Description Usage Arguments Value References See Also
Compute the penalized log partial likelihood for a Cox PH model with MIC penalty
1 |
beta |
A p-dimensional vector containing the regression ceofficients in the CoxPH model. |
time |
The observed survival time. |
status |
The status indicator: 1 for event and 0 for censoring. |
X |
An n by p design matrix. |
lambda |
The penalty parameter euqals either 2 in AIC or ln(n0) in BIC (by default), where n0 is the number of uncensored survival times observed in the data. You can also specify it to a specific value of your own choice. |
a |
The scale parameter in the hyperbolic tangent function of the MIC penalty. By default, a = n0, i.e., the number of uncensored survival times observed in the data. |
The value of the penalized log partial likelihood function evaluated at beta.
Abdolyousefi, R. N. and Su, X. (2016). coxphMIC: An R package for sparse estimation of Cox PH Models via approximated information criterion. Tentatively accepted, The R Journal.
Su, X. (2015). Variable selection via subtle uprooting. Journal of Computational and Graphical Statistics, 24(4): 1092–1113. URL http://www.tandfonline.com/doi/pdf/10.1080/10618600.2014.955176
Su, X., Wijayasinghe, C. S., Fan, J., and Zhang, Y. (2015). Sparse estimation of Cox proportional hazards models via approximated information criteria. Biometrics, 72(3): 751–759. URL http://onlinelibrary.wiley.com/doi/10.1111/biom.12484/epdf
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