| 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,
na.action = na.pass, ...)
object |
an object fitted by |
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 |
... |
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.
Predictions from infinite inputs will be NA since loess
does not support extrapolation.
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.
B. D. Ripley, based on the cloess package of Cleveland,
Grosse and Shyu.
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)