LoglikPen: Compute the penalized log partial likelihood for a Cox PH...

In xgsu/coxphMIC: Sparse Estimation of Cox Proportional Hazards Models via Approximated Inforamtion Criterion

Description

Compute the penalized log partial likelihood for a Cox PH model with MIC penalty

Usage

LoglikPen(beta, time, status, X, lambda, a)

Arguments

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.

Value

The value of the penalized log partial likelihood function evaluated at beta.

References

• 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

See Also

coxph

xgsu/coxphMIC documentation built on May 4, 2019, 1:06 p.m.