Forward algorithm with homogeneous transition probability matrix
forward.Rd
Calculates the log-likelihood of a sequence of observations under a homogeneous hidden Markov model using the forward algorithm.
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
transition probability matrix of dimension c(N,N), or array of k transition probability matrices of dimension c(N,N,k), if
trackID
is provided- allprobs
matrix of state-dependent probabilities/ density values of dimension c(n, N)
- trackID
optional vector of length n containing IDs
If provided, the total log-likelihood will be the sum of each track's likelihood contribution. In this case,
Gamma
can be a matrix, leading to the same transition probabilities for each track, or an array of dimension c(N,N,k), with one (homogeneous) transition probability matrix for each track. Furthermore, instead of a single vectordelta
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.- 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
andallprobs
should be reported from the fitted model. Defaults toTRUE
, but only works ifad = TRUE
.
See also
Other forward algorithms:
forward_g()
,
forward_hsmm()
,
forward_ihsmm()
,
forward_p()
,
forward_phsmm()
Examples
## negative log likelihood function
nll = function(par, step) {
# parameter transformations for unconstrained optimisation
Gamma = tpm(par[1:2]) # multinomial logit link
delta = stationary(Gamma) # stationary HMM
mu = exp(par[3:4])
sigma = exp(par[5:6])
# 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(delta, Gamma, allprobs)
}
## fitting an HMM to the trex data
par = c(-2,-2, # initial tpm params (logit-scale)
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[1:1000])