MATLAB: Uncertainty and curve fitting

uncertainty

Hi,

Please I need your help. I'm working on curve fitting, I'm using lsqcurvefit function to do it!! I'm trying to estimate uncertainty of the coefficient A and B of the function fitted to my observation ponits (y=A.x^B)!! Please could you help me!! Thank you in advance

Mira

Best Answer

If you need confidence intervals nlinfit is a better option

Related Solutions

MATLAB: Estimate confidence intervals after regress!

The ‘BINT’ matrix contains the parameter confidence intervals.

If you want both parameter and prediction confidence intervals, use fitlm:

x = (1:10)';                                        % Create Data

y = 1.5 + 2.3*x + 1.5*randn(size(x));               % Create Data
mdl = fitlm(x, y);                                  % Fit Data
B = mdl.Coefficients.Estimate;                      % Coefficients
CI = coefCI(mdl);                                   % Coefficient Confidence Intervals
[Ypred,YCI] = predict(mdl, x);                      % Fitted Regression Line & Confidence Intervals
figure(1)
plot(x, y, 'pg')
hold on
plot(x, Ypred,'-r',    x, YCI, '--r')
hold off
grid

MATLAB: Parametric bootstraping in curve fitting

In general when I hear the expression parametric bootstrap I think "parametric residual bootstrap"

I'm attaching some simple code that contrasts and parametric and a non parametric residual bootstrap.

%%Bootstrap Examples
% Generate some data
x = 1:10000;
X = x';
Y = 3*X + 5  + randn(10000,1);
% Run a regression
b = regress(Y, [ones(size(X)), X])
%%Parametric residual bootstrap
% Appropriate if you have reason to believe that your residuals are

% normally distributed
YHat = [ones(size(X)), X] * b;
resid = Y - YHat;
foo = fitdist(resid, 'normal');
se = std(bootstrp(1000,@(bootr)regress(YHat+random(foo, size(bootr)),[ones(size(X)), X]),resid))
%%Nonparametric residual bootstrap
% Appropriate if you have reason to believe that your residuals are
% homoskedastic
YHat = [ones(size(X)), X] * b;
resid = Y - YHat;
se = std(bootstrp(1000,@(bootr)regress(YHat+bootr,[ones(size(X)), X]),resid))

Best Answer

Related Solutions

MATLAB: Estimate confidence intervals after regress!

MATLAB: Parametric bootstraping in curve fitting

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