STAN (version 2.0.3)

initBdHMM: Initialization of bidirectional hidden Markov models

Description

Initialization of bidirectional hidden Markov models

Usage

initBdHMM(obs, nStates, method, directedObs = rep(0, ncol(obs[[1]])), sizeFactors = matrix(1, nrow = length(obs), ncol = ncol(obs[[1]])), sharedCov = FALSE)

Arguments

obs

The observations. A list of one or more entries containing the observation matrix (numeric) for the samples (e.g. chromosomes).

nStates

The number of states.

method

Emission distribution of the model. One out of c("NegativeBinomial", "PoissonLogNormal", "NegativeMultinomial", "ZINegativeBinomial", "Poisson", "Bernoulli", "Gaussian", "IndependentGaussian")

directedObs

Integer vector defining the directionality (or strand-specificity) of the data tracks. Undirected (non-strand-specific) data tracks (e.g. ChIP) are indicated indicated by '0'. Directed (strand-specific) data tracks are indicated by increasing pairs of integers. For instance c(0,0,0,1,1,2,2): The first three data tracks are undirected, followed by two pairs of directed measurements.

sizeFactors

Library size factors for Emissions PoissonLogNormal or NegativeBinomial as a length(obs) x ncol(obs[[1]]) matrix.

sharedCov

If TRUE, (co-)variance of (Independent)Gaussian is shared over states. Only applicable to 'Gaussian' or 'IndependentGaussian' emissions. Default: FALSE.

Value

A HMM object.

Examples

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