Smoothing Data Using an Adaptive Method

supsmooth(vx, vy) Returns a vector created by the piecewise use of a symmetric nearest neighbor linear least-squares fitting on each element in vy, in which the number of nearest neighbors is adaptively chosen.

Arguments:

• vx is a vector of real numbers with elements in ascending order.

• vy is a vector of real numbers the same length as vx.

Notes:

• This function is most useful when your data lies along a band of relatively constant width.
• Bandwidth is usually set to a few times the spacing between data points on the x-axis, depending on the desired degree of smoothing.
• Some types of data are better smoothed with one type of algorithm over another. You may wish to compare this method with Median smoothing or Gaussian Kernel smoothing. The loess polynomial regression technique is also an effective smoother.

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