Description Usage Arguments Value See Also Examples
The function is used to fit (bidirectional) Hidden Markov Models, given one or more observation sequence.
1 
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 EMalgorithm (default: 1e6). 
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 loglikelihood during EM learning and the fitted HMM model.
1 2 3  data(example)
hmm_ex = initHMM(observations, nStates=3, method="Gaussian")
hmm_fitted = fitHMM(observations, hmm_ex)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.