I have a set of data that I want to perform a fit to (data X and Y). I want to fit this data to a function Yfit = A / (1+(X/B)), where A and B are coefficients I want to find by minimising the sum of the squared residuals using fminsearch .
Here’s what I am trying at the moment: First, in a separate .m file I define the Yfit function:
function[res] = Yfit_func(A,B,X)
Yfit = A/(1+(X/B));
res = sum((Y-Yfit).^2);
end
As I understand, that defines a function called “Yfit_func”, with an output called “res”, and inputs “A”, “B” and “X”. “res” is the residuals term to be minimised to find A and B.
In the main .m file I call the fitting function using:
Coefficients = fminsearch(@Yfit_func, guess,[], Y, X);
My understanding of that line is that fminsearch calls the function "Yfit_func", uses the values contained in "guess" as starting points for finding the values of A and B that minimise "res" when fitting the function in "Yfit_func" to the data contained in X and Y.
The error I get is: “Error using / Matrix dimensions must agree. Error in Yfit_func (line 2) Yfit = A/(1+(X/B));”
Not sure what's going wrong here, any help would be appreciated. Thanks.
