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. A Variable Span Scatterplot Smoother. Palo Alto, CA: Laboratory for Computational Statistics, Stanford University. Technical Report No. 5.
Friedman, J. H. 1984. SMART User's Guide. Palo Alto, CA: Laboratory for Computational Statistics, Stanford University. Technical Report No. 1.
See Also
Examples
smoxy <- with(Sdatasets::fuel.frame, supsmu(Weight, Mileage))