Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/chooseLowessSpan.R
Choose an optimal span, depending on the number of points, for lowess smoothing of variance trends.
1 | chooseLowessSpan(n=1000, small.n=25, min.span=0.2, power=1/3)
|
n |
the number of points the lowess curve will be applied to. |
small.n |
the span will be set to 1 for any |
min.span |
the minimum span for large |
power |
numeric power between 0 and 1 how fast the chosen span decreases with |
The span is the proportion of points used for each of the local regressions. When there a few points, a large span should be used to ensure a smooth curve. When there are a large number of points, smaller spans can be used because each span window still contains good coverage. By default, the chosen span decreases as the cube-root of the number of points, a rule that is motivated by analogous rules to choose the number of bins for a histogram (Scott, 1979; Freedman & Diaconis, 1981; Hyndman, 1995).
The span returned is essentially
min.span + (1-min.span) * (small.n/n)^power
.
The span is set to 1 for any n
less than small.n
.
The function is tuned for smoothing of mean-variance trends, for which the trend is usually monotonic, so preference is given to moderately large spans.
Even for the large datasets, the span is always greater than min.span
.
This function is used to create the default span for vooma
, eBayes
, squeezeVar
and fitFDistRobustly
.
A numeric vector of length 1 containing the span value.
Gordon Smyth
Freedman, D. and Diaconis, P. (1981). On the histogram as a density estimator: L_2 theory. Zeitschrift fur Wahrscheinlichkeitstheorie und verwandte Gebiete, 57, 453-476.
Hyndman, R. J. (1995). The problem with Sturges' rule for constructing histograms. http://robjhyndman.com/papers/sturges.pdf.
Scott, D. W. (1979). On optimal and data-based histograms. Biometrika, 66, 605-610.
loessFit
, weightedLowess
, lowess
, loess
.
vooma
, eBayes
, squeezeVar
, fitFDistRobustly
.
1 2 3 4 5 6 | chooseLowessSpan(100)
chooseLowessSpan(1e6)
n <- 10:5000
span <- chooseLowessSpan(n)
plot(n,span,type="l")
|
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