USVT: estimates the network probability matrix by the improved...
In randnet: Random Network Model Estimation, Selection and Parameter Tuning
USVT R Documentation
estimates the network probability matrix by the improved universal singular value thresholding
Description
estimates the network probability matrix by the universal singular value thresholding of Chatterjee (2015), with the improvement mentioned in Zhang et. al. (2017).
Usage
USVT(A)
Arguments
A
adjacency matrix
Details
Instead of using the original threshold in Chatterjee (2015), the estimate is generated by taking the n^(1/3) leading spectral components. The method was mentioned in Zhang et. al. (2017) and suggested by an anonymous reviewer.
Value
The estimated probability matrix.
Author(s)
Tianxi Li and Can M. Le
Maintainer: Tianxi Li tianxili@virginia.edu
References
S. Chatterjee. Matrix estimation by universal singular value thresholding. The Annals of Statistics,
43(1):177-214, 2015.
Y. Zhang, E. Levina, and J. Zhu. Estimating network edge probabilities by neighbourhood smoothing. Biometrika, 104(4):771-783, 2017.
See Also
LSM.PGD
Examples
dt <- RDPG.Gen(n=600,K=2,directed=TRUE)
A <- dt$A
fit <- USVT(A)
randnet documentation built on May 31, 2023, 6:44 p.m.
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| USVT | R Documentation |
estimates the network probability matrix by the improved universal singular value thresholding
Description
estimates the network probability matrix by the universal singular value thresholding of Chatterjee (2015), with the improvement mentioned in Zhang et. al. (2017).
Usage
USVT(A)
Arguments
A |
adjacency matrix |
Details
Instead of using the original threshold in Chatterjee (2015), the estimate is generated by taking the n^(1/3) leading spectral components. The method was mentioned in Zhang et. al. (2017) and suggested by an anonymous reviewer.
Value
The estimated probability matrix.
Author(s)
Tianxi Li and Can M. Le
Maintainer: Tianxi Li tianxili@virginia.edu
References
S. Chatterjee. Matrix estimation by universal singular value thresholding. The Annals of Statistics, 43(1):177-214, 2015. Y. Zhang, E. Levina, and J. Zhu. Estimating network edge probabilities by neighbourhood smoothing. Biometrika, 104(4):771-783, 2017.
See Also
LSM.PGD
Examples
dt <- RDPG.Gen(n=600,K=2,directed=TRUE)
A <- dt$A
fit <- USVT(A)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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