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 `se = TRUE`

is slower than
prediction, notably as it needs a relatively large workspace (memory),
notably matrices of dimension `N \times Nf`

where ```
f =
```

`span`

, i.e., `se = TRUE`

is `O(N^2)`

and hence stops when the sample size `N`

is larger than about 40'600
(for default `span = 0.75`

).

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)
```

[Package *stats* version 4.3.0 Index]