Download MATLAB CURVE FITTING TOOLBOX - RELEASE NOTES User`s guide

smooth
are given below. For the Savitzky-Golay method, the default polynomial degree
is 2.
Method
Description
moving
Moving average filter.
lowess
Locally weighted scatter plot smooth using least squares
linear polynomial fitting.
loess
Locally weighted scatter plot smooth using least squares
quadratic polynomial fitting.
sgolay
Savitzky-Golay filter. Note that the algorithm used by the
toolbox can accept nonuniform predictor data.
rlowess
Lowess smoothing that is resistant to outliers.
rloess
Loess smoothing that is resistant to outliers.
yy = smooth(ydata,span,'method') smooths data using the specified span
and method. For the loess and lowess methods, you can specify span as a
percentage of the total number of data points. In this case, span must be less
than or equal to 1. For the moving average and Savitzky-Golay methods, span
must be odd. If an even span is specified, it is reduced by 1.
yy = smooth(ydata,'sgolay',degree) uses the Savitzky-Golay method with
polynomial degree specified by degree.
yy = smooth(ydata,span,'sgolay',degree) uses the number of data points
specified by span in the Savitzky-Golay calculation. span must be odd and
degree must be less than span.
yy = smooth(xdata,ydata,...) smooths the data specified by ydata and the
associated predictor data, xdata. You should specify the predictor data when it
is not uniformly spaced or it is not sorted. If xdata is not uniform and you do
not specify method, lowess is used. If the smoothing method requires xdata to
be sorted, the sorting occurs automatically.
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