predict.loess             package:modreg             R Documentation

Predict Loess Curve or Surface

Description:

     Predictions from a `loess' fit, optionally with standard errors.

Usage:

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

Arguments:

  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?

     ...: arguments passed to or from other methods.

Details:

     The standard errors calculation is slower than prediction.

     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.

Value:

     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. 

Author(s):

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

See Also:

     `loess'

Examples:

     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)

