Robust Location-Free Scale Estimate More Efficient than MAD
Compute the robust scale estimator Sn, an efficient alternative to the MAD.
Sn(x, constant = 1.1926, finite.corr = missing(constant)) s_Sn(x, mu.too = FALSE, ...)
x |
numeric vector of observations. |
constant |
number by which the result is multiplied; the default achieves consisteny for normally distributed data. |
finite.corr |
logical indicating if the finite sample bias
correction factor should be applied. Default to |
mu.too |
logical indicating if the |
... |
potentially further arguments for |
............ FIXME ........
Sn() returns a number, the Sn robust scale estimator, scaled to be
consistent for σ^2 and i.i.d. Gaussian observatsions,
optionally bias corrected for finite samples.
s_Sn(x, mu.too=TRUE) returns a length-2 vector with location
(μ) and scale; this is typically only useful for
covOGK(*, sigmamu = s_Sn).
Original Fortran code:
Christophe Croux and Peter Rousseeuw rousse@wins.uia.ac.be.
Port to C and R: Martin Maechler, maechler@R-project.org
Rousseeuw, P.J. and Croux, C. (1993) Alternatives to the Median Absolute Deviation, Journal of the American Statistical Association 88, 1273–1283.
x <- c(1:10, 100+1:9)# 9 outliers out of 19 Sn(x) Sn(x, c=1)# 9 Sn(x[1:18], c=1)# 9 set.seed(153) x <- sort(c(rnorm(80), rt(20, df = 1))) s_Sn(x, mu.too=TRUE)
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