Plot Confidence Interval of 95%

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Renan Fraga
Renan Fraga el 30 de Mayo de 2021
Comentada: Star Strider el 5 de Ag. de 2024
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?

Respuestas (1)

Star Strider
Star Strider el 30 de Mayo de 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);
Local minimum possible. lsqcurvefit stopped because the final change in the sum of squares relative to its initial value is less than the value of the function tolerance.
[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')
.
  4 comentarios
Khadija
Khadija el 5 de Ag. de 2024
How can I put the matrix of data in nlpredci?
Star Strider
Star Strider el 5 de Ag. de 2024
Use the procedures described in the documentation for nlpredci.

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