| optimStepSizeFactor {CoxBoost} | R Documentation |
This routine helps in finding an optimum step-size modification factor for CoxBoost, i.e., that results in an optimum in terms of cross-validated partial log-likelihood.
optimStepSizeFactor(time,status,x,
direction=c("down","up","both"),start.stepsize=0.1,
iter.max=10,constant.cv.res=NULL,
parallel=FALSE,trace=FALSE,...)
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. If this vector contains elements not equal to 0 or 1, these are taken to indicate events from a competing risk and a model for the subdistribution hazard with respect to event 1 is fitted (see e.g. Fine and Gray, 1999). |
x |
n * p matrix of covariates. |
direction |
direction of line search for an optimal step-size modification factor (starting from value 1). |
start.stepsize |
step size used for the line search. A final step is performed using half this size. |
iter.max |
maximum number of search iterations. |
constant.cv.res |
result of cv.CoxBoost for stepsize.factor=1, that can be provided for saving computing time, if it already is available. |
parallel |
logical value indicating whether computations in the cross-validation folds should be performed in parallel on a compute cluster. Parallelization is performed via the package snowfall and the initialization function of of this package, sfInit, should be called before calling cv.CoxBoost. |
trace |
logical value indicating whether information on progress should be printed. |
... |
miscellaneous parameters for cv.CoxBoost. |
A coarse line search is performed for finding the best parameter stepsize.factor for CoxBoost. If an pendistmat argument is provided (which is passed on to CoxBoost), a search for factors smaller than 1 is sensible (corresponding to direction="down"). If no connection information is provided, it is reasonable to employ direction="both", for avoiding restrictions without subject matter knowledge.
List with the following components:
factor.list |
array with the evaluated step-size modification factors. |
critmat |
matrix with the mean partial log-likelihood for each step-size modification factor in the course of the boosting steps. |
optimal.factor.index |
index of the optimal step-size modification factor. |
optimal.factor |
optimal step-size modification factor. |
optimal.step |
optimal boosting step number, i.e., with minimum mean partial log-likelihood, for step-size modification factor optimal.factor. |
Written by Harald Binder binderh@fdm.uni-freiburg.de.
Binder, H. and Schumacher, M. (2008b). Incorporating pathway information into boosting estimation of high-dimensional risk prediction models. Manuscript.
## 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 step-size modification factor. As there is no connection matrix,
# perform search into both directions
optim.res <- optimStepSizeFactor(direction="both",
time=obs.time,status=status,x=x,
trace=TRUE)
# Fit with obtained step-size modification parameter and optimal number of boosting
# steps obtained by cross-validation
cbfit <- CoxBoost(time=obs.time,status=status,x=x,
stepno=optim.res$optimal.step,
stepsize.factor=optim.res$optimal.factor)
summary(cbfit)
## End(Not run)