VS: Function to perform variable selection using SNC-BIC...
In psbcGroup: Penalized Parametric and Semiparametric Bayesian Survival Models with Shrinkage and Grouping Priors
VS R Documentation
Function to perform variable selection using SNC-BIC thresholding method
Description
The VS is a function to perform variable selection using SNC-BIC thresholding method
Usage
VS(fit, X, psiVec=seq(0.001, 1, 0.001))
Arguments
fit
an object of class aftGL, psbcEN, psbcFL, or psbcGL.
X
a covariate matrix, n observations by p variables
psiVec
a vector of candidate threshold values for the SNC step
Author(s)
Kyu Ha Lee
References
Lee, K. H., Chakraborty, S., and Sun, J. (2017).
Variable Selection for High-Dimensional Genomic Data with Censored Outcomes Using Group Lasso Prior. Computational Statistics and Data Analysis, Volume 112, pages 1-13.
See Also
psbcEN, psbcFL, psbcGL, aftGL
psbcGroup documentation built on May 29, 2024, 4:02 a.m.
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| VS | R Documentation |
Function to perform variable selection using SNC-BIC thresholding method
Description
The VS is a function to perform variable selection using SNC-BIC thresholding method
Usage
VS(fit, X, psiVec=seq(0.001, 1, 0.001))
Arguments
fit |
an object of class |
X |
a covariate matrix, |
psiVec |
a vector of candidate threshold values for the SNC step |
Author(s)
Kyu Ha Lee
References
Lee, K. H., Chakraborty, S., and Sun, J. (2017).
Variable Selection for High-Dimensional Genomic Data with Censored Outcomes Using Group Lasso Prior. Computational Statistics and Data Analysis, Volume 112, pages 1-13.
See Also
psbcEN, psbcFL, psbcGL, aftGL
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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