

   KKeerrnneell RReeggrreessssiioonn SSmmooootthheerr

        ksmooth(x, y, kernel=c("box", "normal"), bandwidth=0.5,
                range.x=range(x), n.points=max(100, length(x)), x.points)

   AArrgguummeennttss::

          x: input x values

          y: input y values

     kernel: The kernel to be used.

   bandwidth: the bandwidth. The kernels are scaled so that
             their quartiles (viewed as probability densities)
             are at `+/-0.25*bandwidth'.

    range.x: the range of points to be covered in the output.

   n.points: the number of points at which to evaluate the fit.

   x.points: points at which to evaluate the smoothed fit. If
             missing, `n.points' are chosen uniformly to cover
             `range.x'.

   DDeessccrriippttiioonn::

        The Nadaraya-Watson kernel regression estimate.

   VVaalluuee::

        A list with components

          x: values at which the smoothed fit is evaluated.
             Guaranteed to be in increasing order.

          y: fitted values corresponding to `x'.

   NNoottee::

        This function is implemented purely for compatibility
        with S, although it is nowhere near as slow as the S
        function. Better kernel smoothers are available in
        other packages.

   AAuutthhoorr((ss))::

        B.D. Ripley

   EExxaammpplleess::

        data(cars)
        attach(cars)
        plot(speed, dist)
        lines(ksmooth(speed, dist, "normal", bandwidth=2), col=2)
        lines(ksmooth(speed, dist, "normal", bandwidth=5), col=3)
        lines(ksmooth(speed, dist, "normal", bandwidth=10), col=4)

