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3 Fitting Data

3-72

Example: Nonparametric Fit

This example fits the following data using a cubic spline interpolant and

several smoothing splines.

rand('state',0);

x = (4*pi)*[0 1 rand(1,25)];

y = sin(x) + .2*(rand(size(x))-.5);

As shown below, you can fit the data with a cubic spline by selecting

Interpolant from the Type of fit list.

The results shown below indicate that goodness of fit statistics are not defined

for interpolants.

As described in “Interpolants” on page 3-68, cubic spline interpolation is

defined as a piecewise polynomial that results in a structure of coefficients. The

number of “pieces” in the structure is one less than the number of fitted data

points, and the number of coefficients for each piece is four because the

polynomial degree is three. The toolbox does not allow you to access the

structure of coefficients.

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