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 |
status |
censoring indicator, i.e., vector of length |
x |
|
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 |
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 |
trace |
logical value indicating whether information on progress should be printed. |
... |
miscellaneous parameters for |
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 |
Written by Harald Binder binderh@fdm.uni-freiburg.de.
Binder, H. and Schumacher, M. (2009). Incorporating pathway information into boosting estimation of high-dimensional risk prediction models. BMC Bioinformatics. 10:18.
## 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)