STAN (version 1.4.0)

bdHMM: Create a bdHMM object

Description

This function creates a bdHMM function.

Usage

bdHMM(initProb = numeric(), transMat = matrix(numeric(), ncol = 1, nrow = 1), emission, nStates = numeric(), status = character(), stateLabel = character(), transitionsOptim = character(), directedObs = integer())

Arguments

initProb

Initial state probabilities.

transMat

Transition probabilities

emission

Emission parameters as an HMMEmission object.

nStates

Number of states.

stateLabel

Indicates directinality of states. States can be forward (F1, F2, ..., Fn), reverse (R1, R2, ..., Rn) or undirectional (U1, U2, ..., Um). Number of F and R states must be equal and twin states are indicated by integers in id (e.g. F1 and R1 and twins).

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.

status

Status of the bdHMM. 'Initial' means that the model was not fitted yet. 'EM' means that the model was optimized using Expectation maximization.

See Also

HMMEmission

Examples

nStates = 5
stateLabel = 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)

bdHMM(initProb=initProb, transMat=transMat, emission=HMMEmission(type='Gaussian', parameters=list(mean=means, cov=Sigma), nStates=length(means)), nStates=nStates, status='initial', stateLabel=stateLabel, transitionsOptim='none', directedObs=as.integer(0))