R: Adaptive smoothing by Intersection of Confidence Intervals...

ICIsmooth {aws} | R Documentation

## Adaptive smoothing by Intersection of Confidence Intervals (ICI)

### Description

The function performs adaptive smoothing by Intersection of Confidence Intervals (ICI) as described in Katkovnik et al (2006)

### Usage

    ICIsmooth(y, hmax, hinc = 1.45, thresh = NULL, kern = "Gaussian", m = 0,
              sigma = NULL, nsector = 1, sector = 1, symmetric = FALSE,
              presmooth = FALSE, unit = c("SD","FWHM"))

### Arguments

`y` | Object of class `"array"` containing the original (response) data on a grid
`hmax` | maximum bandwidth
`hinc` | factor used to increase the bandwidth from scale to scale
`thresh` | threshold used in tests to determine the best scale
`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.
`sigma` | error standard deviation
`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.
`presmooth` | Object of class `"logical"` determines if bandwidths are smoothed for more stable results.
`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

This mainly follows Chapter 6.1 in Katkovnik et al (2006).

### Value

An object of class `ICIsmooth`

### Author(s)

Joerg Polzehl polzehl@wias-berlin.de

### References

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 (URL: http://doi.org/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

### See Also

`ICIcombined`, `ICIsmooth-class`, `kernsm`

[Package aws version 2.5-1 Index]