I doubt that's possible with polyfit. If you are only using a first order regression model and you have access to one of the nonlinear curve fitting routines ( nlinfit in the Statistics Toolbox or lsqcurvefit in the Optimization Toolbox), the easiest way to do this is simply to use them with an objective function similar to:
Intercept = 5;
RegModel = @(B,X) B(1).*X + Intercept;
then:
beta0 = rand;
[Beta,R,J,CovB,MSE] = nlinfit(X, Y, RegModel, beta0);
You can use whatever sort of regression model you want, of course. In this one I used a simple single-parameter model.
You can specify whatever you want in a model function, and can fit as many parameters as you want (that are mathematically appropriate).
