predict.loess
Predict Loess Curve or Surface
Predictions from a loess
fit, optionally with standard errors.
 Keywords
 smooth
Usage
"predict"(object, newdata = NULL, se = FALSE, na.action = na.pass, ...)
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?
 na.action
 function determining what should be done with missing
values in data frame
newdata
. The default is to predictNA
.  ...
 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 axisaligned hypercube enclosing the original data will have
missing (NA
) predictions and standard errors.
Value

If
 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 tbased confidence intervals. If
se = FALSE
, a vector giving the prediction for each row of
newdata
(or the original data). If se = TRUE
, a list
containing components
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.Predictions from infinite inputs will be NA
since loess
does not support extrapolation.
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.
See Also
Examples
library(stats)
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)
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