# supsmu

##### Friedman's SuperSmoother

Smooth the (x, y) values by Friedman's

- Keywords
- smooth

##### Usage

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

##### 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.

##### 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: `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.

Friedman, J. H. (1984)
A variable span scatterplot smoother.
Laboratory for Computational Statistics, Stanford University Technical
Report No.

##### See Also

##### Examples

`library(stats)`

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

*Documentation reproduced from package stats, version 3.3, License: Part of R 3.3*