Description Usage Arguments Details Value References Examples
Fits broken adaptive ridge regression for competing risks regression. Based on the crrp package which performs penalized variable selection using LASSO, SCAD, and MCP. This package allows for ridge and broken adaptive ridge penalties.
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ftime |
A vector of event/censoring times. |
fstatus |
A vector with unique code for each event type and a separate code for censored observations. |
X |
A matrix of fixed covariates (nobs x ncovs) |
failcode |
Integer: code of |
cencode |
Integer: code of |
lambda |
Numeric: BAR tuning parameter value |
xi |
Numeric: tuning parameter for initial ridge regression |
delta |
Numeric: change from 2 in ridge norm dimension |
eps |
Numeric: algorithm stops when the relative change in any coefficient is less than |
tol |
Numeric: absolute threshold at which to force coefficients to 0 (default is |
lam.min |
Numeric: smallest value of lambda if performing grid search |
nlambda |
Numeric: number of |
log |
Logical: Whether or not the grid search is log10 spaced (default is |
max.iter |
Numeric: maximum iterations to achieve convergence (default is 1000) |
The crrBAR
function penalizes the log-partial likelihood of the proportional subdistribution hazards model
from Fine and Gray (1999) with the Broken Adaptive Ridge (BAR) penalty. A cyclic coordinate descent algorithm is used for implementation.
For stability, the covariate matrix X
is standardized prior to implementation.
Special cases: Fixing xi
and lambda
to 0 results in the standard competing risk regression using crr
.
Fixing lambda
to 0 and specifying xi
will result in a ridge regression solution.
Returns a list of class crrBAR
.
Breheny, P. and Huang, J. (2011) Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection. Ann. Appl. Statist., 5: 232-253.
Fine J. and Gray R. (1999) A proportional hazards model for the subdistribution of a competing risk. JASA 94:496-509.
Fu Z., Parikh C. and Zhou B. (2017). Penalized variable selection in competing risks regression. Lifetime Data Anal 23:353-376.
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