predict.loess {modreg}R Documentation

Predict Loess Curve or Surface


Predictions from a loess fit, optionally with standard errors.


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


object an object fitted by loess.
newdata an optional data frame specifying points at which to do the predictions. If missing, the original data points are used.
se should standard errors be computed?
... arguments passed to or from other methods.


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.


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.


B.D. Ripley, based on the cloess package of Cleveland, Grosse and Shyu.

See Also



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,
predict(cars.lo2, data.frame(speed=seq(5, 30, 1)), se=TRUE)

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