DRIP (version 2.3)

print.Surface_Cluster_Parameters: Print Parameter Selection Results in Surface Estimation

Description

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

Usage

# S3 method for Surface_Cluster_Parameters
print(x, type = "all", ...)

Value

A display of parameter selection results in clustering-based surface estimation.

Arguments

x

A clustering-based surface estimation parameter selection object.

type

The type of information to display. The "cv_scores" option prints the cross-validation or modified cross-validation scores for each bandwidth. The "sigma" option prints the estimated noise level. The "phi0" option prints the estimated value of the error density at 0. The "mean_std_abs" option prints the estimated mean of absolute error. The "all" option prints all the information.

...

Further arguments passed to or from other methods.

Author

Yicheng Kang

Details

Prints some information about a clustering-based surface estimation parameter selection object. In particular, this method prints the cross- validation or modified cross-validation scores, the selected bandwidth, the estimated noise level, the estimated value of the error density at 0 and the estimated mean of absolute error.

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, 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, tools:::Rd_expr_doi("10.1007/s10463-007-0166-9").

See Also

surfaceCluster_bandwidth, summary.Surface_Cluster_Parameters, plot.Surface_Cluster_Parameters

Examples

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