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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | GAUSFIT GAUSFIT Gaussian Curve Fit
Usage
The gausfit routine has the following syntax
[mu,sigma,dc,gain,yhat] = gausfit(t,y,w,mug,sigmag,dcg,gaing).
where the required inputs are
- t - the values of the independant variable (e.g., time samples)
- y - the values of the dependant variable (e.g., f(t))
The following inputs are all optional, and default values are
available for each of them.
- w - the weights to use in the fitting (set to ones if omitted)
- mug - initial estimate of the mean
- sigmag - initial estimate of the sigma (standard deviation)
- dcg - initial estimate of the DC value
- gaing - initial estimate of the gain
The fit is of the form yhat=gain*exp((t-mu).^2/(2*sigma^2))+dc.
The outputs are
- mu - the mean of the fit
- sigma - the sigma of the fit
- dc - the dc term of the fit
- gain - the gain of the gaussian fit
- yhat - the output samples (the Gaussian fits)
Because the fit is nonlinear, a good initial guess is critical to
convergence of the solution. Thus, you can supply initial guesses
for each of the parameters using the mug, sigmag, dcg,
gaing arguments. Any arguments not supplied are estimated using
a simple algorithm. In particular, the DC value is estimated by
taking the minimum value from the vector y. The gain is
estimated from the range of y. The mean and standard deviation
are estimated using the first and second order moments of y.
This function uses fitfun.
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