This routine helps in finding an optimum stepsize modification factor for CoxBoost
, i.e., that results in an optimum in terms of crossvalidated partial loglikelihood.
1 2 3 4 
time 
vector of length 
status 
censoring indicator, i.e., vector of length 
x 

direction 
direction of line search for an optimal stepsize 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 crossvalidation 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 stepsize modification factors. 
critmat 
matrix with the mean partial loglikelihood for each stepsize modification factor in the course of the boosting steps. 
optimal.factor.index 
index of the optimal stepsize modification factor. 
optimal.factor 
optimal stepsize modification factor. 
optimal.step 
optimal boosting step number, i.e., with minimum mean partial loglikelihood, for stepsize modification factor 
Written by Harald Binder binderh@unimainz.de.
Binder, H. and Schumacher, M. (2009). Incorporating pathway information into boosting estimation of highdimensional risk prediction models. BMC Bioinformatics. 10:18.
CoxBoost
, cv.CoxBoost
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27  ## Not run:
# Generate some survival data with 10 informative covariates
n < 200; p < 100
beta < c(rep(1,10),rep(0,p10))
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 stepsize 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 stepsize modification parameter and optimal number of boosting
# steps obtained by crossvalidation
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)

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