Predictions from a
loess fit, optionally with standard errors.
## S3 method for class 'loess': predict((object, newdata = NULL, se = FALSE, na.action = na.pass, ...))
- an object fitted by
- 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.
- should standard errors be computed?
- function determining what should be done with missing values in data frame
newdata. The default is to predict
- 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.
se = FALSE, a vector giving the prediction for each row of
newdata (or the original data). If
se = TRUE, a list containing components 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
loess does not support extrapolation.
- the predicted values.
- an estimated standard error for each predicted value.
- the estimated scale of the residuals used in computing the standard errors.
- an estimate of the effective degrees of freedom used in estimating the residual scale, intended for use with t-based confidence intervals.
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.
Documentation reproduced from R 3.0.1. License: GPL-2.