JPLLK_surface: Jump-Preserving Local Linear Kernel Smoothing
View source: R/JPLLK_surface.R
| JPLLK_surface | R Documentation |
Jump-Preserving Local Linear Kernel Smoothing
Description
Estimate surface using piecewise local linear kernel smoothing. The bandwidth is chosen by leave-one-out cross validation.
Usage
JPLLK_surface(image, bandwidth, plot = FALSE)
Arguments
image |
A square matrix, no missing value allowed. |
bandwidth |
A numeric vector of positive integers, which specifies the number of pixels used in the local smoothing. The final fitted surface uses the optimal bandwidth chosen from those provided by users. |
plot |
If plot = TRUE, the image of the fitted surface 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. 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.
Value
A list of fitted values, residuals, chosen bandwidth and estimated sigma.
Author(s)
Yicheng Kang
References
Qiu, P. (2009) "Jump-Preserving Surface Reconstruction from Noisy Data", Annals of the Institute of Statistical Mathematics, 61(3), 715 – 751, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s10463-007-0166-9")}.
See Also
threeStage, surfaceCluster
Examples
fit <- JPLLK_surface(image = sar, bandwidth = c(3, 4))
