are you sure? that data looks nothing like the curve in your picture.
In anycase you can fit the curve using curve fitting toolbox, and the way to check is to try the following command in MATLAB.
>> cftool
It is intuitive enough that you can get to work immediately. If you don't then you will have to set up an optimization problem to do the same. You can start here:
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