STAN (version 2.0.3)

bdHMM: Create a bdHMM object

Description

This function creates a bdHMM function.

Usage

bdHMM(initProb = numeric(), transMat = matrix(numeric(), ncol = 0, nrow = 0), emission, nStates = numeric(), status = character(), stateNames = character(), dimNames = character(), transitionsOptim = "analytical", directedObs = integer(), dirScore = numeric())

Arguments

initProb

Initial state probabilities.

transMat

Transition probabilities

emission

Emission parameters as an HMMEmission object.

nStates

Number of states.

status

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

stateNames

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).

dimNames

Names of data tracks.

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', 'analytical'). '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.

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))