Description Usage Arguments Details Value References See Also Examples

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

1 2 | ```
knockoffDMC(X, pInit, Q, groups = NULL, seed = 123, cluster = NULL,
display_progress = FALSE)
``` |

`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). |

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]*.

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

sesia2019SNPknock \insertRefsesia2019multiSNPknock

Other knockoffs: `knockoffGenotypes`

,
`knockoffHMM`

,
`knockoffHaplotypes`

1 2 3 4 5 6 7 8 9 10 11 | ```
# 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)
``` |

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