3 parameters model fitting to experimental data

Dear all

I collected some experimental data which gave me two vector: hits and false alarm. What I need to do now is to use matlab to fit a specific model (see the model details below) to this data and see which are the model parameters which minimise the SSE. I tried with different approach (different matlab solver) but I'm really stuck.

Here the function which contains my model:

function modelYData=modelFun(R,d,C,x)

fOld=normcdf(d/2-C);
fNew=normcdf(-d/2-C);
modelHits=R+(1-R)*fOld;
modelFa=fNew;
[pModel,SModel]=polyfit(modelFa,modelHits,2);
modelYData=polyval(pModel,x);

end

Here the explanation of the parameters:

• R (single integer)
• d (single integer)
• C (vector of five elements in the from of 5 equally spaced integer with distance of 0.5)

Basically I can create a for loop with the most plausible ranges for each parameter, but i'd like to find a more elegant solution with any of the matlab solver.I really appreciate if someone can give me an help because, as I said, I'm really stuck.

Thanks

Best, Andrea