print.Surface_Cluster_Parameters {DRIP} | R Documentation |
Print Parameter Selection Results in Surface Estimation
Description
Display information about a clustering-based surface estimation parameter selection object.
Usage
## S3 method for class 'Surface_Cluster_Parameters'
print(x, type = "all", ...)
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. |
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.
Value
A display of parameter selection results in clustering-based surface estimation.
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, 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, 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")