Plot Confidence Interval of 95%
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Hi, I used the optimoptions to find the fitted curve of my result points, and now I'm trying to plot the points, the fitted curve and the confidence interval.
These are the points:
T0 = [-49;-45;-19;-20;30;30;100;98;238;239;350;349];
Y = [0;0;0;0;12;8;48;44;46;34;34;40];
And this is the code to find the fitted curve:
lb = [];
ub = [];
% Starting point
x0 = [10;10;10;10];
F = @(x) (x(1) + x(2)*tanh((x(3) - T0)/x(4)) );
Fobj = @(x,T0) F(x);
options = optimoptions('lsqcurvefit','Algorithm','levenberg-marquardt');
x = lsqcurvefit(Fobj,x0,T0,Y,lb,ub,options);
So how can I plot the points, the fitted curve and the confidence interval together?
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Answers (1)
Star Strider
on 30 May 2021
The nlpredci funciton will work here, however in the presence of a constrained optimisation, no confidence limits may be reliable.
T0 = [-49;-45;-19;-20;30;30;100;98;238;239;350;349];
Y = [0;0;0;0;12;8;48;44;46;34;34;40];
% And this is the code to find the fitted curve:
lb = [];
ub = [];
% Starting point
x0 = [10;10;10;10];
F = @(x) (x(1) + x(2)*tanh((x(3) - T0)/x(4)) );
Fobj = @(x,T0) F(x);
options = optimoptions('lsqcurvefit','Algorithm','levenberg-marquardt');
[x,resnorm,residual,exitflag,output,lambda,jacobian] = lsqcurvefit(Fobj,x0,T0,Y,lb,ub,options);
[Ypred,delta] = nlpredci(Fobj,T0,x,residual,'Jacobian',jacobian);
figure
plot(T0, Y,'p')
hold on
plot(T0, Ypred,'-r', T0,delta*[-1 1]+Ypred, '--r')
hold off
grid
legend('Data', 'Fitted Regression', '95% Confidence Limits', 'Location','best')
.
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