crrHARD: Hard Threshold Regression for Competing Risks Regression
In erickawaguchi/crrBAR: Broken Adaptive Ridge for Competing Risks Regression

Description Usage Arguments Details Value References Examples

Fits hard thresholding penalty for competing risks regression. Estimates are calculated via crr from the cmprsk package.

1 2	crrHARD(ftime, fstatus, X, failcode = 1, cencode = 0, lambda = 0, nlambda = 25)

`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 `fstatus` that event type of interest (default is 1)
`cencode`	Integer: code of `fstatus` that denotes censored observations (default is 0)
`nlambda`	Numeric: number of `lambda` values if performing grid search (default is 25)

The crrHARD performs hard thresholding on the MPLE using the rule \hat{β_j} = \hat{beta}_j I(|β_j| > λ)

Returns a list of class crrBAR.

Fine J. and Gray R. (1999) A proportional hazards model for the subdistribution of a competing risk. JASA 94:496-509.

set.seed(10)
ftime <- rexp(200)
fstatus <- sample(0:2, 200, replace = TRUE)
cov <- matrix(runif(1000), nrow = 200)
dimnames(cov)[[2]] <- c('x1','x2','x3','x4','x5')
fit <- crrHARD(ftime, fstatus, cov, lambda = 0.05)
fit$coef