| predict.loess {stats} | R Documentation |
Predictions from a loess fit, optionally with standard errors.
## S3 method for class 'loess': predict(object, newdata = NULL, se = FALSE, ...)
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. |
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.
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.
B.D. Ripley, based on the cloess package of Cleveland,
Grosse and Shyu.
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)