%get the residual
d_res=d_data-d_model;

%plot the data, fitted values, and residual in the same plot
% figure(1);
% clf;
% subplot(3, 1, 1);
% plot(time,d_data);
% axis tight;
% ylabel('Data', 'FontSize', 18);
% title(['Two Parameters Estimation C=' num2str(C) ', K=' num2str(K)]);
% 
% subplot(3, 1, 2);
% plot(time,d_model, 'r');
% axis tight;
% ylabel('Model', 'FontSize', 18);
% 
% subplot(3, 1, 3);
% plot(d_res, 'g');
% axis tight;
% ylabel('Residual', 'FontSize', 18);

% %plot them in the same plot
% figure(2);
% clf;
% plot(time,d_data);
% hold on;
% plot(d_model, 'r--');
% plot(d_res, 'g:');
% hold off;
% legend('experimental data','model displacement', 'Residual');
% xlabel('Time','FontSize', 18);
% ylabel('Displacement');
% axis tight;

%just plot the residual vs time
%this is to check the dependence structure

figure(3);
clf;
plot(time,d_res, 'g.');
%axis tight;
axis([0.0 0.5 -0.00006 0.00006]);
ylabel('Residual', 'FontSize', 18);
xlabel('Time', 'FontSize', 18);
grid on;

%checking the residuals have the same variance or not
%plot the residual vs. the fitted values

figure(4);
clf;
plot(d_model, d_res, 'o');
axis tight;
xlabel('Fitted Value', 'FontSize', 18);
ylabel('Residual', 'FontSize', 18);

%check whether the residuals are truly from Normal distribution
%1. quantile-quantile plot

figure(5);
clf;
qqplot(d_res);

%2. normality-probablity plot

% figure(6);
% clf;
% normplot(d_res);