supsmu
Scatter Plot Smoothing Using Super Smoother
Description
Returns a list containing x and y components that
are a smooth of the input data.
This algorithm is designed to be fast,
and by default uses cross validation to pick the span.
Usage
supsmu(x, y, wt = rep(1, n), span = "cv", periodic = FALSE, bass = 0)
Arguments
| x |
vector of abscissa values for the scatter plot.
Missing values (NAs) are allowed.
|
| y |
vector of ordinate values for the scatter plot.
Missing values (NAs) are allowed.
|
| wt |
vector of weights for each (x,y) observation, n is the length
of y.
Missing values (NAs) are allowed.
|
| span |
fraction of observations in the smoothing window.
If span="cv", then automatic (variable) span selection is done by means
of cross validation.
|
| periodic |
if TRUE, the smoother assumes x is a periodic variable
with values in the range [0.0, 1.0] and period 1.0.
An error occurs if x has values outside this range.
|
| bass |
controls high frequency (small span) penalty used with automatic span selection
(bass tone control).
Values of bass less than 0 or greater than 10
have no effect.
|
Value
a list with the following components:
| x |
the sorted input x vector with duplicate points removed.
|
| y |
the smoothed y vector corresponding to the output x.
|
Side Effects
a warning is issued if there were NAs and/or Infs in the input
x, y or wt.
Such observations are dropped from the computations.
Background
The supsmu function serves a purpose similar to that of the function lowess.
supsmu is much faster although it does not have
the robustness properties of lowess.
For small samples (n<40),
or if there are substantial serial correlations between observations close
in x-value,
a pre-specified fixed span smoother (span > 0) should be used.
Reasonable span values are from 0.3 to 0.5.
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
Examples
smoxy <- with(Sdatasets::fuel.frame, supsmu(Weight, Mileage))