plot.Cluster: Plot Parameter Selection Results in Surface Estimation

In DRIP: Discontinuous Regression and Image Processing

Plot Parameter Selection Results in Surface Estimation

Description

Plot information about a clustering-based surface estimation parameter selection object.

Usage

## S3 method for class 'Surface_Cluster_Parameters'
plot(x, ...)

Arguments

x	A clustering-based surface estimation parameter selection object.
...	Further arguments passed to or from other methods.

Details

Plot some information about a clustering-based surface estimation parameter selection object. In particular, it plots the cross-validation (no blur) or modified cross-validation (there is blur involved) scores against the specified bandwidth values.

Value

A plot of (modified) cross-validation scores is produced.

Author(s)

Yicheng Kang

References

Kang, Y., Mukherjee, P.S. and Qiu, P. (2018) "Efficient Blind Image Deblurring Using Nonparametric Regression and Local Pixel Clustering", Technometrics, 60(4), 522 – 531, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/00401706.2017.1415975")}.

Qiu, P. (2009) "Jump-Preserving Surface Reconstruction from Noisy Data", Annals of the Institute of Statistical Mathematics, 61, 715 – 751, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s10463-007-0166-9")}.

See Also

surfaceCluster_bandwidth, print.Surface_Cluster_Parameters, summary.Surface_Cluster_Parameters

Examples

data(brain)
bandwidth_select <- surfaceCluster_bandwidth(image = brain,
    bandwidths = c(3:4), sig.level = .9995, blur = FALSE)
plot(bandwidth_select)

DRIP documentation built on April 4, 2025, 12:31 a.m.