How to do 2D surface fitting regression ?
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I have data x, y are 2 independant variables , x is vector data 1x200, y is vector data 1x5, z is dependant variable which is matrix data dim 5x200. I am trying to fit polynomail surface to the given data in the form Z = p00 + p10*x + p01*y + p20*x.^2 + p11*x*y + p30*x.^3 + p21*x.^2*y since the variables dimension don't match , I got error when using the least error method to estimate the coffiecients P P=[1 x y x.^2 x*y x.^3 x.^2*y]\Z how to solve that? and represent it in matrix forms?
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Ahmet Cecen
am 27 Mai 2016
There is something wrong with the way you are setting up the problem. You cannot have independent variables with different dimensions for a regression problem. If x has 200 observations, y also needs to have 200 observations (not necessarily unique).
Image Analyst
am 27 Mai 2016
He'd need to use meshgrid
[allX, allY] = meshgrid(x, y);
and then make sure the Z are associated with the correct coordinate. Or better yet, just start with a 5x200 image of Z. Then he can use polyfitn() like in my answer below.
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Image Analyst
am 26 Mai 2016
I've attached a demo where I use it to do background correction.
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