Performs permutationbased pvalue estimation for the optional covariates in a fit from CoxBoost
.
1 2 
object 
fit object obtained from 
x 

permute.n 
number of permutations employed for obtaining a null distribution. 
per.covariate 
logical value indicating whether a separate null distribution should be considered for each covariate. A larger number of permutations will be needed if this is wanted. 
parallel 
logical value indicating whether computations for obtaining a null distribution via permutation should be performed in parallel on a compute cluster. Parallelization is performed via the package 
multicore 
indicates whether computations in the permuted data sets should be performed in parallel, using package 
trace 
logical value indicating whether progress in estimation should be indicated by printing the number of the permutation that is currently being evaluated. 
... 
miscellaneous parameters for the calls to 
As pvalue estimates are based on permutations, random numbers are drawn for determining permutation indices. Therfore, the results depend on the state of the random number generator. This can be used to explore the variability due to random variation and help to determine an adequate value for permute.n
. A value of 100 should be sufficient, but this can be quite slow. If there is a considerable number of covariates, e.g., larger than 100, a much smaller number of permutations, e.g., 10, might already work well. The estimates might also be negatively affected, if only a small number of boosting steps (say <50) was employed for the original fit.
Vector with pvalue estimates, one value for each optional covariate specificed in the original call to CoxBoost
.
Harald Binder binderh@unimainz.de
Binder, H., Porzelius, C. and Schumacher, M. (2009). Rankbased pvalues for sparse highdimensional risk prediction models fitted by componentwise boosting. FDMPreprint Nr. 101, University of Freiburg, Germany.
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)
# Fit a Cox proportional hazards model by CoxBoost
cbfit < CoxBoost(time=obs.time,status=status,x=x,stepno=100,
penalty=100)
# estimate pvalues
p1 < estimPVal(cbfit,x,permute.n=10)
# get a second vector of estimates for checking how large
# random variation is
p2 < estimPVal(cbfit,x,permute.n=10)
plot(p1,p2,xlim=c(0,1),ylim=c(0,1),xlab="permute 1",ylab="permute 2")
## End(Not run)

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