kernsm: Kernel smoothing on a 1D, 2D or 3D grid

In neuroconductor-releases/aws: Adaptive Weights Smoothing

Description

Performs Kernel smoothing on a 1D, 2D or 3D grid by fft

Usage

kernsm(y, h = 1, kern = "Gaussian", m = 0, nsector = 1, sector = 1,
       symmetric = FALSE, unit = c("SD","FWHM"))

Arguments

y	Object of class "array" containing the original (response) data on a grid
h	bandwidth
kern	Determines the kernel function. Object of class "character" kernel, can be any of c("Gaussian","Uniform","Triangle","Epanechnicov","Biweight","Triweight"). Defaults to kern="Gaussian"
m	Object of class "integer" vector of length length(dy) determining the order of derivatives specified for the coordinate directios.
nsector	number of sectors to use. Positive weights are restricted to the sector selected by sector
sector	Object of class "integer" between 1 and nsector. sector used.
symmetric	Object of class "logical" determines if sectors are symmetric with respect to the origin.
unit	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.

Details

In case of any(m>0) derivative kernels are generated and applied for the corresponding coordinate directions. If nsector>1 the support of the kernel is restricted to a circular sector determined by sector.

Value

An object of class kernsm

Author(s)

Joerg Polzehl [e-mail]

References

Local Approximation Techniques in Signal And Image Processing, SPIE Society of Photo-Optical Instrumentation Engin., 2006, PM157

See Also

kernsm-class, ICIsmooth,ICIcombined

neuroconductor-releases/aws documentation built on Oct. 30, 2020, 11:05 p.m.