# predict.loess

##### Predict Loess Curve or Surface

Predictions from a `loess`

fit, optionally with standard errors.

- Keywords
- smooth

##### Usage

```
# S3 method for 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.

##### Value

If `se = FALSE`

, a vector giving the prediction for each row of
`newdata`

(or the original data). If `se = TRUE`

, a list
containing components

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.

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)`

```
# NOT RUN {
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 package stats, version 3.5.0, License: Part of R 3.5.0*