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

Usage

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

Arguments

object: A Loess object, fitted (via fit) with retain_model = TRUE passed to Loess.
new_x: Numeric vector of out-of-sample query points (flattened, dimensions values per point).
intervals: Grouped coverage levels from intervals_opts.
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 neighbor window before predict errors, catching in-range-but-sparse query points. NULL (default) disables the cap.
outputs: Optional character vector selecting "se", "gradient", or "derivative". NULL (default) selects no optional components.
...: 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 <- Loess(fraction = 0.2, retain_model = TRUE)
fit(model, x, y)
#> <LoessResult>
#>   Points:            100 
#>   Fraction Used:     0.2 
#>   Iterations Used:   3 
predict(model, c(2.5, 7.5))
#> $y
#> [1] 0.5095815 0.8260879
#>