I don’t recognise this particular Hill equation (there are several), but that aside, estimating your parameters and plotting them is relatively straightforward:
Agonist = [0.1 0.5 1 5 10 19 50 100 114 500 1000 2000];
atRA = [0 0 7 15 30 50 58 80 83 87 90 90];
% MAPPING: Emax = b(1), EC50 = b(2)
hill_fit = @(b,x) b(1).*x./(b(2)+x);
b0 = [90; 19]; % Initial Parameter Estimates
B = lsqcurvefit(hill_fit, b0, Agonist, atRA);
AgVct = linspace(min(Agonist), max(Agonist)); % Plot Finer Resolution
figure(1)
plot(Agonist, atRA, 'bp')
hold on
plot(AgVct, hill_fit(B,AgVct), '-r')
hold off
grid
xlabel('Agonist')
ylabel('atRA')
legend('Data', 'Hill Equation Fit', 'Location','SE')
If you’re doing curve-fitting, it’s easiest to use the lsqcurvefit function. It uses lsqnonlin but is specifically designed to make curve-fitting straightforward.
