knockoffDMC {SNPknock}

Group knockoffs of discrete Markov chains

Description

This function constructs knockoffs of variables distributed as a discrete Markov chain.

Usage

knockoffDMC(X, pInit, Q, groups = NULL, seed = 123, cluster = NULL,
  display_progress = FALSE)

Arguments

X

an integer matrix of size n-by-p containing the original variables.

pInit

an array of length K, containing the marginal distribution of the states for the first variable.

Q

an array of size (p-1,K,K), containing a list of p-1 transition matrices between the K states of the Markov chain.

groups

an array of length p, describing the group membership of each variable (default: NULL).

seed

an integer random seed (default: 123).

cluster

a computing cluster object created by makeCluster (default: NULL).

display_progress

whether to show progress bar (default: FALSE).

Details

Each element of the matrix X should be an integer value between 0 and K-1. The transition matrices contained in Q are defined such that P[X_{j+1}=k|X_{j}=l]=Q[j,l,k].

Value

An integer matrix of size n-by-p containing the knockoff variables.

References

Sesia M, Sabatti C, Candès EJ (2019). “Gene hunting with hidden Markov model knockoffs.” Biometrika, 106, 1–18. doi: 10.1093/biomet/asy033. Sesia M, Katsevich E, Bates S, Candès E, Sabatti C (2019). “Multi-resolution localization of causal variants across the genome.” bioRxiv. doi: 10.1101/631390.

See Also

Other knockoffs: knockoffGenotypes, knockoffHMM, knockoffHaplotypes

Examples

# Generate data
p = 10; K = 5;
pInit = rep(1/K,K)
Q = array(stats::runif((p-1)*K*K),c(p-1,K,K))
for(j in 1:(p-1)) { Q[j,,] = Q[j,,] / rowSums(Q[j,,]) }
X = sampleDMC(pInit, Q, n=20)
# Generate knockoffs
Xk = knockoffDMC(X, pInit, Q)
# Generate group-knockoffs for groups of size 3
groups = rep(seq(p), each=3, length.out=p)
Xk = knockoffDMC(X, pInit, Q, groups=groups)