predict.loess             package:stats             R Documentation

Predict Loess Curve or Surface

Description:

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

Usage:

     ## S3 method for class 'loess':
     predict(object, newdata = NULL, se = FALSE, ...)

Arguments:

  object: an object fitted by 'loess'.

 newdata: an optional data frame in which to look for variables with
          which to predict, or a matrix or vector containing exactly
          the variables needs for prediction.  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. 

     If 'newdata' was the result of a call to 'expand.grid', the
     predictions (and s.e.'s if requested) will be an array of the
     appropriate dimensions.

Note:

     Variables are first looked for in 'newdata' and then searched for
     in the usual way (which will include the environment of the
     formula used in the fit).  A warning will be given if the
     variables found are not of the same length as those in 'newdata'
     if it was supplied.

Author(s):

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

See Also:

     'loess'

Examples:

     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)

