Predict Loess Curve or Surface
Usage
predict.loess(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?
|
Description
Predictions from a loess fit, optionally with standard errors.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
loessExamples
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)