sampleHMM: Sample hidden Markov models
In SNPknock: Knockoffs for Hidden Markov Models and Genetic Data

Description Usage Arguments Details Value References See Also Examples

This function draws independent random samples of an hidden Markov model.

1	sampleHMM(pInit, Q, pEmit, n = 1)

`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.
`pEmit`	an array of size (p,M,K), containing the emission probabilities for each of the M possible emission states, from each of the K hidden states and the p variables.
`n`	the number of independent samples to be drawn (default: 1).

Each element of the output matrix is an integer value between 0 and K-1. The transition matrices contained in Q are defined with the same convention as in sampleDMC. The emission propability matrices contained in pEmit are defined such that P[X_{j}=k|H_{j}=l]=\mathrm{pEmit}[j,k,l], where H_j is the latent variable associated to X_j.

A matrix of size n-by-p containing the n observed Markov chains of length p.

\insertRef

sesia2019SNPknock

Other models: sampleDMC

p=10; K=5; M=3;
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,,]) }
pEmit = array(stats::runif(p*M*K),c(p,M,K))
for(j in 1:p) { pEmit[j,,] = pEmit[j,,] / rowSums(pEmit[j,,]) }
X = sampleHMM(pInit, Q, pEmit, n=20)