initBdHMM: Initialization of bidirectional hidden Markov models

Description Usage Arguments Value Examples

View source: R/initBdHMM.R

Description

Initialization of bidirectional hidden Markov models

Usage

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initBdHMM(obs, dStates = 0, uStates = 0, method, dirFlags = NULL, 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).

dStates

The number of directed states.

uStates

The number of undirected states.

method

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

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.

directedObs

Integer vector defining the directionality (or strand-specificity) of the data tracks. Undirected (non-strand-specific) data tracks (e.g. ChIP) are 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

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data(example)
bdHMM_ex = initBdHMM(observations, dStates=3, method="Gaussian") 

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