| supsmu | R Documentation |
Smooth the (x, y) values by Friedman's ‘super smoother’.
supsmu(x, y, wt =, span = "cv", periodic = FALSE, bass = 0, trace = FALSE)
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 |
periodic |
if |
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 |
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.
A list with components
x |
the input values in increasing order with duplicates removed. |
y |
the corresponding y values on the fitted curve. |
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.
ppr
require(graphics)
with(cars, {
plot(speed, dist)
lines(supsmu(speed, dist))
lines(supsmu(speed, dist, bass = 7), lty = 2)
})
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