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Builds all transition probability matrices of a periodic-HSMM-approximating HMM

tpm_phsmm.Rd

Hidden semi-Markov models (HSMMs) are a flexible extension of HMMs. For direct numerical maximum likelihood estimation, HSMMs can be represented as HMMs on an enlarged state space (of size \(M\)) and with structured transition probabilities.

This function computes the transition matrices of a periodically inhomogeneous HSMM.

Usage

tpm_phsmm(omega, dm, eps = 1e-10)

Arguments

omega

embedded transition probability matrix

Either a matrix of dimension c(nStates, nStates) for homogeneous conditional transition probabilities (as computed by tpm_emb), or an array of dimension c(nStates, nStates, L) for inhomogeneous conditional transition probabilities (as computed by tpm_emb_g).

dm

state dwell-time distributions arranged in a list of length nStates

Each list element needs to be a matrix of dimension c(L, N_i), where each row t is the (approximate) probability mass function of state i at time t.

eps

rounding value: If an entry of the transition probability matrix is smaller, it is rounded to zero. Usually, this should not be changed.

Value

list of length L, containing sparse extended-state-space transition probability matrices of the approximating HMM for each time point of the cycle.

Examples

N = 2 # number of states
L = 24 # cycle length
# time-varying mean dwell times
Z = cosinor(1:L, period = L) # trigonometric basis functions design matrix
beta = matrix(c(2, 2, 0.1, -0.1, -0.2, 0.2), nrow = 2)
Lambda = exp(cbind(1, Z) %*% t(beta))
sizes = c(20, 20) # approximating chain with 40 states
# state dwell-time distributions
dm = lapply(1:N, function(i) sapply(1:sizes[i]-1, dpois, lambda = Lambda[,i]))

## homogeneous conditional transition probabilities
# diagonal elements are zero, rowsums are one
omega = matrix(c(0,1,1,0), nrow = N, byrow = TRUE)

# calculating extended-state-space t.p.m.s
Gamma = tpm_phsmm(omega, dm)

## inhomogeneous conditional transition probabilities
# omega can be an array
omega = array(omega, dim = c(N,N,L))

# calculating extended-state-space t.p.m.s
Gamma = tpm_phsmm(omega, dm)