supsmu {stats} | R Documentation

## 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"`.

### 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.

### Value

A list with components

`x` | the input values in increasing order with duplicates removed.
`y` | the corresponding y values on the fitted curve.

### 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)
        })

[Package stats version 3.4.2 Index]