# ldd: Latent dependency detection In ssa: Simultaneous Signal Analysis

## Description

Given two sequences of paired test statistics, tests whether the latent indicators of significance are positively dependent.

## Usage

 ```1 2``` ```ldd(T1, T2, m1 = 1000, m2 = 1000, perm = 0, p1 = TRUE, p2 = TRUE, jitter = NULL) ```

## Arguments

 `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 `perm` times; the permutation p-value will be calculated as a fraction of `perm`+1 `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 `runif(0,jitter)` will be added to all entries of T1 and T2

## Value

 `D` value of the test statistic `p.perm` permutation p-value `p.asymp` asymptotic approximate p-value

## Examples

 ``` 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); ```

ssa documentation built on May 1, 2019, 10:27 p.m.