View source: R/JPLLK_surface.r

JPLLK_surface | R Documentation |

Estimate surface using piecewise local linear kernel smoothing. Bandwidth is chosen by leave-one-out cross validation.

JPLLK_surface(image, bandwidth, plot = FALSE)

`image` |
A square matrix object of size n by n, no missing value allowed. |

`bandwidth` |
A numeric vector with positive integers, which specify the number of pixels used in the local smoothing. The final fitted surface chooses the optimal bandwidth from those provided by users. |

`plot` |
If plot = TRUE, the image of the fitted surface 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 linear kernel (LLK) estimates are obtained in the
two half neighborhoods respectively. Among these two one-sided
estimates, the one with smaller weighted mean square error is
chosen to be the final estimate of the regression surface at the
pixel.

A list of fitted values, residuals, chosen bandwidth and estimated sigma.

Qiu, P., "Jump-preserving surface reconstruction from noisy data", *Annals
of the Institute of Statistical Mathematics*, **61(3)**, 2009, 715-751.

`threeStage`

, `surfaceCluster`

data(sar) # SAR image is bundled with the package and it is a # standard test image in statistics literature. fit = JPLLK_surface(image=sar, bandwidth=c(3, 4))

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