Sn {robustbase} | R Documentation |
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.
mad
for the ‘most robust’ but much
less efficient scale estimator;
Qn
for a similar more efficient but slower alternative;
scaleTau2
.
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)