optimCoxBoostPenalty {CoxBoost}R Documentation

Coarse line search for adequate penalty parameter

Description

This routine helps in finding a penalty value that leads to an ``optimal'' number of boosting steps for CoxBoost, determined by cross-validation, that is not too small/in a specified range.

Usage

optimCoxBoostPenalty(time,status,x,minstepno=50,maxstepno=200,
                     start.penalty=500,iter.max=10,upper.margin=0.05,
                     trace=FALSE,...)

Arguments

time vector of length n specifying the observed times.
status censoring indicator, i.e., vector of length n with entries 0 for censored observations and 1 for uncensored observations.
x n * p matrix of covariates.
minstepno, maxstepno range of boosting steps in which the ``optimal'' number of boosting steps is wanted to be.
start.penalty start value for the search for the appropriate penalty.
iter.max maximum number of search iterations.
upper.margin specifies the fraction of maxstepno which is used as an upper margin in which a cross-validation minimum is not taken to be one. This is necessary because of random fluctuations of cross-validated partial log-likelihood.
trace logical value indicating whether information on progress should be printed.
... miscellaneous parameters for cv.CoxBoost.

Details

The penalty parameter for CoxBoost has to be chosen only very coarsely. In Tutz and Binder (2006) it is suggested for likelihood based boosting just to make sure, that the optimal number of boosting steps, according to some criterion such as cross-validation, is larger or equal to 50. With a smaller number of steps, boosting may become too ``greedy'' and show sub-optimal performance. This procedure uses a very coarse line search and so one should specify a rather large range of boosting steps.

Value

List with element penalty containing the ``optimal'' penalty and cv.res containing the corresponding result of cv.CoxBoost.

Author(s)

Written by Harald Binder binderh@fdm.uni-freiburg.de.

References

Tutz, G. and Binder, H. (2006) Generalized additive modelling with implicit variable selection by likelihood based boosting. Biometrics, 62:961-971.

See Also

CoxBoost, cv.CoxBoost

Examples

## Not run: 
#   Generate some survival data with 10 informative covariates 
n <- 200; p <- 100
beta <- c(rep(1,10),rep(0,p-10))
x <- matrix(rnorm(n*p),n,p)
real.time <- -(log(runif(n)))/(10*exp(drop(x %*% beta)))
cens.time <- rexp(n,rate=1/10)
status <- ifelse(real.time <= cens.time,1,0)
obs.time <- ifelse(real.time <= cens.time,real.time,cens.time)

#  determine penalty parameter

optim.res <- optimCoxBoostPenalty(time=obs.time,status=status,x=x,
                                  trace=TRUE,start.penalty=500)

#   Fit with obtained penalty parameter and optimal number of boosting
#   steps obtained by cross-validation

cbfit <- CoxBoost(time=obs.time,status=status,x=x,
                  stepno=optim.res$cv.res$optimal.step,
                  penalty=optim.res$penalty) 
summary(cbfit)

## End(Not run)


[Package CoxBoost version 1.0 Index]