How to obtain the standard error for each of the fitted parameters
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I am using the curvit toolbox of Matlab and I was wondering how can I get the standard error for each of the fitted parameters:a, b, c, and z. Let's say the x and y data points are:
x = [10, 20, 30, 40]
y = [0.1, 0.02, 0.01, 0.001]
you can visuzlize the data points by the code below
plot(x,y,'*')
The x, y data should fit to function below
function [fitresult, gof] = createFit(x, y)
[xData, yData] = prepareCurveData( x, y );
% Set up fittype and options.
ft = fittype( 'a*exp(b/(x-c)^z)', 'independent', 'x', 'dependent', 'y' );
opts = fitoptions( 'Method', 'NonlinearLeastSquares' );
opts.Display = 'Off';
opts.Lower = [0 0 0 0];
opts.StartPoint = [0.001 0.952083907850712 0.7812 2];
opts.Upper = [1 Inf Inf 3];
% Fit model to data.
[fitresult, gof] = fit( xData, yData, ft, opts );
% Plot fit with data.
figure( 'Name', 'untitled fit 1' );
h = plot( fitresult, xData, yData );
legend( h, 'y vs. x', 'untitled fit 1', 'Location', 'NorthEast', 'Interpreter', 'none' );
% Label axes
xlabel( 'x', 'Interpreter', 'none' );
ylabel( 'y', 'Interpreter', 'none' );
grid on
end
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답변 (2개)
Matt J
2021년 2월 12일
편집: Matt J
2021년 2월 12일
If you're prepared to assume the parameter estimates have Gaussian errors, perhaps you can find the 95% confidence interval width using confint and divide that result by 3.92.
댓글 수: 3
Matt J
2021년 2월 13일
The problem is that you have 4 unknown variables and only only 4 data points to estimate them with.
Jeff Miller
2021년 2월 13일
The notion of a standard error assumes some kind of random sampling. For example, the standard error of your 'b' parameter reflects the variation in b estimates that would be found across from many random samples like the one you have. It isn't really clear from your dataset what is varying randomly, or by how much it varies. You would need that kind of info to get standard error estimates.
댓글 수: 3
Jeff Miller
2021년 2월 13일
Yes, in principle. Generate (say) 1000 random samples. Estimate b for each sample. Compute the standard deviation of the 1000 estimated b values. That standard deviation is your estimated "standard error of b". Similarly you get estimates of the other parameters at the same time, and you can look at the correlation (across 1000 samples) between parameter estimates to get a feel for the tradeoffs between parameters. Of course, 10,000 or more is better if you are patient.
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