Curve fit but only for the values that exists with defined values of x.

Question

Yashvardhan Bhati 2022-10-19

0
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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1830568-curve-fit-but-only-for-the-values-that-exists-with-defined-values-of-x

评论： Yashvardhan Bhati 2022-10-20

I wnat to fit the curve for the point but i only want the curve to fit from value suppose in given code x vary from 1800-1950 and shouldn't extend beyond it. Is there any way to do this?

load census

plot(cdate,pop,'o')

%Create a fit options object and a fit type for the custom nonlinear model , where a and b are coefficients and n is a problem-dependent parameter.

fo = fitoptions('Method','NonlinearLeastSquares',...

'Lower',[0,0],...

'Upper',[Inf,max(cdate)],...

'StartPoint',[1 1]);

ft = fittype('a*(x-b)^n','problem','n','options',fo);

%Fit the data using the fit options and a value of n = 2.

[curve2,gof2] = fit(cdate,pop,ft,'problem',2)

curve2 =

General model: curve2(x) = a*(x-b)^n Coefficients (with 95% confidence bounds): a = 0.006092 (0.005743, 0.006441) b = 1789 (1784, 1793) Problem parameters: n = 2

gof2 = struct with fields:

sse: 246.1543 rsquare: 0.9980 dfe: 19 adjrsquare: 0.9979 rmse: 3.5994

%Fit the data using the fit options and a value of n = 3.

[curve3,gof3] = fit(cdate,pop,ft,'problem',3)

curve3 =

General model: curve3(x) = a*(x-b)^n Coefficients (with 95% confidence bounds): a = 1.359e-05 (1.245e-05, 1.474e-05) b = 1725 (1718, 1731) Problem parameters: n = 3

gof3 = struct with fields:

sse: 232.0058 rsquare: 0.9981 dfe: 19 adjrsquare: 0.9980 rmse: 3.4944

%Plot the fit results with the data.

hold on

plot(curve2,'m')

plot(curve3,'c')

legend('Data','n=2','n=3')

hold off

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Answer 1

Torsten 2022-10-19

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链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1830568-curve-fit-but-only-for-the-values-that-exists-with-defined-values-of-x#answer_1078793

编辑：Torsten 2022-10-19

在 MATLAB Online 中打开

load census

I = cdate >= 1850 & cdate <= 1950;

cdate1 = cdate(I);

pop1 = pop(I);

plot(cdate,pop,'o')

%Create a fit options object and a fit type for the custom nonlinear model , where a and b are coefficients and n is a problem-dependent parameter.

fo = fitoptions('Method','NonlinearLeastSquares',...

'Lower',[0,0],...

'Upper',[Inf,max(cdate)],...

'StartPoint',[1 1]);

ft = fittype('a*(x-b)^n','problem','n','options',fo);

%Fit the data using the fit options and a value of n = 2.

[curve2,gof2] = fit(cdate1,pop1,ft,'problem',2)

curve2 =

General model: curve2(x) = a*(x-b)^n Coefficients (with 95% confidence bounds): a = 0.005544 (0.004984, 0.006103) b = 1784 (1777, 1790) Problem parameters: n = 2

gof2 = struct with fields:

sse: 70.1612 rsquare: 0.9963 dfe: 9 adjrsquare: 0.9959 rmse: 2.7921

%Fit the data using the fit options and a value of n = 3.

[curve3,gof3] = fit(cdate1,pop1,ft,'problem',3)

curve3 =

General model: curve3(x) = a*(x-b)^n Coefficients (with 95% confidence bounds): a = 1.326e-05 (1.023e-05, 1.63e-05) b = 1723 (1708, 1738) Problem parameters: n = 3

gof3 = struct with fields:

sse: 151.9055 rsquare: 0.9919 dfe: 9 adjrsquare: 0.9910 rmse: 4.1083

%Plot the fit results with the data.

hold on

plot(cdate1,curve2(cdate1),'m')

plot(cdate1,curve3(cdate1),'c')

legend('Data','n=2','n=3')

hold off