supsmu
Friedman's SuperSmoother
Smooth the (x, y) values by Friedman's ‘super smoother’.
- 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: “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.\ifelse{latex}{\out{~}}{ } 1.
Friedman, J. H. (1984) A variable span scatterplot smoother. Laboratory for Computational Statistics, Stanford University Technical Report No.\ifelse{latex}{\out{~}}{ } 5.
See Also
Examples
library(stats)
require(graphics)
with(cars, {
plot(speed, dist)
lines(supsmu(speed, dist))
lines(supsmu(speed, dist, bass = 7), lty = 2)
})