Fit model with 3 independent variables and many parameters to data?

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Justin Solomon
Justin Solomon 2012-10-26
回答： Sander van Otterdijk 2019-4-18
Is it possible to use the fit() function to fit a model with 3 independent variables and many parameters (coefficients)? Reading through the documentation, I get the impression that Matlab only supports 2 independent variables. Any insight would be helpful.
Thanks, Justin

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Sean de Wolski
Sean de Wolski 2012-10-26
Do you have the Statistics or Optimization Toolboxes? If so:
Optim:
* doc lsqcurvefit
* doc lsqlin
* doc lsqnonlin
Stats:
* doc NonlinearModel
* doc LinearModel
* doc regress
I'm missing many others, we can point you in a more specific direction if you have more details.
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Justin Solomon
Justin Solomon 2012-10-26
编辑：Justin Solomon 2012-10-26
Sorry, my code is a little bit messy right now (its written in a GUI and it would take me a while to put it in an understandable format).
I've implemented the lscurvefit() routine. It works in a reasonable amount of time if I give it a good starting guess and limits.
Anyways, is there a way to weight input data points (ydata)? In other words, some of my data points are less important than others so I would like to minimize a 'weighted' sum of square errors instead of the normal sum of square errors used by default. Possible? Thanks again for your help.
Justin Solomon
Justin Solomon 2012-10-26
I just saw that I can use lsqnonlin() instead of lsqcurvefit() to do what I need because in lsqnonlin() the function that your minimizing is supposed to return the residuals instead of the predicted values as in lsqcurvefit(). Thus I can just weight the residuals that are returned in my function definition.

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Sander van Otterdijk
hoi
dit is een code
do
if code = dit
do: zijn.
end

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