Forward algorithm for hidden semi-Markov models with periodically inhomogeneous state durations and/ or conditional transition probabilities
forward_phsmm.Rd
Hidden semi-Markov models (HSMMs) are a flexible extension of HMMs, where the state duration distribution is explicitly modelled by a distribution on the positive integers. This function can be used to fit HSMMs where the state-duration distribution and/ or the conditional transition probabilities vary with covariates. For direct numerical maximum likelhood estimation, HSMMs can be represented as HMMs on an enlarged state space (of size \(M\)) and with structured transition probabilities.
This function can be used to fit HSMMs where the state-duration distribution and/ or the conditional transition probabilities vary periodically.
In the special case of periodic variation (as compared to arbitrary covariate influence), this version is to be preferred over forward_ihsmm
because it computes the correct periodically stationary distribution and no observations are lost for the approximation.
This function is designed to be used with automatic differentiation based on the R
package RTMB
. It will be very slow without it!
Usage
forward_phsmm(
dm,
omega,
allprobs,
tod,
trackID = NULL,
delta = NULL,
eps = 1e-10,
report = TRUE
)
Arguments
- dm
list of length N containing matrices (or vectors) of dwell-time probability mass functions (PMFs) for each state.
If the dwell-time PMFs are constant, the vectors are the PMF of the dwell-time distribution fixed in time. The vector lengths correspond to the approximating state aggregate sizes, hence there should be little probablity mass not covered by these.
If the dwell-time PMFs are inhomogeneous, the matrices need to have L rows, where L is the cycle length. The number of columns again correpond to the size of the approximating state aggregates.
- omega
matrix of dimension c(N,N) or array of dimension c(N,N,L) of conditional transition probabilites, also called embedded transition probability matrix
It contains the transition probabilities given the current state is left. Hence, the diagonal elements need to be zero and the rows need to sum to one. Such a matrix can be constructed using
tpm_emb
and an array usingtpm_emb_g
.- allprobs
matrix of state-dependent probabilities/ density values of dimension c(n, N)
- tod
(Integer valued) variable for cycle indexing in 1, ..., L, mapping the data index to a generalised time of day (length n). For half-hourly data L = 48. It could, however, also be day of year for daily data and L = 365.
- trackID
optional vector of length n containing IDs
If provided, the total log-likelihood will be the sum of each track's likelihood contribution. Instead of a single vector
delta
corresponding to the initial distribution, adelta
matrix of initial distributions, of dimension c(k,N), can be provided, such that each track starts with it's own initial distribution.- delta
Optional vector of initial state probabilities of length N. By default, instead of this, the stationary distribution is computed corresponding to the first approximating t.p.m. of each track is computed. Contrary to the homogeneous case, this is not theoretically motivated but just for convenience.
- eps
small value to avoid numerical issues in the approximating transition matrix construction. Usually, this should not be changed.
- report
logical, indicating whether initial distribution, approximating transition probability matrix and
allprobs
matrix should be reported from the fitted model. Defaults toTRUE
.
Details
Calculates the (approximate) log-likelihood of a sequence of observations under a periodically inhomogeneous hidden semi-Markov model using a modified forward algorithm.
See also
Other forward algorithms:
forward()
,
forward_g()
,
forward_hsmm()
,
forward_ihsmm()
,
forward_p()