

   PPrreeddiicctt LLooeessss CCuurrvvee oorr SSuurrffaaccee

        predict.loess(object, newdata = NULL, se = FALSE)

   AArrgguummeennttss::

     object: an object fitted by `loess'.

    newdata: an optional data frame specifying points at which
             to do the predictions. If missing, the original
             data points are used.

         se: should standard errors be computed?

   DDeessccrriippttiioonn::

        Predictions from a `loess' fit, optionally with stan-
        dard errors.

   DDeettaaiillss::

        The standard errors calculation is slower than predic-
        tion.

        When the fit was made using `surface="interpolate"'
        (the default), `predict.loess' will not extrapolate< so
        points outside an axis-aligned hypercube enclosing the
        original data will have missing (`NA') predictions and
        standard errors.

   VVaalluuee::

        If `se = FALSE', a vector giving the prediction for
        each row of `newdata' (or the original data). If `se =
        TRUE', a list containing components

        fit: the predicted values.

         se: an estimated standard error for each predicted
             value.

   residual.scale: the estimated scale of the residuals used in
             computing the standard errors.

         df: an estimate of the effective degrees of freedom
             used in estimating the residual scale, intended
             for use with t-based confidence intervals.

   AAuutthhoorr((ss))::

        B.D. Ripley, based on the `cloess' package of Cleve-
        land, Grosse and Shyu.

   SSeeee AAllssoo::

        `loess'

   EExxaammpplleess::

        data(cars)
        cars.lo <- loess(dist ~ speed, cars)
        predict(cars.lo, data.frame(speed=seq(5, 30, 1)), se=TRUE)
        # to get extrapolation
        cars.lo2 <- loess(dist ~ speed, cars,
          control=loess.control(surface="direct"))
        predict(cars.lo2, data.frame(speed=seq(5, 30, 1)), se=TRUE)

