forward_p: Forward algorithm with for periodically varying transition probability matrices

Description

Calculates the log-likelihood of a sequence of observations under a hidden Markov model with periodically varying transition probabilities using the forward algorithm.

Usage

forward_p(
  delta,
  Gamma,
  allprobs,
  tod,
  trackID = NULL,
  ad = NULL,
  report = TRUE
)

Value

log-likelihood for given data and parameters

Arguments

delta

initial or stationary distribution of length N, or matrix of dimension c(k,N) for k independent tracks, if trackID is provided

Gamma

array of transition probability matrices of dimension c(N,N,L).

Here we use the definition \(\Pr(S_t=j \mid S_{t-1}=i) = \gamma_{ij}^{(t)}\) such that the transition probabilities between time point \(t-1\) and \(t\) are an element of \(\Gamma^{(t)}\).

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, a delta matrix of initial distributions of dimension c(k,N), can be provided, such that each track starts with it's own initial distribution.

ad

optional logical, indicating whether automatic differentiation with RTMB should be used. By default, the function determines this itself.

report

logical, indicating whether delta, Gamma and allprobs should be reported from the fitted model. Defaults to TRUE, but only works if ad = TRUE.

Details

When the transition probability matrix only varies periodically (e.g. as a function of time of day), there are only \(L\) unique matrices if \(L\) is the period length (e.g. \(L=24\) for hourly data and time-of-day variation). Thus, it is much more efficient to only calculate these \(L\) matrices and index them by a time variable (e.g. time of day or day of year) instead of calculating such a matrix for each index in the data set (which would be redundant). This function allows for that by only expecting a transition probability matrix for each time point in a period and an integer valued (\(1, \dots, L\)) time variable that maps the data index to the according time.

Examples

Run this code

## negative log likelihood function
nll = function(par, step, tod) {
 # parameter transformations for unconstrained optimisation
 beta = matrix(par[1:6], nrow = 2)
 Gamma = tpm_p(1:24, beta = beta) # multinomial logit link for each time point
 delta = stationary_p(Gamma, tod[1]) # stationary HMM
 mu = exp(par[7:8])
 sigma = exp(par[9:10])
 # calculate all state-dependent probabilities
 allprobs = matrix(1, length(step), 2)
 ind = which(!is.na(step))
 for(j in 1:2) allprobs[ind,j] = dgamma2(step[ind], mu[j], sigma[j])
 # simple forward algorithm to calculate log-likelihood
 -forward_p(delta, Gamma, allprobs, tod)
}

## fitting an HMM to the nessi data
par = c(-2,-2,            # initial tpm intercepts (logit-scale)
        rep(0, 4),        # initial tpm slopes
        log(c(0.3, 2.5)), # initial means for step length (log-transformed)
        log(c(0.2, 1.5))) # initial sds for step length (log-transformed)
mod = nlm(nll, par, step = trex$step[1:500], tod = trex$tod[1:500])