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 user-specified 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 data-adaptive 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 sub-vector 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
|
$BIC |
Bayesian information criteria of the fitted model at each value of
|
Zhixuan Fu <zhixuan.fu@yale.edu>
Breheny, P. and Huang, J. (2012) Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors. Statistics and Computing
Fine J. and Gray R. (1999) A proportional hazards model for the subdistribution of a competing risk. JASA 94:496-509.
Fu Z., Parikh C. and Zhou B.(2015). Penalized variable selection in competing risks regression. Manuscript submitted for publication.
Huang J., Breheny, P. and Ma, S. (2012). A selective review of group selection in high dimensional models. Statistical Science, 27: 481-499.
crrp, cmprsk, grpreg
1 2 3 4 5 6 7 8 9 10 11 12 | set.seed(10)
ftime <- rexp(200)
fstatus <- sample(0:2,200,replace=TRUE)
cov <- matrix(runif(2000),nrow=200)
dimnames(cov)[[2]] <- paste("x", 1:ncol(cov))
group <- c(1,1,2,2,2,3,4,4,5,5)
#fit gSCAD penalty
fit1 <- gcrrp(ftime, fstatus, cov, group=group, penalty="gSCAD")
beta1 <- fit1$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
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.