| predict.loess {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 |
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. |
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.
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)