predict.loess {stats} | R Documentation |

## Predict Loess Curve or Surface

### Description

Predictions from a `loess`

fit, optionally with standard errors.

### Usage

## S3 method for class 'loess':
predict(object, newdata = NULL, se = FALSE, ...)

### Arguments

`object` |
an object fitted by `loess` . |

`newdata` |
an optional data frame in which to look for variables with
which to predict. If missing, the original data points are used. |

`se` |
should standard errors be computed? |

`...` |
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

`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.

### 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). As from **R** 2.0.0 a warning will be given if the
variables found are not of the same length as those in `newdata`

if it was supplied.

### Author(s)

B.D. Ripley, based on the `cloess`

package of Cleveland,
Grosse and Shyu.

### See Also

`loess`

### 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)

[Package

*stats* version 2.2.1

Index]