Assuming that ‘y’ exists somewhere in your workspace, and since for MATLAB Statistics and Machine Learning Toolbox and Optimization Toolbox curve-fitting require two arguments, the first being the parameters and the second being the independent data, you would have to use this synatx:
@(x,X)myfun(x,y,X)
so your correct nlinfit call would be:
x = nlinfit(X,y,@(x,X)myfun(x,y,X),xstart)
although I cannot determine the reason you are using your dependent variable as an argument to your objective function. Since nlinfit will use it to fit your function, it is not necessary for you to use it specifically in your function. The best options is likely:
function F = myfun(x,X)
F=((1 + x(1)*(x(2)/0.0025662))*X(:,1) - (x(2)/x(3))*(sqrt(1-x(1)^2))*X(:,2) ...
- x( 4)*X(:, 3) - x(5)*X(:, 4)) - x( 6)*X(:, 5) - x( 7)*X(:, 6) - x(8)*X(:,7) ...
- x( 9)*X(:, 8) - x(10)*X(:, 9) - x(11)*X(:,10) - x(12)*X(:,11) ...
- x(13)*X(:,12) - x(14)*X(:,13) - x(15)*X(:,14);
end
and then:
x = nlinfit(X,y,@myfun,xstart)
