Logrank.stat.tie {depend.truncation} | R Documentation |
The weighted log-rank statistics for testing quasi-independence (with ties in data)
Description
The three log-rank statistics (L_0, L_1, and L_log) corresponding to 3 different weights.
Usage
Logrank.stat.tie(x.trunc, z.trunc, d)
Arguments
x.trunc |
vector of variables satisfying x.trunc<=z.trunc |
z.trunc |
vector of variables satisfying x.trunc<=z.trunc |
d |
censoring indicator(0=censoring,1=failure) for z.trunc |
Details
If there is no tie in the data, the function "Logrank.stat.tie" and "Logrank.stat" give identical results. However, "Logrank.stat" is computationally more efficient. The simulations of Emura & Wang (2010) are based on "Logrank.stat" since simulated data are generated from continuous distributions. The real data analyses of Emura & Wang (2010) are based on "Logrank.stat.tie" since there are many ties in the data.
Value
L0 |
Logrank statistics (most powerfull to detect the Clayton copula type dependence) |
L1 |
Logrank statistics (most powerfull to detect the Frank copula type dependence) |
Llog |
Logrank statistics (most powerfull to detect the Gumbel copula type dependence) |
Author(s)
Takeshi Emura
References
Emura T, Wang W (2010) Testing quasi-independence for truncation data. Journal of Multivariate Analysis 101, 223-239
Examples
x.trunc=c(10,5,7,1,3,9)
z.trunc=c(12,11,8,6,4,13)
d=c(1,1,1,1,0,1)
Logrank.stat.tie(x.trunc,z.trunc,d)
Logrank.stat(x.trunc,z.trunc,d) ## since there is no tie, the results are the same.