supsmu {stats} | R Documentation |
Smooth the (x, y) values by Friedman's ‘super smoother’.
supsmu(x, y, wt, span = "cv", periodic = FALSE, bass = 0)
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. |
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.
require(graphics) with(cars, { plot(speed, dist) lines(supsmu(speed, dist)) lines(supsmu(speed, dist, bass = 7), lty = 2) })