diffLCK | R Documentation |

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

`diffLCK(image, bandwidth, plot)`

`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. |

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 constant kernel (LCK) estimates are obtained in the
two half neighborhoods respectively.

Returns a matrix of the estimated difference, `|\widehat{f}_+ - \widehat{f}_-|`

,
at each pixel.

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

`diffLLK`

, `diffLC2K`

, `diffLL2K`

,
`stepEdgeLCK`

```
data(sar) # SAR image is bundled with the package and it is a
# standard test image in statistics literature.
diff <- diffLCK(image = sar, bandwidth = 4)
```

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