fitHMM: Fit a Hidden Markov Model

In STAN: The Genomic STate ANnotation Package

Description

The function is used to fit (bidirectional) Hidden Markov Models, given one or more observation sequence.

Usage

fitHMM(obs=list(), hmm, convergence=1e-6, maxIters=1000, dirFlags=list(), emissionProbs=list(), effectiveZero=0, verbose=FALSE, nCores=1, incrementalEM=FALSE, updateTransMat=TRUE, sizeFactors=matrix(1, nrow=length(obs), ncol=ncol(obs[[1]])), clustering = FALSE)

Arguments

obs	The observations. A list of one or more entries containing the observation matrix (numeric) for the samples (e.g. chromosomes).
hmm	The initial Hidden Markov Model. This is a HMM.
convergence	Convergence cutoff for EM-algorithm (default: 1e-6).
maxIters	Maximum number of iterations.
dirFlags	The flag sequence is needed when a bdHMM is fitted on undirected data (e.g.) ChIP only. It is a list of character vectors indication for each position its knwon directionality. U allows all states. F allows undirected states and states in forward direction. R allows undirected states and states in reverse direction.
emissionProbs	List of precalculated emission probabilities of emission function is of type 'null'.
effectiveZero	Transitions below this cutoff are analytically set to 0 to speed up comptuations.
verbose	logical for printing algorithm status or not.
nCores	Number of cores to use for computations.
incrementalEM	When TRUE, the incremental EM is used to fit the model, where parameters are updated after each iteration over a single observation sequence.
updateTransMat	Wether transitions should be updated during model learning, default: TRUE.
sizeFactors	Library size factors for Emissions PoissonLogNormal or NegativeBinomial as a length(obs) x ncol(obs[[1]]) matrix.
clustering	Boolean variable to specify wether it should be fit as an HMM or or bdClustering. Please, use function bdClust when bdClust is prefered.

Value

A list containing the trace of the log-likelihood during EM learning and the fitted HMM model.

See Also

HMM

Examples

data(example)
hmm_ex = initHMM(observations, nStates=3, method="Gaussian")
hmm_fitted = fitHMM(observations, hmm_ex)

STAN documentation built on Nov. 8, 2020, 11:11 p.m.