Description Usage Arguments Value Examples
Given two sequences of paired test statistics, tests whether the latent indicators of significance are positively dependent.
1 2 |
T1, T2 |
paired vectors of test statistics, both must be the same length; can be p-values or otherwise; if not p-values, must be stochastically larger under the null |
m1, m2 |
search only up the m1th (m2th) most significant test statistic in T1 (T2); NULL to search through all statistics |
perm |
the indices of T1 will be randomly permuted |
p1, p2 |
TRUE if T1 (T2) is a vector of p-values |
jitter |
NULL if no jittering is desired to resolve ties, otherwise a jitter of |
D |
value of the test statistic |
p.perm |
permutation p-value |
p.asymp |
asymptotic approximate p-value |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ## generate paired test statistics
p <- 10^6; ## total number of pairs
X <- c(rep(0,p-30),rep(1,10),rep(2,10),rep(3,10));
## X=0: no signal in either sequence of tests
## X=1: signal in sequence 1 only
## X=2: signal in sequence 2 only
## X=3: simultaneous signal
set.seed(1);
Z1 <- rnorm(p,0,1); Z1[X==1|X==3] <- rnorm(20,3,1);
Z2 <- rnorm(p,0,1); Z2[X==2|X==3] <- rnorm(20,4,1);
## convert to p-value
P1 <- 2*pnorm(-abs(Z1));
P2 <- 2*pnorm(-abs(Z2));
## run different version of ldd()
out.pp <- ldd(P1,P2,perm=100);
out.zp <- ldd(abs(Z1),P2,p1=FALSE,perm=100);
out.pz <- ldd(P1,abs(Z2),p2=FALSE,perm=100);
out.zz <- ldd(abs(Z1),abs(Z2),p1=FALSE,p2=FALSE,perm=100);
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.