Description Usage Arguments Value References Examples
Perform permutation test based on conditional or unconditional approach.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 
trun 
is the left truncation time. 
obs 
is the observed failure time. 
permSize 
is the number of permutations. 
cens 
is the status indicator if the observed failure time are subjected to rightcensoring; 0 = censored, 1 = event. 
sampling 
a character string specifying the sampling method used in permutation. The following are permitted:

kendallOnly, minp1Only, minp2Only 
optional values indicating which test statistics to be used.
If all leave as 
nc 
is the number of cores used in permutation.
When 
seed 
an optional vector containing random seeds to be used to generate permutation samples. Random seeds will be used when left unspecified. 
minp.eps 
an optional value indicating the width of the intervals used in minp2 procedure. The following input are allowed:

plot.int 
an optional logical value indicating whether an animated scatterplot will be produced to how the minp intervals are chosen for the observed data. 
anim_name 
an optional character string specifying the file name that the animation to be saved.
When not specified, file name based on the current system date and time will be used.
This argument will only be executed when 
A list containing output with the following components:
the observed pvalue using Kendall's tau test statistic.
the observed pvalue using minp1 test statistic.
the observed pvalue using minp2 test statistic.
the observed minp1 test statistic.
the observed minp2 test statistic.
Kendall's tau test statistics from permutation samples.
minp1 test statistics from permutation samples.
minp2 test statistics from permutation samples.
Chiou, S.H., Qian, J., and Betensky, R.A. (2018). Permutation Test for General Dependent Truncation. Computational Statistics \& Data Analysis,128, p308–324.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23  simDat < function(n) {
k < s < 1
tt < xx < yy < cc < delta < rep(1, n)
while(k <= n){
tt[k] < runif(1, 0, 3.5)
xx[k] < 1.95 + 0.65 * (tt[k]  1.25)^2 + rnorm(1, sd = 0.1)
cc[k] < runif(1, 0, 10)
delta[k] < (xx[k] <= cc[k])
yy[k] < pmin(xx[k], cc[k])
s < s + 1
if(tt[k] <= yy[k]) k = k+1
}
data.frame(list(trun = tt, obs = yy, delta = delta))
}
set.seed(123)
dat < simDat(50)
B < 20
## Perform conditional permutation with Kendall's tau, minp1 and minp2
set.seed(123)
system.time(fit < with(dat, permDep(trun, obs, B, delta, nc = 1)))
fit

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