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

ICIcombined {aws} | R Documentation

## Adaptive smoothing by Intersection of Confidence Intervals (ICI) using multiple windows

### Description

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

### Usage

    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"))

### 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.
`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.
`combine` | 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.
`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 6.2 in Katkovnik et al (2006).

### Value

An object of class `ICIsmooth`

### Author(s)

Joerg Polzehl polzehl@wias-berlin.de

### References

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

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

[Package aws version 2.2-1 Index]