STAN (version 2.0.3)

bdHMM-class: This class is a generic container for bidirectional Hidden Markov Models.

Description

This class is a generic container for bidirectional Hidden Markov Models.

Slots

initProb

Initial state probabilities.

transMat

Transition probabilities

emission

Emission parameters as an HMMEmission object.

nStates

Number of states.

status

of the HMM. On of c('initial', 'EM').

stateNames

State names.

dimNames

Names of data tracks.

LogLik

Log likelihood of a fitted HMM.

transitionsOptim

There are three methods to choose from for fitting the transitions. Bidirectional transition matrices (invariant under reversal of time and direction) can be fitted using c('rsolnp', 'ipopt'). 'None' uses standard update formulas and the resulting matrix is not constrained to be bidirectional.

directedObs

An integer indicating which dimensions are directed. Undirected dimensions are 0. Directed observations must be marked as unique integer pairs. For instance c(0,0,0,0,0,1,1,2,2,3,3) contains 5 undirected observations, and thre pairs (one for each direction) of directed observations.

dirScore

Directionlity score of states of a fitted bdHMM.

Methods

[

get elements from the bdHMM

See Also

HMMEmission

Examples

nStates = 5
stateNames = c('F1', 'F2', 'R1', 'R2', 'U1')
means = list(4,11,4,11,-1)
Sigma = lapply(list(4,4,4,4,4), as.matrix)
transMat = matrix(1/nStates, nrow=nStates, ncol=nStates)
initProb = rep(1/nStates, nStates)
myEmission = list(d1=HMMEmission(type='Gaussian', parameters=list(mu=means, cov=Sigma), nStates=length(means)))

bdhmm = bdHMM(initProb=initProb, transMat=transMat, emission=myEmission, nStates=nStates, status='initial', stateNames=stateNames, transitionsOptim='none', directedObs=as.integer(0))