LaMa (version 2.0.3)

stateprobs_g: Calculate conditional local state probabilities for inhomogeneous HMMs

Description

Computes $$\Pr(S_t = j \mid X_1, ..., X_T)$$ for inhomogeneous HMMs

Usage

stateprobs_g(delta, Gamma, allprobs, trackID = NULL, mod = NULL)

Value

matrix of conditional state probabilities of dimension c(n,N)

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,n-1), as in a time series of length n, there are only n-1 transitions

If an array of dimension c(N,N,n) for a single track is provided, the first slice will be ignored.

If trackID is provided, Gamma needs to be an array of dimension c(N,N,n), where n is the number of rows in allprobs. Then for each track the first transition matrix will be ignored.

allprobs

matrix of state-dependent probabilities/ density values of dimension c(n, N)

trackID

optional vector of k track IDs, if multiple tracks need to be decoded separately

mod

optional model object containing initial distribution delta, transition probability matrix Gamma, matrix of state-dependent probabilities allprobs, and potentially a trackID variable

If you are using automatic differentiation either with RTMB::MakeADFun or qreml and include forward_g in your likelihood function, the objects needed for state decoding are automatically reported after model fitting. Hence, you can pass the model object obtained from running RTMB::report() or from qreml directly to this function.

See Also

Other decoding functions: stateprobs(), stateprobs_p(), viterbi(), viterbi_g(), viterbi_p()

Examples

Gamma = tpm_g(runif(99), matrix(c(-1,-1,1,-2), nrow = 2, byrow = TRUE))
delta = c(0.5, 0.5)
allprobs = matrix(runif(200), nrow = 100, ncol = 2)

probs = stateprobs_g(delta, Gamma, allprobs)