Description Usage Arguments Details Value Author(s) References See Also Examples
Extends R package grpreg to the proportional subdistribution hazards (PSH) model (Fine and Gray, 1999). Performs penalized variable selection at the group level. Penalties include group LASSO, adaptive group LASSO, group SCAD, and group MCP.
1 2 3 4 
time 
vector of failure/censoring times 
fstatus 
vector with a unique code for each failure type and a separate code for censored observations 
X 
design matrix; 
failcode 
code of fstatus that denotes the failure type of interest 
cencode 
code of fstatus that denotes censored observations 
group 
vector of group indicator (see details) 
penalty 
penalty to be applied to the model. Either "gLASSO", "gSCAD", or "gMCP" 
gamma 
tuning parameter of the gMCP/gSCAD penalty. Default is 2.7 for group MCP and 3.7 for group SCAD. 
alpha 
tuning parameter indicating contributions from the MCP/SCAD penalty and the L2 penalty. 
lambda.min 
the smallest value for 
nlambda 
number of 
lambda 
a userspecified sequence of 
eps 
iteration stops when the relative change in any coefficient is less than 
max.iter 
maximum number of iterations. Default is 1000 
weighted 
Default is 
The group
vector indicates the grouping of variables. For greatest efficiency, group
should be a vector of consecutive integers, although unordered groups are also allowed.
Penalties include group LASSO, group SCAD, and group MCP. We also include adaptive group LASSO by putting weighted=TRUE
. The gcrrp
function calculates dataadaptive weights formulated by the maximum parital likelihood estimator(MPLE) of the PSH model. The weight for each group is the inverse of the norm of the corresponding subvector of MPLE. The algorithm employed is the group coordinate descent algorithm.
Return a list of class gcrrp
with components
$beta 
fitted coefficients matrix with 
$iter 
number of iterations until convergence for each 
$group 
same as above 
$lambda 
sequence of tuning parameter values 
$penalty 
same as above. 
$gamma 
same as above. 
$alpha 
same as above. 
$loglik 
log likelihood of the fitted model at each value of

$GCV 
generalized cross validation of the fitted model at each value of

$BIC 
Bayesian information criteria of the fitted model at each value of

Zhixuan Fu <zhixuan.fu@yale.edu>
Breheny, P. and Huang, J. (2012) Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors. Statistics and Computing
Fine J. and Gray R. (1999) A proportional hazards model for the subdistribution of a competing risk. JASA 94:496509.
Fu Z., Parikh C. and Zhou B.(2015). Penalized variable selection in competing risks regression. Manuscript submitted for publication.
Huang J., Breheny, P. and Ma, S. (2012). A selective review of group selection in high dimensional models. Statistical Science, 27: 481499.
crrp, cmprsk, grpreg
1 2 3 4 5 6 7 8 9 10 11 12  set.seed(10)
ftime < rexp(200)
fstatus < sample(0:2,200,replace=TRUE)
cov < matrix(runif(2000),nrow=200)
dimnames(cov)[[2]] < paste("x", 1:ncol(cov))
group < c(1,1,2,2,2,3,4,4,5,5)
#fit gSCAD penalty
fit1 < gcrrp(ftime, fstatus, cov, group=group, penalty="gSCAD")
beta1 < fit1$beta[, which.min(fit1$BIC)]
#fit adaptive gLASSO
fit2 < gcrrp(ftime, fstatus, cov, group=group, penalty="gLASSO", weighted=TRUE)
beta2 < fit2$beta[, which.min(fit2$BIC)]

Loading required package: survival
Loading required package: Matrix
Loading required package: cmprsk
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