sampleHMM() R function from [SNPknock]

Sample hidden Markov models

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


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

Arguments

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).

Returns

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

Details

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 $X_j$ .

Examples


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)

References

Rdpack::insert_ref(key="sesia2019",package="SNPknock")

sampleHMM function