The function performs adaptive smoothing by Intersection of Confidence Intervals (ICI) using multiple windows as described in Katkovnik et al (2006)
ICIcombined(y, hmax, hinc = 1.45, thresh = NULL, kern = "Gaussian", m = 0,
sigma = NULL, nsector = 1, symmetric = FALSE, presmooth = FALSE,
combine = "weighted", unit = c("SD","FWHM"))An object of class ICIsmooth
Object of class "array" containing the original (response) data
on a grid
maximum bandwidth
factor used to increase the bandwidth from scale to scale
threshold used in tests to determine the best scale
Determines the kernel function.
Object of class "character" kernel, can be any of
c("Gaussian","Uniform","Triangle","Epanechnicov","Biweight","Triweight").
Defaults to kern="Gaussian".
Object of class "integer" vector of length length(dy)
determining the order of derivatives specified for the coordinate directios.
error standard deviation
number of sectors to use.
Object of class "logical" determines if sectors are symmetric with respect to
the origin.
Object of class "logical" determines if bandwidths are smoothed
for more stable results.
Either "weighted" or "minvar". Determines how whether to combine
sectorial results a weighted (with inverse variance) mean or to chose the sectorial
estimate with minimal variance.
How should the bandwidth be interpreted in case of a Gaussian kernel.
For "SD" the bandwidth refers to the standard deviation of the
kernel while "FWHM" interprets the banwidth in terms of Full Width Half
Maximum of the kernel.
Joerg Polzehl polzehl@wias-berlin.de
This mainly follows Chapter 6.2 in Katkovnik et al (2006).
J. Polzehl, K. Papafitsoros, K. Tabelow (2020). Patch-Wise Adaptive Weights Smoothing in R, Journal of Statistical Software, 95(6), 1-27. doi:10.18637/jss.v095.i06.
V. Katkovnik, K. Egiazarian and J. Astola, Local Approximation Techniques in Signal And Image Processing, SPIE Society of Photo-Optical Instrumentation Engin., 2006, PM157
ICIsmooth, ICIsmooth-class, kernsm