fitHMM {STAN} | R Documentation |
The function is used to fit (bidirectional) Hidden Markov Models, given one or more observation sequence.
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)
obs |
The observations. A list of one or more entries containing the observation matrix ( |
hmm |
The initial Hidden Markov Model. This is a |
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 |
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 |
|
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. |
A list containing the trace of the log-likelihood during EM learning and the fitted HMM model.
data(example) hmm_ex = initHMM(observations, nStates=3, method="Gaussian") hmm_fitted = fitHMM(observations, hmm_ex)