stats (version 3.4.0)

supsmu: Friedman's SuperSmoother

Description

Smooth the (x, y) values by Friedman's ‘super smoother’.

Usage

supsmu(x, y, wt =, span = "cv", periodic = FALSE, bass = 0, trace = FALSE)

Arguments

x

x values for smoothing

y

y values for smoothing

wt

case weights, by default all equal

span

the fraction of the observations in the span of the running lines smoother, or "cv" to choose this by leave-one-out cross-validation.

periodic

if TRUE, the x values are assumed to be in [0, 1] and of period 1.

bass

controls the smoothness of the fitted curve. Values of up to 10 indicate increasing smoothness.

trace

logical, if true, prints one line of info “per spar”, notably useful for "cv".

Value

A list with components

x

the input values in increasing order with duplicates removed.

y

the corresponding y values on the fitted curve.

Details

supsmu is a running lines smoother which chooses between three spans for the lines. The running lines smoothers are symmetric, with k/2 data points each side of the predicted point, and values of k as 0.5 * n, 0.2 * n and 0.05 * n, where n is the number of data points. If span is specified, a single smoother with span span * n is used. The best of the three smoothers is chosen by cross-validation for each prediction. The best spans are then smoothed by a running lines smoother and the final prediction chosen by linear interpolation. The FORTRAN code says: “For small samples (n < 40) or if there are substantial serial correlations between observations close in x-value, then a pre-specified fixed span smoother (span > 0) should be used. Reasonable span values are 0.2 to 0.4.” Cases with non-finite values of x, y or wt are dropped, with a warning.

References

Friedman, J. H. (1984) SMART User's Guide. Laboratory for Computational Statistics, Stanford University Technical Report No. 1. Friedman, J. H. (1984) A variable span scatterplot smoother. Laboratory for Computational Statistics, Stanford University Technical Report No. 5.

See Also

ppr

Examples

require(graphics)

with(cars, {
    plot(speed, dist)
    lines(supsmu(speed, dist))
    lines(supsmu(speed, dist, bass = 7), lty = 2)
    })