diffLLK {DRIP} R Documentation

## local linear kernel difference

### Description

Compute difference between two one-sided LLK estimators along the gradient direction.

### Usage

diffLLK(image, bandwidth, plot)

### Arguments

 image A square matrix object of size n by n, no missing value allowed. bandwidth A positive integer to specify the number of pixels used in the local smoothing. plot If plot = TRUE, an image of the difference at each pixel is plotted.

### Details

At each pixel, the gradient is estimated by a local linear kernel smoothing procedure. Next, the local neighborhood is divided into two halves along the direction perpendicular to (\widehat{f}'_{x}, \widehat{f}'_{y}). Then the one- sided local linear kernel (LLK) estimates are obtained in the two half neighborhoods respectively.

### Value

Returns a matrix of the estimated difference, |\widehat{f}_+ - \widehat{f}_-|, at each pixel.

### References

Kang, Y., and Qiu, P., "Jump Detection in Blurred Regression Surfaces," Technometrics, 56, 2014, 539-550.

diffLCK, diffLC2K, diffLL2K, stepEdgeLLK
data(sar) # SAR image is bundled with the package and it is a