# predict.loess {stats}

### Description

Predictions from a `loess`

fit, optionally with standard errors.

### Usage

## S3 method for class 'loess': 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 predict`NA`

. - ...
- 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.

### Values

If `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 `NA`

since `loess`

does not support extrapolation.

- 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.

### 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

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)

Documentation reproduced from R 3.0.2. License: GPL-2.