CGGM.mean {epsi} | R Documentation |
Computation of two dimensional redescending M-kernel estimators.
CGGM.mean(data, h, g=NULL, silent=FALSE) CGGM.lts(data, h, g=NULL, trim=0, silent=FALSE) CGGM.autoscale(data, h, silent=FALSE)
data |
numerical matrix of observed data. |
h |
positive number. Bandwidth for the kernel. |
g |
optional positive number. Scale parameter. If g is NULL
(default), the scale parameter is determined automatically by the
function CGGM.autoscale . |
trim |
optional number within [0,1). Part of observations trimmed
by CGGM.lts |
silent |
optional boolean. If true, CGGM.autoscale
produces no output. |
CGGM.mean
implements a corner-preserving smoothing method
introduced by Chu et al. (1998) which is based on a redescending
M-kernel estimator. As kernel and score function the density of the
standard normal distribution is used. A robust version of this
estimator is introduced by Hillebrand (2002) and implemented in
CGGM.lts
.
CGGM.autoscale
calculates the median of the interquartile
ranges within the 'windows' used in CGGM.mean
and
CGGM.lts
. This can be used as scale parameter.
Return value is a numerical matrix containing the smoothed data.
Tim Garlipp (garlipp@mathematik.uni-oldenburg.de)
Chu, C.K., Glad, I.K., Godtliebsen, F., Marron, J.S. (1998) Edge-Preserving Smoothers for Image Processing, J. Amer. Statis. Assoc. 93, 526-541.
Hillebrand, M. (2002) On Robust Corner-Preserving Smoothing in Image Processing, Carl-von-Ossietzky-Universität Oldenburg, Dissertation (http://docserver.bis.uni-oldenburg.de/publikationen/dissertation/2003/hilonr03/hilonr03.html).
y <- matrix(rep(0,60*60),nrow=60) y[21:40,21:40]<-1 y <- y + matrix(rnorm(60*60,0,0.1),nrow=60) image(y,col=gray(seq(0,1,1/255))) ymean <- CGGM.mean(y,0.04) image(ymean,col=gray(seq(0,1,1/255)))