# predict.loess

0th

Percentile

##### 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

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.

##### 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.

loess
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) # }