Predict from a fitted LOWESS model at out-of-sample points

Usage

# S3 method for class 'Lowess'
predict(
    object,
    new_x,
    outputs = NULL,
    intervals = NULL,
    seed = NULL,
    extrapolation = "clamp",
    max_extrapolation_distance = NULL,
    max_neighbor_distance = NULL,
    ...
)

Arguments

object: A Lowess object, fitted (via fit) with retain_model = TRUE passed to Lowess.
new_x: Numeric vector of out-of-sample query points.
outputs: Character vector selecting optional prediction components: "se" and/or "derivative". NULL (default) returns only predicted values.
intervals: Interval options, created with intervals_opts (or a named list with any of confidence, prediction, bootstrap). Confidence and prediction coverage levels are independent; for example, intervals_opts(confidence = 0.90, prediction = 0.99). NULL (default) disables intervals.
seed: Non-negative whole-number seed for bootstrap resampling. NULL (default) uses a random seed.
extrapolation: Behavior for query points outside the training range: "clamp" (default), "linear", or "error".
max_extrapolation_distance: Under "linear" extrapolation, the maximum allowed distance beyond the training boundary before predict errors instead of returning an unbounded value. NULL (default) disables the cap.
max_neighbor_distance: Maximum allowed distance to the farthest point in a query's local window before predict errors, catching in-range-but-sparse query points. NULL (default) disables the cap.
...: Must be empty.

Value

A list with a y element (predicted values) and optional standard_errors/confidence_lower/confidence_upper/ prediction_lower/prediction_upper/derivative elements.

Examples

x <- seq(0, 10, length.out = 100)
y <- sin(x) + rnorm(100, 0, 0.1)
model <- Lowess(fraction = 0.2, retain_model = TRUE)
fit(model, x, y)
#> <LowessResult>
#>   Points:            100 
#>   Fraction Used:     0.2 
#>   Iterations Used:   3 
predict(model, c(2.5, 7.5))
#> $y
#> [1] 0.5211602 0.8473707
#>